lots of per-plot configs
This commit is contained in:
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13999a477d
commit
04253b961f
36 changed files with 5121 additions and 540 deletions
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@ -13,15 +13,15 @@ lin_count_bp <- function( lf_data = lin_lf
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, display_label_col = "p_count"
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, bar_stat_stype = "identity"
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, x_lab_angle = 90
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, d_lab_size = 5
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, d_lab_size = 2.3
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, d_lab_hjust = 0.5
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, d_lab_vjust = 0.5
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, d_lab_col = "black"
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, my_xats = 20 # x axis text size
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, my_yats = 20 # y axis text size
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, my_xals = 22 # x axis label size
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, my_yals = 22 # y axis label size
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, my_lls = 22 # legend label size
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, my_xats = 8 # x axis text size
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, my_yats = 8 # y axis text size
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, my_xals = 10 # x axis label size
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, my_yals = 10 # y axis label size
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, my_lls = 10 # legend label size
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, bar_col_labels = c("Mutations", "Total Samples")
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, bar_col_values = c("grey50", "gray75")
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, bar_leg_name = ""
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@ -56,7 +56,8 @@ lin_count_bp <- function( lf_data = lin_lf
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, axis.title.y = element_text(size = my_yals
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, colour = "black")
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, legend.position = leg_location
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, legend.text = element_text(size = my_lls)) +
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, legend.text = element_text(size = my_lls)
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, legend.key.size = unit(my_lls, 'pt')) +
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geom_label(aes(label = eval(parse(text = display_label_col)))
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, size = d_lab_size
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@ -14,15 +14,15 @@ lin_count_bp_diversity <- function( lf_data = lin_wf
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, display_label_col = "snp_diversity_f"
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, bar_stat_stype = "identity"
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, x_lab_angle = 90
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, d_lab_size = 5
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, d_lab_size = 2.3
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, d_lab_hjust = 0.5
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, d_lab_vjust = 0.5
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, d_lab_col = "black"
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, my_xats = 20 # x axis text size
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, my_yats = 20 # y axis text size
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, my_xals = 22 # x axis label size
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, my_yals = 22 # y axis label size
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, my_lls = 22 # legend label size
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, my_xats = 8 # x axis text size
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, my_yats = 8 # y axis text size
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, my_xals = 10 # x axis label size
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, my_yals = 10 # y axis label size
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, my_lls = 10 # legend label size
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, bar_col_labels = "" #c("Mutations", "Total Samples")
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, bar_col_values = c("gray50", "gray75")
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, bar_leg_name = ""
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@ -64,6 +64,7 @@ lin_count_bp_diversity <- function( lf_data = lin_wf
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, colour = "black")
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, legend.position = leg_location
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, legend.text = element_text(size = my_lls)
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, legend.key.size = unit(my_lls, 'pt')
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, plot.title = element_text(size = my_lls
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, colour = title_colour
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, hjust = 0.5)
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@ -7,9 +7,6 @@
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# LigDist_colname #from globals: plotting_globals.R
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# ppi2Dist_colname #from globals: plotting_globals.R
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# naDist_colname #from globals: plotting_globals.R
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geneL_normal = c("pnca")
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geneL_na = c("gid", "rpob")
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geneL_ppi2 = c("alr", "embb", "katg", "rpob")
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corr_data_extract <- function(df
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, gene
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@ -75,11 +72,24 @@ corr_data_extract <- function(df
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if (tolower(gene)%in%geneL_na){
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colnames_to_extract = c(common_colnames,"mcsm_na_affinity", naDist_colname)
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display_colnames = c(display_common_colnames, "mCSM-NA", "NCA-Dist")
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display_colnames = c(display_common_colnames, "mCSM-NA", "NA-Dist")
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corr_df = df[,colnames_to_extract]
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colnames(corr_df) = display_colnames
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}
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# SPECIAL case for rpob as it exists in both ppi and na
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if (tolower(gene)%in%c("rpob")){
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colnames_to_extract = c(common_colnames
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, "mcsm_ppi2_affinity", ppi2Dist_colname
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, "mcsm_na_affinity", naDist_colname)
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display_colnames = c(display_common_colnames
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,"mCSM-PPI2", "PPI-Dist"
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,"mCSM-NA", "NA-Dist" )
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corr_df = df[,colnames_to_extract]
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colnames(corr_df) = display_colnames
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}
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# [optional] arg: extract_scaled_cols
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if (extract_scaled_cols){
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cat("\nExtracting scaled columns as well...\n")
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@ -734,7 +734,7 @@ dm_om_wf_lf_data <- function(df
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# mcsm-ppi2 affinity
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# data filtered by cut off
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#========================
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if (tolower(gene)%in%geneL_ppi2){
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if (tolower(gene)%in%geneL_ppi2 || tolower(gene)%in%geneL_both){
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#-----------------
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# mCSM-PPI2: WF and lF
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#-----------------
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@ -776,13 +776,11 @@ dm_om_wf_lf_data <- function(df
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}
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#====================
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# mcsm-NA affinity
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# data filtered by cut off
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#====================
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if (tolower(gene)%in%geneL_na){
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if (tolower(gene)%in%geneL_na|| tolower(gene)%in%geneL_both){
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#---------------
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# mCSM-NA: WF and lF
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#-----------------
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@ -20,11 +20,11 @@ lineage_distP <- function(plotdf
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, fill_categ = "mutation_info_labels"
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, fill_categ_cols = c("#E69F00", "#999999")
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, label_categories = c("LABEL1", "LABEL2")
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, my_ats = 15 # axis text size
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, my_als = 20 # axis label size
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, my_leg_ts = 16
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, my_leg_title = 16
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, my_strip_ts = 20
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, my_ats = 15 # 15 axis text size
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, my_als = 20 # 20 axis label size
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, my_leg_ts = 16 #16
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, my_leg_title = 16 #16
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, my_strip_ts = 20 #20
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, leg_pos = c(0.8, 0.9)
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, leg_pos_wf = c("top", "left", "bottom", "right")
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, leg_dir_wf = c("horizontal", "vertical")
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@ -57,6 +57,7 @@ lineage_distP <- function(plotdf
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, axis.title.y = element_blank()
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, strip.text = element_text(size = my_strip_ts)
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, legend.text = element_text(size = my_leg_ts)
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, legend.key.size = unit(my_leg_ts, 'pt')
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, legend.title = element_text(size = my_leg_title)
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, legend.position = c(0.8, 0.9)) +
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labs(x = x_lab
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@ -132,7 +132,8 @@ site_snp_count_bp <- function (plotdf
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scale_x_continuous(breaks = unique(snpsBYpos_df$snpsBYpos)) +
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geom_label(stat = "count", aes(label = ..count..)
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, color = "black"
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, size = geom_ls) +
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, size = geom_ls
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, position = position_dodge2(width = 1)) +
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theme(axis.text.x = element_text(size = axis_text_size
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, angle = 0)
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, axis.text.y = element_text(size = axis_text_size
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@ -49,9 +49,13 @@ if (!exists("infile_params") && exists("gene")){
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cat("\nInput file for mcsm comb data not specified, assuming filename: ", infile_params, "\n")
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}
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# Input 1: read <gene>_comb_afor.csv
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# Input 1:
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cat("\nReading mcsm combined data file: ", infile_params)
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mcsm_df = read.csv(infile_params, header = T)
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if (tolower(gene)%in%c("rpob")){
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mcsm_df = mcsm_df[mcsm_df$position!=1148,]
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}
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pd_df = plotting_data(mcsm_df
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, gene = gene # ADDED
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, lig_dist_colname = LigDist_colname
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164
scripts/plotting/plotting_thesis/alr/dm_om_plots_layout_alr.R
Normal file
164
scripts/plotting/plotting_thesis/alr/dm_om_plots_layout_alr.R
Normal file
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@ -0,0 +1,164 @@
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# source dm_om_plots.R
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#============
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# Plot labels
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#============
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tit1 = "Stability changes"
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tit2 = "Genomic measure"
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tit3 = "Distance to partners"
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tit4 = "Evolutionary Conservation"
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tit5 = "Affinity changes"
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pt_size = 30
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theme_georgia <- function(...) {
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theme_gray(base_family = "sans", ...) +
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theme(plot.title = element_text(face = "bold"))
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}
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title_theme <- calc_element("plot.title", theme_georgia())
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pt1 = ggdraw() +
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draw_label(
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tit1,
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fontfamily = title_theme$family,
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fontface = title_theme$face,
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#size = title_theme$size
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size = pt_size
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)
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pt2 = ggdraw() +
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draw_label(
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tit2,
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fontfamily = title_theme$family,
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fontface = title_theme$face,
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size = pt_size
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)
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pt3 = ggdraw() +
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draw_label(
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tit3,
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fontfamily = title_theme$family,
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fontface = title_theme$face,
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size = pt_size
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)
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pt4 = ggdraw() +
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draw_label(
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tit4,
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fontfamily = title_theme$family,
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fontface = title_theme$face,
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size = pt_size
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)
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pt5 = ggdraw() +
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draw_label(
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tit5,
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fontfamily = title_theme$family,
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fontface = title_theme$face,
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size = pt_size
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)
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#======================
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# Output plot function
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#======================
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OutPlot_dm_om = function(x){
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# dist b/w plot title and plot
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relH_tp = c(0.08, 0.92)
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my_label_size = 25
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#----------------
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# Top panel
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#----------------
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top_panel = cowplot::plot_grid(
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cowplot::plot_grid(pt1,
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cowplot::plot_grid(duetP, foldxP, deepddgP, dynamut2P
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, nrow = 1
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, labels = c("A", "B", "C", "D")
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, label_size = my_label_size)
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, ncol = 1
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, rel_heights = relH_tp
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),
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NULL,
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cowplot::plot_grid(pt2,
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cowplot::plot_grid(genomicsP
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, nrow = 1
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, labels = c("E")
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, label_size = my_label_size)
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, ncol = 1
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, rel_heights = relH_tp
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),
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NULL,
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cowplot::plot_grid(pt3,
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cowplot::plot_grid( #distanceP
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distanceP_lig
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, distanceP_ppi2
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#, distanceP_na
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, nrow = 1
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, labels = c("F", "G")
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, label_size = my_label_size)
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, ncol = 1
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, rel_heights = relH_tp
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),
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nrow = 1,
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rel_widths = c(2/7, 0.1/7, 0.5/7, 0.1/7, 1/7)
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)
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#----------------
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# Bottom panel
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#----------------
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bottom_panel = cowplot::plot_grid(
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cowplot::plot_grid(pt4,
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cowplot::plot_grid(consurfP, proveanP, snap2P
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, nrow = 1
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, labels = c("H", "I", "J")
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, label_size = my_label_size)
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, ncol = 1
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, rel_heights =relH_tp
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),NULL,
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cowplot::plot_grid(pt5,
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cowplot::plot_grid(mcsmligP, mcsmlig2P
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, mcsmppi2P
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#, mcsmnaP
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, nrow = 1
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, labels = c("K", "L", "M")
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, label_size = my_label_size)
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, ncol = 1
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, rel_heights = relH_tp
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),NULL,
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nrow = 1,
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rel_widths = c(3/6,0.1/6,3/6, 0.1/6 )
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)
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#-------------------------------
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# combine: Top and Bottom panel
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#-------------------------------
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cowplot::plot_grid (top_panel, bottom_panel
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, nrow =2
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, rel_widths = c(1, 1)
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, align = "hv")
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}
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#=====================
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# OutPlot: svg and png
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#======================
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dm_om_combinedP = paste0(outdir_images
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,tolower(gene)
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,"_dm_om_all.svg")
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cat("DM OM plots with stats:", dm_om_combinedP)
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svg(dm_om_combinedP, width = 32, height = 18)
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OutPlot_dm_om()
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dev.off()
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dm_om_combinedP_png = paste0(outdir_images
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,tolower(gene)
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,"_dm_om_all.png")
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cat("DM OM plots with stats:", dm_om_combinedP_png)
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png(dm_om_combinedP_png, width = 32, height = 18, units = "in", res = 300)
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OutPlot_dm_om()
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dev.off()
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@ -183,7 +183,6 @@ if (tolower(gene)%in%geneL_na){
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}
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#####################################################################
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# ------------------------------
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# bp site site count: mCSM-lig
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# < 10 Ang ligand
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@ -203,10 +202,10 @@ posC_lig = site_snp_count_bp(plotdf = df3_lig
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, axis_label_size = 10)
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posC_lig
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# ------------------------------
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#------------------------------
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# bp site site count: ppi2
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# < 10 Ang interface
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# ------------------------------
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#------------------------------
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if (tolower(gene)%in%geneL_ppi2){
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posC_ppi2 = site_snp_count_bp(plotdf = df3_ppi2
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@ -223,10 +222,10 @@ if (tolower(gene)%in%geneL_ppi2){
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posC_ppi2
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}
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# ------------------------------
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#------------------------------
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# bp site site count: NCA dist
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# < 10 Ang nca
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# ------------------------------
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#------------------------------
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if (tolower(gene)%in%geneL_na){
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posC_nca = site_snp_count_bp(plotdf = df3_na
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@ -242,15 +241,11 @@ if (tolower(gene)%in%geneL_na){
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, axis_label_size = 10)
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posC_nca
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}
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#===============================================================
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# ------------------------------
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#------------------------------
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# bp site site count: ALL
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# <10 Ang ligand
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# ------------------------------
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#------------------------------
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posC_all = site_snp_count_bp(plotdf = df3
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, df_colname = "position"
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, xaxis_title = "Number of nsSNPs"
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@ -282,34 +277,6 @@ consurfP = stability_count_bp(plotdf = df3
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consurfP
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####################
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# Sensitivity count: Mutations
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####################
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table(df3$sensitivity)
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rect_sens=data.frame(mutation_class=c("Resistant","Sensitive")
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, tile_colour =c("red","blue")
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, numbers = c(table(df3$sensitivity)[[1]], table(df3$sensitivity)[[2]]))
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sensP = ggplot(rect_sens, aes(mutation_class, y = 0
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, fill = tile_colour
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, label = paste0("n=", numbers)
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)) +
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geom_tile(width = 1, height = 1) + # make square tiles
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geom_label(color = "black", size = 1.7,fill = "white", alpha=0.7) + # add white text in the middle
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scale_fill_identity(guide = "none") + # color the tiles with the colors in the data frame
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coord_fixed() + # make sure tiles are square
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#coord_flip()+ scale_x_reverse() +
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# theme_void() # remove any axis markings
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theme_nothing() # remove any axis markings
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sensP
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# sensP2 = sensP +
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# coord_flip() + scale_x_reverse()
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# sensP2
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##############################################################
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#===================
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# Stability
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@ -421,5 +388,4 @@ snap2P = stability_count_bp(plotdf = df3
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, ltis = 11
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, geom_ls = 2.5)
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snap2P
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#####################################################################################
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357
scripts/plotting/plotting_thesis/basic_barplots_UPDATED.R
Normal file
357
scripts/plotting/plotting_thesis/basic_barplots_UPDATED.R
Normal file
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@ -0,0 +1,357 @@
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#!/usr/bin/env Rscript
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#########################################################
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# TASK: Barplots
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# basic barplots with outcome
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# basic barplots with frequency of count of mutations
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#########################################################
|
||||
#=============
|
||||
# Data: Input
|
||||
#==============
|
||||
#source("~/git/LSHTM_analysis/config/pnca.R")
|
||||
#source("~/git/LSHTM_analysis/config/embb.R")
|
||||
#source("~/git/LSHTM_analysis/config/gid.R")
|
||||
|
||||
#source("~/git/LSHTM_analysis/config/alr.R")
|
||||
#source("~/git/LSHTM_analysis/config/katg.R")
|
||||
source("~/git/LSHTM_analysis/config/rpob.R")
|
||||
|
||||
source("~/git/LSHTM_analysis/scripts/plotting/get_plotting_dfs.R")
|
||||
#source("~/git/LSHTM_analysis/scripts/plotting/plotting_colnames.R") sourced by above
|
||||
|
||||
cat("\nSourced plotting cols as well:", length(plotting_cols))
|
||||
|
||||
####################################################
|
||||
class(merged_df3)
|
||||
|
||||
df3 = subset(merged_df3, select = -c(pos_count))
|
||||
|
||||
#=======
|
||||
# output
|
||||
#=======
|
||||
outdir_images = paste0("~/git/Writing/thesis/images/results/", tolower(gene), "/")
|
||||
cat("plots will output to:", outdir_images)
|
||||
|
||||
###########################################################
|
||||
#------------------------------
|
||||
# plot default sizes
|
||||
#------------------------------
|
||||
#=========================
|
||||
# Affinity outcome
|
||||
# check this var: outcome_cols_affinity
|
||||
# get from preformatting or put in globals
|
||||
#==========================
|
||||
DistCutOff
|
||||
LigDist_colname # = "ligand_distance" # from globals
|
||||
ppi2Dist_colname
|
||||
naDist_colname
|
||||
|
||||
###########################################################
|
||||
# get plotting data within the distance
|
||||
df3_lig = df3[df3[[LigDist_colname]]<DistCutOff,]
|
||||
df3_ppi2 = df3[df3[[ppi2Dist_colname]]<DistCutOff,]
|
||||
df3_na = df3[df3[[naDist_colname]]<DistCutOff,]
|
||||
common_bp_title = paste0("Sites <", DistCutOff, angstroms_symbol)
|
||||
|
||||
#------------------------------
|
||||
# barplot for ligand affinity:
|
||||
# <10 Ang of ligand
|
||||
#------------------------------
|
||||
mLigP = stability_count_bp(plotdf = df3_lig
|
||||
, df_colname = "ligand_outcome"
|
||||
#, leg_title = "mCSM-lig"
|
||||
#, bp_plot_title = paste(common_bp_title, "ligand")
|
||||
, yaxis_title = "Number of nsSNPs"
|
||||
, leg_position = "none"
|
||||
, subtitle_text = "mCSM-lig"
|
||||
, bar_fill_values = c("#F8766D", "#00BFC4")
|
||||
, subtitle_colour= "black"
|
||||
, sts = 10
|
||||
, lts = 8
|
||||
, ats = 12
|
||||
, als = 11
|
||||
, ltis = 11
|
||||
, geom_ls = 2.5)
|
||||
mLigP
|
||||
#------------------------------
|
||||
# barplot for ligand affinity:
|
||||
# <10 Ang of ligand
|
||||
# mmCSM-lig: will be the same no. of sites but the effect will be different
|
||||
#------------------------------
|
||||
mmLigP = stability_count_bp(plotdf = df3_lig
|
||||
, df_colname = "mmcsm_lig_outcome"
|
||||
#, leg_title = "mmCSM-lig"
|
||||
#, label_categories = labels_mmlig
|
||||
#, bp_plot_title = paste(common_bp_title, "ligand")
|
||||
|
||||
, yaxis_title = ""
|
||||
, leg_position = "none"
|
||||
, subtitle_text = "mmCSM-lig"
|
||||
, bar_fill_values = c("#F8766D", "#00BFC4")
|
||||
, subtitle_colour= "black"
|
||||
, sts = 10
|
||||
, lts = 8
|
||||
, ats = 12
|
||||
, als = 11
|
||||
, ltis = 11
|
||||
, geom_ls = 2.5
|
||||
)
|
||||
mmLigP
|
||||
#------------------------------
|
||||
# barplot for ppi2 affinity
|
||||
# <10 Ang of interface
|
||||
#------------------------------
|
||||
if (tolower(gene)%in%geneL_ppi2){
|
||||
ppi2P = stability_count_bp(plotdf = df3_ppi2
|
||||
, df_colname = "mcsm_ppi2_outcome"
|
||||
#, leg_title = "mCSM-ppi2"
|
||||
#, label_categories = labels_ppi2
|
||||
#, bp_plot_title = paste(common_bp_title, "PP-interface")
|
||||
|
||||
, yaxis_title = "Number of nsSNPs"
|
||||
, leg_position = "none"
|
||||
, subtitle_text = "mCSM-ppi2"
|
||||
, bar_fill_values = c("#F8766D", "#00BFC4")
|
||||
, subtitle_colour= "black"
|
||||
, sts = 10
|
||||
, lts = 8
|
||||
, ats = 12
|
||||
, als = 11
|
||||
, ltis = 11
|
||||
, geom_ls = 2.5
|
||||
)
|
||||
ppi2P
|
||||
}
|
||||
#----------------------------
|
||||
# barplot for ppi2 affinity
|
||||
# <10 Ang of interface
|
||||
#------------------------------
|
||||
if (tolower(gene)%in%geneL_na){
|
||||
|
||||
nca_distP = stability_count_bp(plotdf = df3_na
|
||||
, df_colname = "mcsm_na_outcome"
|
||||
#, leg_title = "mCSM-NA"
|
||||
#, label_categories =
|
||||
#, bp_plot_title = paste(common_bp_title, "Dist to NA")
|
||||
|
||||
, yaxis_title = "Number of nsSNPs"
|
||||
, leg_position = "none"
|
||||
, subtitle_text = "mCSM-NA"
|
||||
, bar_fill_values = c("#F8766D", "#00BFC4")
|
||||
, subtitle_colour= "black"
|
||||
, sts = 10
|
||||
, lts = 8
|
||||
, ats = 12
|
||||
, als = 11
|
||||
, ltis = 11
|
||||
, geom_ls = 2.5
|
||||
)
|
||||
nca_distP
|
||||
}
|
||||
|
||||
#####################################################################
|
||||
# ------------------------------
|
||||
# bp site site count: mCSM-lig
|
||||
# < 10 Ang ligand
|
||||
# ------------------------------
|
||||
common_bp_title = paste0("Sites <", DistCutOff, angstroms_symbol)
|
||||
|
||||
posC_lig = site_snp_count_bp(plotdf = df3_lig
|
||||
, df_colname = "position"
|
||||
, xaxis_title = "Number of nsSNPs"
|
||||
, yaxis_title = "Number of Sites"
|
||||
, subtitle_colour = "chocolate4"
|
||||
, subtitle_text = ""
|
||||
, subtitle_size = 8
|
||||
, geom_ls = 2.6
|
||||
, leg_text_size = 10
|
||||
, axis_text_size = 10
|
||||
, axis_label_size = 10)
|
||||
|
||||
posC_lig
|
||||
#------------------------------
|
||||
# bp site site count: ppi2
|
||||
# < 10 Ang interface
|
||||
#------------------------------
|
||||
if (tolower(gene)%in%geneL_ppi2){
|
||||
|
||||
posC_ppi2 = site_snp_count_bp(plotdf = df3_ppi2
|
||||
, df_colname = "position"
|
||||
, xaxis_title = "Number of nsSNPs"
|
||||
, yaxis_title = "Number of Sites"
|
||||
, subtitle_colour = "chocolate4"
|
||||
, subtitle_text = ""
|
||||
, subtitle_size = 8
|
||||
, geom_ls = 2.6
|
||||
, leg_text_size = 10
|
||||
, axis_text_size = 10
|
||||
, axis_label_size = 10)
|
||||
posC_ppi2
|
||||
}
|
||||
|
||||
#------------------------------
|
||||
# bp site site count: NCA dist
|
||||
# < 10 Ang nca
|
||||
#------------------------------
|
||||
if (tolower(gene)%in%geneL_na){
|
||||
|
||||
posC_nca = site_snp_count_bp(plotdf = df3_na
|
||||
, df_colname = "position"
|
||||
, xaxis_title = "Number of nsSNPs"
|
||||
, yaxis_title = "Number of Sites"
|
||||
, subtitle_colour = "chocolate4"
|
||||
, subtitle_text = ""
|
||||
, subtitle_size = 8
|
||||
, geom_ls = 2.6
|
||||
, leg_text_size = 10
|
||||
, axis_text_size = 10
|
||||
, axis_label_size = 10)
|
||||
posC_nca
|
||||
}
|
||||
#===============================================================
|
||||
#------------------------------
|
||||
# bp site site count: ALL
|
||||
# <10 Ang ligand
|
||||
#------------------------------
|
||||
posC_all = site_snp_count_bp(plotdf = df3
|
||||
, df_colname = "position"
|
||||
, xaxis_title = "Number of nsSNPs"
|
||||
, yaxis_title = "Number of Sites"
|
||||
, subtitle_colour = "chocolate4"
|
||||
, subtitle_text = "All mutations sites"
|
||||
, subtitle_size = 8
|
||||
, geom_ls = 2.6
|
||||
, leg_text_size = 10
|
||||
, axis_text_size = 10
|
||||
, axis_label_size = 10)
|
||||
posC_all
|
||||
##################################################################
|
||||
consurfP = stability_count_bp(plotdf = df3
|
||||
, df_colname = "consurf_outcome"
|
||||
#, leg_title = "ConSurf"
|
||||
#, label_categories = labels_consurf
|
||||
, yaxis_title = "Number of nsSNPs"
|
||||
, leg_position = "top"
|
||||
, subtitle_text = "ConSurf"
|
||||
, bar_fill_values = consurf_colours # from globals
|
||||
, subtitle_colour= "black"
|
||||
, sts = 10
|
||||
, lts = 8
|
||||
, ats = 8
|
||||
, als = 8
|
||||
, ltis = 11
|
||||
, geom_ls = 2)
|
||||
|
||||
consurfP
|
||||
|
||||
##############################################################
|
||||
#===================
|
||||
# Stability
|
||||
#===================
|
||||
# duetP
|
||||
duetP = stability_count_bp(plotdf = df3
|
||||
, df_colname = "duet_outcome"
|
||||
, leg_title = "mCSM-DUET"
|
||||
#, label_categories = labels_duet
|
||||
, yaxis_title = "Number of nsSNPs"
|
||||
, leg_position = "none"
|
||||
, subtitle_text = "mCSM-DUET"
|
||||
, bar_fill_values = c("#F8766D", "#00BFC4")
|
||||
, subtitle_colour= "black"
|
||||
, sts = 10
|
||||
, lts = 8
|
||||
, ats = 12
|
||||
, als = 11
|
||||
, ltis = 11
|
||||
, geom_ls = 2.5
|
||||
)
|
||||
duetP
|
||||
|
||||
# foldx
|
||||
foldxP = stability_count_bp(plotdf = df3
|
||||
, df_colname = "foldx_outcome"
|
||||
#, leg_title = "FoldX"
|
||||
#, label_categories = labels_foldx
|
||||
, yaxis_title = ""
|
||||
, leg_position = "none"
|
||||
, subtitle_text = "FoldX"
|
||||
, bar_fill_values = c("#F8766D", "#00BFC4")
|
||||
, sts = 10
|
||||
, lts = 8
|
||||
, ats = 12
|
||||
, als = 11
|
||||
, ltis = 11
|
||||
, geom_ls = 2.5
|
||||
)
|
||||
foldxP
|
||||
|
||||
# deepddg
|
||||
deepddgP = stability_count_bp(plotdf = df3
|
||||
, df_colname = "deepddg_outcome"
|
||||
#, leg_title = "DeepDDG"
|
||||
#, label_categories = labels_deepddg
|
||||
, yaxis_title = ""
|
||||
, leg_position = "none"
|
||||
, subtitle_text = "DeepDDG"
|
||||
, bar_fill_values = c("#F8766D", "#00BFC4")
|
||||
, sts = 10
|
||||
, lts = 8
|
||||
, ats = 12
|
||||
, als = 11
|
||||
, ltis = 11
|
||||
, geom_ls = 2.5
|
||||
)
|
||||
deepddgP
|
||||
|
||||
# deepddg
|
||||
dynamut2P = stability_count_bp(plotdf = df3
|
||||
, df_colname = "ddg_dynamut2_outcome"
|
||||
#, leg_title = "Dynamut2"
|
||||
#, label_categories = labels_ddg_dynamut2_outcome
|
||||
, yaxis_title = ""
|
||||
, leg_position = "none"
|
||||
, subtitle_text = "Dynamut2"
|
||||
, bar_fill_values = c("#F8766D", "#00BFC4")
|
||||
, sts = 10
|
||||
, lts = 8
|
||||
, ats = 12
|
||||
, als = 11
|
||||
, ltis = 11
|
||||
, geom_ls = 2.5
|
||||
)
|
||||
dynamut2P
|
||||
|
||||
# provean
|
||||
proveanP = stability_count_bp(plotdf = df3
|
||||
, df_colname = "provean_outcome"
|
||||
#, leg_title = "PROVEAN"
|
||||
#, label_categories = labels_provean
|
||||
, yaxis_title = "Number of nsSNPs"
|
||||
, leg_position = "none" # top
|
||||
, subtitle_text = "PROVEAN"
|
||||
, bar_fill_values = c("#D01C8B", "#F1B6DA") # light pink and deep
|
||||
, sts = 10
|
||||
, lts = 8
|
||||
, ats = 12
|
||||
, als = 11
|
||||
, ltis = 11
|
||||
, geom_ls = 2.5
|
||||
)
|
||||
proveanP
|
||||
|
||||
# snap2
|
||||
snap2P = stability_count_bp(plotdf = df3
|
||||
, df_colname = "snap2_outcome"
|
||||
#, leg_title = "SNAP2"
|
||||
#, label_categories = labels_snap2
|
||||
, yaxis_title = ""
|
||||
, leg_position = "none" # top
|
||||
, subtitle_text = "SNAP2"
|
||||
, bar_fill_values = c("#D01C8B", "#F1B6DA") # light pink and deep
|
||||
, sts = 10
|
||||
, lts = 8
|
||||
, ats = 12
|
||||
, als = 11
|
||||
, ltis = 11
|
||||
, geom_ls = 2.5)
|
||||
snap2P
|
||||
#####################################################################################
|
||||
|
|
@ -36,11 +36,11 @@ genomicsP = lf_bp2(genomics_dataP
|
|||
|
||||
genomicsP
|
||||
|
||||
#check
|
||||
wilcox.test(wf_dist_genP$`Log10(MAF)`[wf_dist_genP$mutation_info_labels=="R"]
|
||||
, wf_dist_genP$`Log10(MAF)`[wf_dist_genP$mutation_info_labels=="S"], paired = FALSE)
|
||||
|
||||
tapply(wf_dist_genP$`Log10(MAF)`, wf_dist_genP$mutation_info_labels, summary)
|
||||
# #check
|
||||
# wilcox.test(wf_dist_genP$`Log10(MAF)`[wf_dist_genP$mutation_info_labels=="R"]
|
||||
# , wf_dist_genP$`Log10(MAF)`[wf_dist_genP$mutation_info_labels=="S"], paired = FALSE)
|
||||
#
|
||||
# tapply(wf_dist_genP$`Log10(MAF)`, wf_dist_genP$mutation_info_labels, summary)
|
||||
|
||||
#-------------------
|
||||
# Distance data plot:
|
||||
|
|
@ -61,16 +61,16 @@ distanceP = lf_bp2(dist_dataP
|
|||
|
||||
distanceP
|
||||
|
||||
# check
|
||||
wilcox.test(wf_dist_genP$`PPI Dist(Å)`[wf_dist_genP$mutation_info_labels=="R"]
|
||||
, wf_dist_genP$`PPI Dist(Å)`[wf_dist_genP$mutation_info_labels=="S"], paired = FALSE)
|
||||
|
||||
wilcox.test(wf_dist_genP$`Lig Dist(Å)`[wf_dist_genP$mutation_info_labels=="R"]
|
||||
, wf_dist_genP$`Lig Dist(Å)`[wf_dist_genP$mutation_info_labels=="S"], paired = FALSE)
|
||||
|
||||
tapply(wf_dist_genP$`PPI Dist(Å)`, wf_dist_genP$mutation_info_labels, summary)
|
||||
|
||||
tapply(wf_dist_genP$`Lig Dist(Å)`, wf_dist_genP$mutation_info_labels, summary)
|
||||
# # check
|
||||
# wilcox.test(wf_dist_genP$`PPI Dist(Å)`[wf_dist_genP$mutation_info_labels=="R"]
|
||||
# , wf_dist_genP$`PPI Dist(Å)`[wf_dist_genP$mutation_info_labels=="S"], paired = FALSE)
|
||||
#
|
||||
# wilcox.test(wf_dist_genP$`Lig Dist(Å)`[wf_dist_genP$mutation_info_labels=="R"]
|
||||
# , wf_dist_genP$`Lig Dist(Å)`[wf_dist_genP$mutation_info_labels=="S"], paired = FALSE)
|
||||
#
|
||||
# tapply(wf_dist_genP$`PPI Dist(Å)`, wf_dist_genP$mutation_info_labels, summary)
|
||||
#
|
||||
# tapply(wf_dist_genP$`Lig Dist(Å)`, wf_dist_genP$mutation_info_labels, summary)
|
||||
|
||||
|
||||
#-------------------
|
||||
|
|
@ -100,7 +100,9 @@ if (tolower(gene)%in%geneL_ppi2){
|
|||
levels(dist_data_ppi2$param_type)
|
||||
distanceP_ppi2 = lf_bp2(dist_data_ppi2
|
||||
#, p_title = ""
|
||||
, violin_quantiles = c(0.5), monochrome = F)
|
||||
, violin_quantiles = c(0.5)
|
||||
, dot_transparency = 0.2
|
||||
, monochrome = F)
|
||||
|
||||
distanceP_ppi2
|
||||
}
|
||||
|
|
@ -109,9 +111,9 @@ if (tolower(gene)%in%geneL_na){
|
|||
#-------------------
|
||||
# Distance data plot: NADist
|
||||
#--------------------
|
||||
levels(dist_dataP$param_type)[[3]]
|
||||
levels(dist_dataP$param_type)[[2]]
|
||||
#Lig Dist(Å), PPI Dist(Å)
|
||||
dist_data_na = dist_dataP[dist_dataP$param_type%in%c(levels(dist_dataP$param_type)[[3]]),]
|
||||
dist_data_na = dist_dataP[dist_dataP$param_type%in%c(levels(dist_dataP$param_type)[[2]]),]
|
||||
dist_data_na$param_type = factor(dist_data_na$param_type)
|
||||
table(dist_data_na$param_type)
|
||||
levels(dist_data_na$param_type)
|
||||
|
|
@ -233,9 +235,9 @@ table(lf_mcsm_ligP$param_type)
|
|||
mcsmligP = lf_bp2(lf_mcsm_ligP
|
||||
#, p_title = ""
|
||||
, violin_quantiles = c(0.5), monochrome = F
|
||||
, dot_transparency = 1)
|
||||
|
||||
, dot_transparency = 0.8)
|
||||
|
||||
mcsmligP
|
||||
#=================
|
||||
# Plot: mmCSM-lig2
|
||||
#=================
|
||||
|
|
@ -248,7 +250,7 @@ table(lf_mmcsm_lig2P$param_type)
|
|||
mcsmlig2P = lf_bp2(lf_mmcsm_lig2P
|
||||
#, p_title = ""
|
||||
, violin_quantiles = c(0.5), monochrome = F
|
||||
, dot_transparency = 1)
|
||||
, dot_transparency = 0.8)
|
||||
|
||||
mcsmlig2P
|
||||
|
||||
|
|
@ -265,7 +267,7 @@ if (tolower(gene)%in%geneL_ppi2){
|
|||
mcsmppi2P = lf_bp2(lf_mcsm_ppi2P
|
||||
#, p_title = ""
|
||||
, violin_quantiles = c(0.5), monochrome = F
|
||||
, dot_transparency = 1)
|
||||
, dot_transparency = 0.3)
|
||||
|
||||
}
|
||||
|
||||
|
|
@ -282,7 +284,7 @@ if (tolower(gene)%in%geneL_na){
|
|||
mcsmnaP = lf_bp2(lf_mcsm_naP
|
||||
#, p_title = ""
|
||||
, violin_quantiles = c(0.5), monochrome = F
|
||||
, dot_transparency = 1)
|
||||
, dot_transparency = 0.4)
|
||||
|
||||
}
|
||||
|
||||
|
|
|
|||
271
scripts/plotting/plotting_thesis/gid/basic_barplots_layout_gid.R
Normal file
271
scripts/plotting/plotting_thesis/gid/basic_barplots_layout_gid.R
Normal file
|
|
@ -0,0 +1,271 @@
|
|||
duetP
|
||||
foldxP
|
||||
deepddgP
|
||||
dynamut2P
|
||||
proveanP
|
||||
snap2P
|
||||
|
||||
mLigP
|
||||
mmLigP
|
||||
posC_lig
|
||||
ppi2P
|
||||
posC_nca
|
||||
peP
|
||||
sensP
|
||||
#========================
|
||||
# Common title settings
|
||||
#=========================
|
||||
theme_georgia <- function(...) {
|
||||
theme_gray(base_family = "sans", ...) +
|
||||
theme(plot.title = element_text(face = "bold"))
|
||||
}
|
||||
title_theme <- calc_element("plot.title", theme_georgia())
|
||||
|
||||
###############################################################
|
||||
common_bp_title = paste0("Sites <", DistCutOff, angstroms_symbol)
|
||||
|
||||
# extract common legends
|
||||
# lig affinity
|
||||
common_legend_outcome = get_legend(mLigP +
|
||||
guides(color = guide_legend(nrow = 1)) +
|
||||
theme(legend.position = "top"))
|
||||
|
||||
# stability
|
||||
common_legend_outcome = get_legend(duetP +
|
||||
guides(color = guide_legend(nrow = 1)) +
|
||||
theme(legend.position = "top"))
|
||||
# conservation
|
||||
cons_common_legend_outcome = get_legend(snap2P +
|
||||
guides(color = guide_legend(nrow = 1)) +
|
||||
theme(legend.position = "top"))
|
||||
###################################################################
|
||||
#==================================
|
||||
# Stability+Conservation: COMBINE
|
||||
#==================================
|
||||
tt_size = 10
|
||||
#----------------------------
|
||||
# stability and consv title
|
||||
#----------------------------
|
||||
tt_stab = ggdraw() +
|
||||
draw_label(
|
||||
paste0("Stability outcome"),
|
||||
fontfamily = title_theme$family,
|
||||
fontface = title_theme$face,
|
||||
#size = title_theme$size
|
||||
size = tt_size
|
||||
)
|
||||
|
||||
tt_cons = ggdraw() +
|
||||
draw_label(
|
||||
paste0("Conservation outcome"),
|
||||
fontfamily = title_theme$family,
|
||||
fontface = title_theme$face,
|
||||
size = tt_size
|
||||
)
|
||||
|
||||
#----------------------
|
||||
# Output plot
|
||||
#-----------------------
|
||||
stab_cons_CLP = paste0(outdir_images
|
||||
,tolower(gene)
|
||||
,"_stab_cons_BP_CLP.png")
|
||||
|
||||
print(paste0("plot filename:", stab_cons_CLP))
|
||||
png(stab_cons_CLP, units = "in", width = 10, height = 5, res = 300 )
|
||||
|
||||
cowplot::plot_grid(
|
||||
cowplot::plot_grid(
|
||||
cowplot::plot_grid(
|
||||
tt_stab,
|
||||
common_legend_outcome,
|
||||
nrow = 2
|
||||
),
|
||||
cowplot::plot_grid(
|
||||
duetP,
|
||||
foldxP,
|
||||
deepddgP,
|
||||
dynamut2P,
|
||||
nrow = 1,
|
||||
labels = c("A", "B", "C", "D"),
|
||||
label_size = 12),
|
||||
nrow = 2,
|
||||
rel_heights=c(1,10)
|
||||
),
|
||||
NULL,
|
||||
cowplot::plot_grid(
|
||||
cowplot::plot_grid(
|
||||
cowplot::plot_grid(
|
||||
tt_cons,
|
||||
cons_common_legend_outcome,
|
||||
nrow = 2
|
||||
),
|
||||
cowplot::plot_grid(
|
||||
proveanP,
|
||||
snap2P,
|
||||
nrow=1,
|
||||
labels = c("E", "F"),
|
||||
align = "hv"),
|
||||
nrow = 2,
|
||||
rel_heights = c(1, 10),
|
||||
label_size = 12),
|
||||
nrow=1
|
||||
),
|
||||
rel_widths = c(2,0.15,1),
|
||||
nrow=1
|
||||
)
|
||||
|
||||
dev.off()
|
||||
|
||||
#################################################################
|
||||
#=======================================
|
||||
# Affinity barplots: COMBINE ALL three
|
||||
#========================================
|
||||
|
||||
ligT = paste0(common_bp_title, " ligand")
|
||||
lig_affT = ggdraw() +
|
||||
draw_label(
|
||||
ligT,
|
||||
fontfamily = title_theme$family,
|
||||
fontface = title_theme$face,
|
||||
#size = title_theme$size
|
||||
size = 8
|
||||
)
|
||||
|
||||
p1 = cowplot::plot_grid(cowplot::plot_grid(lig_affT
|
||||
, common_legend_outcome
|
||||
, nrow=2),
|
||||
cowplot::plot_grid(mLigP, mmLigP, posC_lig
|
||||
, nrow = 1
|
||||
, rel_widths = c(1,1,1.8)
|
||||
, align = "h"),
|
||||
nrow = 2,
|
||||
rel_heights = c(1,8)
|
||||
|
||||
)
|
||||
p1
|
||||
###########################################################
|
||||
ncaT = paste0(common_bp_title, " Dist-NA")
|
||||
nca_affT = ggdraw() +
|
||||
draw_label(
|
||||
ncaT,
|
||||
fontfamily = title_theme$family,
|
||||
fontface = title_theme$face,
|
||||
#size = title_theme$size
|
||||
size = 8
|
||||
)
|
||||
|
||||
p2 = cowplot::plot_grid(cowplot::plot_grid(nca_affT
|
||||
, common_legend_outcome
|
||||
, nrow=2),
|
||||
cowplot::plot_grid(nca_distP, posC_nca
|
||||
, nrow = 1
|
||||
, rel_widths = c(1.2,1.8)
|
||||
, align = "h"),
|
||||
nrow = 2,
|
||||
rel_heights = c(1,8)
|
||||
)
|
||||
p2
|
||||
###########################################################
|
||||
# PE + All position count
|
||||
peT_allT = ggdraw() +
|
||||
draw_label(
|
||||
paste0("All mutation sites"),
|
||||
fontfamily = title_theme$family,
|
||||
fontface = title_theme$face,
|
||||
#size = title_theme$size
|
||||
size = 8
|
||||
)
|
||||
|
||||
p3 = cowplot::plot_grid(cowplot::plot_grid(peT_allT, nrow = 2
|
||||
, rel_widths = c(1,3),axis = "lr"),
|
||||
cowplot::plot_grid(
|
||||
peP2, posC_all,
|
||||
nrow = 2,
|
||||
rel_widths = c(1,1),
|
||||
align = "v",
|
||||
axis = "lr",
|
||||
rel_heights = c(1,8)
|
||||
),
|
||||
rel_heights = c(1,18),
|
||||
nrow = 2,axis = "lr")
|
||||
p3
|
||||
#===============
|
||||
# Final combine
|
||||
#===============
|
||||
w = 11.79
|
||||
h = 3.5
|
||||
mut_impact_CLP = paste0(outdir_images
|
||||
,tolower(gene)
|
||||
,"_mut_impactCLP.png")
|
||||
|
||||
#svg(affP, width = 20, height = 5.5)
|
||||
print(paste0("plot filename:", mut_impact_CLP))
|
||||
png(mut_impact_CLP, units = "in", width = w, height = h, res = 300 )
|
||||
|
||||
cowplot::plot_grid(p1, p2
|
||||
#, p3
|
||||
, nrow = 1
|
||||
, labels = "AUTO"
|
||||
, label_size = 12
|
||||
, rel_widths = c(3,2,2)
|
||||
#, rel_heights = c(1)
|
||||
)
|
||||
|
||||
dev.off()
|
||||
##################################################
|
||||
sensP
|
||||
consurfP
|
||||
#=================
|
||||
# Combine sensitivity + ConSurf
|
||||
# or ConSurf
|
||||
#=================
|
||||
w = 3
|
||||
h = 3
|
||||
# sens_conP = paste0(outdir_images
|
||||
# ,tolower(gene)
|
||||
# ,"_sens_cons_CLP.png")
|
||||
#
|
||||
# print(paste0("plot filename:", sens_conP))
|
||||
# png(sens_conP, units = "in", width = w, height = h, res = 300 )
|
||||
#
|
||||
# cowplot::plot_grid(sensP, consurfP,
|
||||
# nrow = 2,
|
||||
# rel_heights = c(1, 1.5)
|
||||
# )
|
||||
#
|
||||
# dev.off()
|
||||
|
||||
conCLP = paste0(outdir_images
|
||||
,tolower(gene)
|
||||
,"_consurf_BP.png")
|
||||
|
||||
print(paste0("plot filename:", conCLP))
|
||||
png(conCLP, units = "in", width = w, height = h, res = 300 )
|
||||
consurfP
|
||||
|
||||
dev.off()
|
||||
#================================
|
||||
# Sensitivity mutation numbers: geom_tile
|
||||
#================================
|
||||
sensCLP = paste0(outdir_images
|
||||
,tolower(gene)
|
||||
,"_sensN_tile.png")
|
||||
|
||||
print(paste0("plot filename:", sensCLP))
|
||||
png(sensCLP, units = "in", width = 1, height = 1, res = 300 )
|
||||
sensP
|
||||
dev.off()
|
||||
#================================
|
||||
# Sensitivity SITE numbers: geom_tile
|
||||
#================================
|
||||
sens_siteCLP = paste0(outdir_images
|
||||
,tolower(gene)
|
||||
,"_sens_siteC_tile.png")
|
||||
|
||||
print(paste0("plot filename:", sens_siteCLP))
|
||||
png(sens_siteCLP, units = "in", width = 1, height = 1, res = 300 )
|
||||
sens_siteP
|
||||
dev.off()
|
||||
|
||||
###########################################################
|
||||
|
||||
320
scripts/plotting/plotting_thesis/gid/dm_om_plots_gid.R
Normal file
320
scripts/plotting/plotting_thesis/gid/dm_om_plots_gid.R
Normal file
|
|
@ -0,0 +1,320 @@
|
|||
#################
|
||||
# Numbers
|
||||
##################
|
||||
#all_dm_om_df = dm_om_wf_lf_data(df = merged_df3, gene = gene)
|
||||
all_dm_om_df = dm_om_wf_lf_data(df = df3, gene = gene)
|
||||
|
||||
# lf_duet = all_dm_om_df[['lf_duet']]
|
||||
# table(lf_duet$param_type)
|
||||
################################################################
|
||||
|
||||
#======================
|
||||
# Data: Dist+Genomics
|
||||
#======================
|
||||
lf_dist_genP = all_dm_om_df[['lf_dist_gen']]
|
||||
wf_dist_genP = all_dm_om_df[['wf_dist_gen']]
|
||||
|
||||
levels(lf_dist_genP$param_type)
|
||||
#lf_dist_genP$param_type <- factor(lf_dist_genP$param_type, levels=c("Log10(MAF)", "Lig Dist(Å)", "PPI Dist(Å)"))
|
||||
table(lf_dist_genP$param_type)
|
||||
|
||||
genomics_param = c("Log10(MAF)")
|
||||
|
||||
dist_genP = lf_bp2(lf_dist_genP
|
||||
#, p_title
|
||||
, violin_quantiles = c(0.5), monochrome = F)
|
||||
dist_genP
|
||||
#-------------------
|
||||
# Genomics data plot
|
||||
#-------------------
|
||||
genomics_dataP = lf_dist_genP[lf_dist_genP$param_type%in%genomics_param,]
|
||||
genomics_dataP$param_type = factor(genomics_dataP$param_type)
|
||||
table(genomics_dataP$param_type)
|
||||
|
||||
genomicsP = lf_bp2(genomics_dataP
|
||||
#, p_title = ""
|
||||
, violin_quantiles = c(0.5), monochrome = F)
|
||||
|
||||
genomicsP
|
||||
|
||||
#check
|
||||
wilcox.test(wf_dist_genP$`Log10(MAF)`[wf_dist_genP$mutation_info_labels=="R"]
|
||||
, wf_dist_genP$`Log10(MAF)`[wf_dist_genP$mutation_info_labels=="S"], paired = FALSE)
|
||||
|
||||
tapply(wf_dist_genP$`Log10(MAF)`, wf_dist_genP$mutation_info_labels, summary)
|
||||
|
||||
#-------------------
|
||||
# Distance data plot:
|
||||
#--------------------
|
||||
# not genomics
|
||||
dist_dataP = lf_dist_genP[!lf_dist_genP$param_type%in%genomics_param,]
|
||||
dist_dataP$param_type = factor(dist_dataP$param_type)
|
||||
table(dist_dataP$param_type)
|
||||
levels(dist_dataP$param_type)
|
||||
# relevel factor to control ordering of appearance of plot
|
||||
dist_dataP$param_type <-relevel(dist_dataP$param_type, "Lig Dist(Å)" )
|
||||
table(dist_dataP$param_type)
|
||||
levels(dist_dataP$param_type)
|
||||
|
||||
distanceP = lf_bp2(dist_dataP
|
||||
#, p_title = ""
|
||||
, violin_quantiles = c(0.5), monochrome = F)
|
||||
|
||||
distanceP
|
||||
|
||||
# check
|
||||
wilcox.test(wf_dist_genP$`PPI Dist(Å)`[wf_dist_genP$mutation_info_labels=="R"]
|
||||
, wf_dist_genP$`PPI Dist(Å)`[wf_dist_genP$mutation_info_labels=="S"], paired = FALSE)
|
||||
|
||||
wilcox.test(wf_dist_genP$`Lig Dist(Å)`[wf_dist_genP$mutation_info_labels=="R"]
|
||||
, wf_dist_genP$`Lig Dist(Å)`[wf_dist_genP$mutation_info_labels=="S"], paired = FALSE)
|
||||
|
||||
|
||||
wilcox.test(wf_dist_genP$`NA Dist(Å)`[wf_dist_genP$mutation_info_labels=="R"]
|
||||
, wf_dist_genP$`NA Dist(Å)`[wf_dist_genP$mutation_info_labels=="S"], paired = FALSE)
|
||||
|
||||
tapply(wf_dist_genP$`PPI Dist(Å)`, wf_dist_genP$mutation_info_labels, summary)
|
||||
|
||||
tapply(wf_dist_genP$`Lig Dist(Å)`, wf_dist_genP$mutation_info_labels, summary)
|
||||
|
||||
|
||||
#-------------------
|
||||
# Distance data plot: LigDist
|
||||
#--------------------
|
||||
levels(dist_dataP$param_type)[[1]]
|
||||
#Lig Dist(Å), PPI Dist(Å)
|
||||
dist_data_lig = dist_dataP[dist_dataP$param_type%in%c(levels(dist_dataP$param_type)[[1]]),]
|
||||
dist_data_lig$param_type = factor(dist_data_lig$param_type)
|
||||
table(dist_data_lig$param_type)
|
||||
levels(dist_data_lig$param_type)
|
||||
distanceP_lig = lf_bp2(dist_data_lig
|
||||
#, p_title = ""
|
||||
, violin_quantiles = c(0.5), monochrome = F)
|
||||
|
||||
distanceP_lig
|
||||
|
||||
if (tolower(gene)%in%geneL_ppi2){
|
||||
#-------------------
|
||||
# Distance data plot: LigDist
|
||||
#--------------------
|
||||
levels(dist_dataP$param_type)[[2]]
|
||||
#Lig Dist(Å), PPI Dist(Å)
|
||||
dist_data_ppi2 = dist_dataP[dist_dataP$param_type%in%c(levels(dist_dataP$param_type)[[2]]),]
|
||||
dist_data_ppi2$param_type = factor(dist_data_ppi2$param_type)
|
||||
table(dist_data_ppi2$param_type)
|
||||
levels(dist_data_ppi2$param_type)
|
||||
distanceP_ppi2 = lf_bp2(dist_data_ppi2
|
||||
#, p_title = ""
|
||||
, violin_quantiles = c(0.5), monochrome = F)
|
||||
|
||||
distanceP_ppi2
|
||||
}
|
||||
|
||||
if (tolower(gene)%in%geneL_na){
|
||||
#-------------------
|
||||
# Distance data plot: NADist
|
||||
#--------------------
|
||||
levels(dist_dataP$param_type)[[2]]
|
||||
#Lig Dist(Å), PPI Dist(Å)
|
||||
dist_data_na = dist_dataP[dist_dataP$param_type%in%c(levels(dist_dataP$param_type)[[2]]),]
|
||||
dist_data_na$param_type = factor(dist_data_na$param_type)
|
||||
table(dist_data_na$param_type)
|
||||
levels(dist_data_na$param_type)
|
||||
distanceP_na = lf_bp2(dist_data_na
|
||||
#, p_title = ""
|
||||
, violin_quantiles = c(0.5), monochrome = F)
|
||||
|
||||
distanceP_na
|
||||
}
|
||||
#==============
|
||||
# Plot:DUET
|
||||
#==============
|
||||
lf_duetP = all_dm_om_df[['lf_duet']]
|
||||
#lf_duetP = lf_duet[!lf_duet$param_type%in%c(static_colsP),]
|
||||
table(lf_duetP$param_type)
|
||||
lf_duetP$param_type = factor(lf_duetP$param_type)
|
||||
table(lf_duetP$param_type)
|
||||
|
||||
duetP = lf_bp2(lf_duetP
|
||||
#, p_title = ""
|
||||
, violin_quantiles = c(0.5), monochrome = F
|
||||
, dot_transparency = 0.2)
|
||||
|
||||
#==============
|
||||
# Plot:FoldX
|
||||
#==============
|
||||
lf_foldxP = all_dm_om_df[['lf_foldx']]
|
||||
#lf_foldxP = lf_foldx[!lf_foldx$param_type%in%c(static_colsP),]
|
||||
table(lf_foldxP$param_type)
|
||||
lf_foldxP$param_type = factor(lf_foldxP$param_type)
|
||||
table(lf_foldxP$param_type)
|
||||
|
||||
foldxP = lf_bp2(lf_foldxP
|
||||
#, p_title = ""
|
||||
, violin_quantiles = c(0.5), monochrome = F
|
||||
, dot_transparency = 0.1)
|
||||
|
||||
#==============
|
||||
# Plot:DeepDDG
|
||||
#==============
|
||||
lf_deepddgP = all_dm_om_df[['lf_deepddg']]
|
||||
#lf_deepddgP = lf_deepddg[!lf_deepddg$param_type%in%c(static_colsP),]
|
||||
table(lf_deepddgP$param_type)
|
||||
lf_deepddgP$param_type = factor(lf_deepddgP$param_type)
|
||||
table(lf_deepddgP$param_type)
|
||||
|
||||
deepddgP = lf_bp2(lf_deepddgP
|
||||
#, p_title = ""
|
||||
, violin_quantiles = c(0.5), monochrome = F
|
||||
, dot_transparency = 0.2)
|
||||
|
||||
deepddgP
|
||||
|
||||
#==============
|
||||
# Plot: Dynamut2
|
||||
#==============
|
||||
lf_dynamut2P = all_dm_om_df[['lf_dynamut2']]
|
||||
#lf_dynamut2P = lf_dynamut2[!lf_dynamut2$param_type%in%c(static_colsP),]
|
||||
table(lf_dynamut2P$param_type)
|
||||
lf_dynamut2P$param_type = factor(lf_dynamut2P$param_type)
|
||||
table(lf_dynamut2P$param_type)
|
||||
|
||||
dynamut2P = lf_bp2(lf_dynamut2P
|
||||
#, p_title = ""
|
||||
, violin_quantiles = c(0.5), monochrome = F
|
||||
, dot_transparency = 0.2)
|
||||
|
||||
|
||||
#==============
|
||||
# Plot:ConSurf
|
||||
#==============
|
||||
lf_consurfP = all_dm_om_df[['lf_consurf']]
|
||||
#lf_consurfP = lf_consurf[!lf_consurf$param_type%in%c(static_colsP),]
|
||||
table(lf_consurfP$param_type)
|
||||
lf_consurfP$param_type = factor(lf_consurfP$param_type)
|
||||
table(lf_consurfP$param_type)
|
||||
|
||||
consurfP = lf_bp2(lf_consurfP
|
||||
#, p_title = ""
|
||||
, violin_quantiles = c(0.5), monochrome = F)
|
||||
|
||||
#==============
|
||||
# Plot:PROVEAN
|
||||
#==============
|
||||
lf_proveanP = all_dm_om_df[['lf_provean']]
|
||||
#lf_proveanP = lf_provean[!lf_provean$param_type%in%c(static_colsP),]
|
||||
table(lf_proveanP$param_type)
|
||||
lf_proveanP$param_type = factor(lf_proveanP$param_type)
|
||||
table(lf_proveanP$param_type)
|
||||
|
||||
proveanP = lf_bp2(lf_proveanP
|
||||
#, p_title = ""
|
||||
, violin_quantiles = c(0.5), monochrome = F)
|
||||
|
||||
#==============
|
||||
# Plot:SNAP2
|
||||
#==============
|
||||
lf_snap2P = all_dm_om_df[['lf_snap2']]
|
||||
#lf_snap2P = lf_snap2[!lf_snap2$param_type%in%c(static_colsP),]
|
||||
table(lf_snap2P$param_type)
|
||||
lf_snap2P$param_type = factor(lf_snap2P$param_type)
|
||||
table(lf_snap2P$param_type)
|
||||
|
||||
snap2P = lf_bp2(lf_snap2P
|
||||
#, p_title = ""
|
||||
, violin_quantiles = c(0.5), monochrome = F)
|
||||
|
||||
|
||||
############################################################################
|
||||
#================
|
||||
# Plot: mCSM-lig
|
||||
#================
|
||||
lf_mcsm_ligP = all_dm_om_df[['lf_mcsm_lig']]
|
||||
#lf_mcsm_ligP = lf_mcsm_lig[!lf_mcsm_lig$param_type%in%c(static_colsP),]
|
||||
table(lf_mcsm_ligP$param_type)
|
||||
lf_mcsm_ligP$param_type = factor(lf_mcsm_ligP$param_type)
|
||||
table(lf_mcsm_ligP$param_type)
|
||||
|
||||
mcsmligP = lf_bp2(lf_mcsm_ligP
|
||||
#, p_title = ""
|
||||
, violin_quantiles = c(0.5), monochrome = F
|
||||
, dot_transparency = 1)
|
||||
|
||||
|
||||
#=================
|
||||
# Plot: mmCSM-lig2
|
||||
#=================
|
||||
lf_mmcsm_lig2P = all_dm_om_df[['lf_mmcsm_lig2']]
|
||||
#lf_mmcsm_lig2P = lf_mmcsm_lig2P[!lf_mmcsm_lig2P$param_type%in%c(static_colsP),]
|
||||
table(lf_mmcsm_lig2P$param_type)
|
||||
lf_mmcsm_lig2P$param_type = factor(lf_mmcsm_lig2P$param_type)
|
||||
table(lf_mmcsm_lig2P$param_type)
|
||||
|
||||
mcsmlig2P = lf_bp2(lf_mmcsm_lig2P
|
||||
#, p_title = ""
|
||||
, violin_quantiles = c(0.5), monochrome = F
|
||||
, dot_transparency = 1)
|
||||
|
||||
mcsmlig2P
|
||||
|
||||
#================
|
||||
# Plot: mCSM-ppi2
|
||||
#================
|
||||
if (tolower(gene)%in%geneL_ppi2){
|
||||
lf_mcsm_ppi2P = all_dm_om_df[['lf_mcsm_ppi2']]
|
||||
#lf_mcsm_ppi2P = lf_mcsm_ppi2[!lf_mcsm_ppi2$param_type%in%c(static_colsP),]
|
||||
table(lf_mcsm_ppi2P$param_type)
|
||||
lf_mcsm_ppi2P$param_type = factor(lf_mcsm_ppi2P$param_type)
|
||||
table(lf_mcsm_ppi2P$param_type)
|
||||
|
||||
mcsmppi2P = lf_bp2(lf_mcsm_ppi2P
|
||||
#, p_title = ""
|
||||
, violin_quantiles = c(0.5), monochrome = F
|
||||
, dot_transparency = 1)
|
||||
|
||||
}
|
||||
|
||||
#==============
|
||||
# Plot: mCSM-NA
|
||||
#==============
|
||||
if (tolower(gene)%in%geneL_na){
|
||||
lf_mcsm_naP = all_dm_om_df[['lf_mcsm_na']]
|
||||
#lf_mcsm_naP = lf_mcsm_na[!lf_mcsm_na$param_type%in%c(static_colsP),]
|
||||
table(lf_mcsm_naP$param_type)
|
||||
lf_mcsm_naP$param_type = factor(lf_mcsm_naP$param_type)
|
||||
table(lf_mcsm_naP$param_type)
|
||||
|
||||
mcsmnaP = lf_bp2(lf_mcsm_naP
|
||||
#, p_title = ""
|
||||
, violin_quantiles = c(0.5), monochrome = F
|
||||
, dot_transparency = 1)
|
||||
|
||||
}
|
||||
|
||||
######################################
|
||||
# Outplot with stats
|
||||
######################################
|
||||
# outdir_images = paste0("~/git/Writing/thesis/images/results/", tolower(gene), "/")
|
||||
#
|
||||
# dm_om_combinedP = paste0(outdir_images
|
||||
# ,tolower(gene)
|
||||
# ,"_dm_om_all.svg" )
|
||||
#
|
||||
# cat("DM OM plots with stats:", dm_om_combinedP)
|
||||
# svg(dm_om_combinedP, width = 32, height = 18)
|
||||
# cowplot::plot_grid(
|
||||
# cowplot::plot_grid(duetP, foldxP, deepddgP, dynamut2P, genomicsP, distanceP
|
||||
# , nrow=1
|
||||
# , rel_widths = c(1/7, 1/7,1/7,1/7, 1/7, 1.75/7)),
|
||||
# #, rel_widths = c(1/8, 1/8,1/8,1/8, 1/8, 2.75/8)), # for 3 distances
|
||||
# cowplot::plot_grid(consurfP, proveanP, snap2P
|
||||
# , mcsmligP
|
||||
# , mcsmlig2P
|
||||
# , mcsmppi2P
|
||||
# #, mcsmnaP
|
||||
# , nrow=1),
|
||||
# nrow=2)
|
||||
#
|
||||
# dev.off()
|
||||
|
||||
|
||||
178
scripts/plotting/plotting_thesis/gid/gg_pairs_all_gid.R
Normal file
178
scripts/plotting/plotting_thesis/gid/gg_pairs_all_gid.R
Normal file
|
|
@ -0,0 +1,178 @@
|
|||
#source("~/git/LSHTM_analysis/config/embb.R")
|
||||
#source("~/git/LSHTM_analysis/scripts/plotting/plotting_colnames.R")
|
||||
#source("~/git/LSHTM_analysis/scripts/plotting/get_plotting_dfs.R")
|
||||
|
||||
my_gg_pairs=function(plot_df, plot_title
|
||||
, tt_args_size = 2.5
|
||||
, gp_args_size = 2.5){
|
||||
ggpairs(plot_df,
|
||||
columns = 1:(ncol(plot_df)-1),
|
||||
upper = list(
|
||||
continuous = wrap('cor', # ggally_cor()
|
||||
method = "spearman",
|
||||
use = "pairwise.complete.obs",
|
||||
title="ρ",
|
||||
digits=2,
|
||||
justify_labels = "centre",
|
||||
#title_args=c(colour="black"),
|
||||
title_args=c(size=tt_args_size),#2.5
|
||||
group_args=c(size=gp_args_size)#2.5
|
||||
)
|
||||
),
|
||||
lower = list(
|
||||
continuous = wrap("points",
|
||||
alpha = 0.7,
|
||||
size=0.125),
|
||||
combo = wrap("dot",
|
||||
alpha = 0.7,
|
||||
size=0.125)
|
||||
),
|
||||
aes(colour = factor(ifelse(dst_mode==0,
|
||||
"S",
|
||||
"R") ),
|
||||
alpha = 0.5),
|
||||
title=plot_title) +
|
||||
|
||||
scale_colour_manual(values = c("red", "blue")) +
|
||||
scale_fill_manual(values = c("red", "blue")) #+
|
||||
# theme(text = element_text(size=7,
|
||||
# face="bold"))
|
||||
}
|
||||
|
||||
DistCutOff = 10
|
||||
###########################################################################
|
||||
geneL_normal = c("pnca")
|
||||
geneL_na = c("gid", "rpob")
|
||||
geneL_ppi2 = c("alr", "embb", "katg", "rpob")
|
||||
|
||||
merged_df3 = as.data.frame(merged_df3)
|
||||
|
||||
corr_plotdf = corr_data_extract(merged_df3
|
||||
, gene = gene
|
||||
, drug = drug
|
||||
, extract_scaled_cols = F)
|
||||
|
||||
aff_dist_cols = colnames(corr_plotdf)[grep("Dist", colnames(corr_plotdf))]
|
||||
static_cols = c("Log10(MAF)"
|
||||
, "Log10(OR)"
|
||||
)
|
||||
############################################################
|
||||
#=============================================
|
||||
# Creating masked df for affinity data
|
||||
#=============================================
|
||||
corr_affinity_df = corr_plotdf
|
||||
|
||||
#----------------------
|
||||
# Mask affinity columns
|
||||
#-----------------------
|
||||
corr_affinity_df[corr_affinity_df["Lig-Dist"]>DistCutOff,"mCSM-lig"]=0
|
||||
corr_affinity_df[corr_affinity_df["Lig-Dist"]>DistCutOff,"mmCSM-lig"]=0
|
||||
|
||||
if (tolower(gene)%in%geneL_ppi2){
|
||||
corr_affinity_df[corr_affinity_df["PPI-Dist"]>DistCutOff,"mCSM-PPI2"]=0
|
||||
}
|
||||
|
||||
if (tolower(gene)%in%geneL_na){
|
||||
corr_affinity_df[corr_affinity_df["NCA-Dist"]>DistCutOff,"mCSM-NA"]=0
|
||||
}
|
||||
|
||||
# count 0
|
||||
#res <- colSums(corr_affinity_df==0)/nrow(corr_affinity_df)*100
|
||||
unmasked_vals <- nrow(corr_affinity_df) - colSums(corr_affinity_df==0)
|
||||
unmasked_vals
|
||||
|
||||
##########################################################
|
||||
#================
|
||||
# Stability
|
||||
#================
|
||||
corr_ps_colnames = c(static_cols
|
||||
, "DUET"
|
||||
, "FoldX"
|
||||
, "DeepDDG"
|
||||
, "Dynamut2"
|
||||
, aff_dist_cols
|
||||
, "dst_mode")
|
||||
|
||||
corr_df_ps = corr_plotdf[, corr_ps_colnames]
|
||||
|
||||
# Plot #1
|
||||
plot_corr_df_ps = my_gg_pairs(corr_df_ps, plot_title="Stability estimates")
|
||||
##########################################################
|
||||
#================
|
||||
# Conservation
|
||||
#================
|
||||
corr_conservation_cols = c( static_cols
|
||||
, "ConSurf"
|
||||
, "SNAP2"
|
||||
, "PROVEAN"
|
||||
#, aff_dist_cols
|
||||
, "dst_mode"
|
||||
)
|
||||
|
||||
corr_df_cons = corr_plotdf[, corr_conservation_cols]
|
||||
|
||||
# Plot #2
|
||||
plot_corr_df_cons = my_gg_pairs(corr_df_cons, plot_title="Conservation estimates")
|
||||
|
||||
##########################################################
|
||||
#================
|
||||
# Affinity: lig, ppi and na as applicable
|
||||
#================
|
||||
#corr_df_lig = corr_plotdf[corr_plotdf["Lig-Dist"]<DistCutOff,]
|
||||
common_aff_colnames = c("mCSM-lig"
|
||||
, "mmCSM-lig")
|
||||
|
||||
if (tolower(gene)%in%geneL_normal){
|
||||
aff_colnames = common_aff_colnames
|
||||
}
|
||||
if (tolower(gene)%in%geneL_ppi2){
|
||||
aff_colnames = c(common_aff_colnames, "mCSM-PPI2")
|
||||
}
|
||||
|
||||
if (tolower(gene)%in%geneL_na){
|
||||
aff_colnames = c(common_aff_colnames, "mCSM-NA")
|
||||
}
|
||||
|
||||
# building ffinal affinity colnames for correlation
|
||||
corr_aff_colnames = c(static_cols
|
||||
, aff_colnames
|
||||
, "dst_mode") # imp
|
||||
|
||||
corr_df_aff = corr_affinity_df[, corr_aff_colnames]
|
||||
colnames(corr_df_aff)
|
||||
|
||||
# Plot #3
|
||||
plot_corr_df_aff = my_gg_pairs(corr_df_aff,
|
||||
plot_title="Affinity estimates",
|
||||
tt_args_size = 4,
|
||||
gp_args_size = 4)
|
||||
|
||||
#=============
|
||||
# combine
|
||||
#=============
|
||||
|
||||
#png("/home/tanu/tmp/gg_pairs_all.png", height = 6, width=11.75, unit="in",res=300)
|
||||
png(paste0(outdir_images
|
||||
,tolower(gene)
|
||||
,"_CorrAB.png"), height = 7, width=11.75, unit="in",res=300)
|
||||
|
||||
cowplot::plot_grid(ggmatrix_gtable(plot_corr_df_ps),
|
||||
ggmatrix_gtable(plot_corr_df_cons),
|
||||
# ggmatrix_gtable(plot_corr_df_aff),
|
||||
# nrow=1, ncol=3, rel_heights = 7,7,3
|
||||
nrow=1,
|
||||
rel_widths = c(1.5,1),
|
||||
labels = "AUTO",
|
||||
label_size = 12)
|
||||
dev.off()
|
||||
|
||||
# affinity corr
|
||||
#png("/home/tanu/tmp/gg_pairs_affinity.png", height =7, width=7, unit="in",res=300)
|
||||
png(paste0(outdir_images
|
||||
,tolower(gene)
|
||||
,"_CorrC.png"), height =7, width=7, unit="in",res=300)
|
||||
|
||||
cowplot::plot_grid(ggmatrix_gtable(plot_corr_df_aff),
|
||||
labels = "C",
|
||||
label_size = 12)
|
||||
dev.off()
|
||||
38
scripts/plotting/plotting_thesis/just.R
Normal file
38
scripts/plotting/plotting_thesis/just.R
Normal file
|
|
@ -0,0 +1,38 @@
|
|||
bar = merged_df3[, c("mutationinformation", "position", "pos_count", "df2_pos_count_all")]
|
||||
|
||||
foo = merged_df3[, c("mutationinformation", "position"
|
||||
, "ligand_distance"
|
||||
, "interface_dist"
|
||||
, "or_mychisq"
|
||||
, "maf")]
|
||||
|
||||
foo[foo$position==445,]
|
||||
|
||||
foo1 = na.omit(foo)
|
||||
foo2 = foo1[order(-foo1$or_mychisq),]
|
||||
head(foo1, 10)
|
||||
head(foo2, 20)
|
||||
|
||||
foo2[foo2$position==497,]
|
||||
|
||||
foo2[foo2$position==328,]
|
||||
foo2[foo2$position==347,]
|
||||
|
||||
ggplot(foo, aes(x= position), reorder(x)) + geom_bar()
|
||||
foo2[foo2$position%in%c(405, 406, 407, 408,409),]
|
||||
|
||||
dr = seq(319,334, 1)
|
||||
dr = seq(303, 306, 1)
|
||||
|
||||
foo2[foo2$position%in%dr,]
|
||||
foo2[foo2$position%in%aa_pos_dsl,]
|
||||
foo2[foo2$position%in%aa_pos_ca,]
|
||||
foo2[foo2$position%in%c(644,645, 690),]
|
||||
# maf
|
||||
|
||||
foo3 = foo[order(-foo$maf),]
|
||||
head(foo3)
|
||||
E378A, M306V, M306I, G406A, R24P, Q497R
|
||||
|
||||
foo3_nona = foo1[order(-foo1$maf),]
|
||||
head(foo3_nona)
|
||||
364
scripts/plotting/plotting_thesis/katg/basic_barplots_katg.R
Normal file
364
scripts/plotting/plotting_thesis/katg/basic_barplots_katg.R
Normal file
|
|
@ -0,0 +1,364 @@
|
|||
#!/usr/bin/env Rscript
|
||||
#########################################################
|
||||
# TASK: Barplots
|
||||
# basic barplots with outcome
|
||||
# basic barplots with frequency of count of mutations
|
||||
#########################################################
|
||||
#=============
|
||||
# Data: Input
|
||||
#==============
|
||||
#source("~/git/LSHTM_analysis/config/katg.R")
|
||||
#source("~/git/LSHTM_analysis/scripts/plotting/get_plotting_dfs.R")
|
||||
|
||||
#cat("\nSourced plotting cols as well:", length(plotting_cols))
|
||||
|
||||
####################################################
|
||||
class(merged_df3)
|
||||
|
||||
df3 = subset(merged_df3, select = -c(pos_count))
|
||||
|
||||
#=======
|
||||
# output
|
||||
#=======
|
||||
outdir_images = paste0("~/git/Writing/thesis/images/results/", tolower(gene), "/")
|
||||
cat("plots will output to:", outdir_images)
|
||||
|
||||
##########################################################
|
||||
# blue, red bp
|
||||
sts = 8
|
||||
lts = 8
|
||||
ats = 8
|
||||
als = 8
|
||||
ltis = 8
|
||||
geom_ls = 2.2
|
||||
|
||||
#pos_count
|
||||
subtitle_size = 8
|
||||
geom_ls_pc = 2.2
|
||||
leg_text_size = 8
|
||||
axis_text_size = 8
|
||||
axis_label_size = 8
|
||||
|
||||
###########################################################
|
||||
#------------------------------
|
||||
# plot default sizes
|
||||
#------------------------------
|
||||
#=========================
|
||||
# Affinity outcome
|
||||
# check this var: outcome_cols_affinity
|
||||
# get from preformatting or put in globals
|
||||
#==========================
|
||||
DistCutOff
|
||||
LigDist_colname # = "ligand_distance" # from globals
|
||||
ppi2Dist_colname
|
||||
naDist_colname
|
||||
|
||||
###########################################################
|
||||
# get plotting data within the distance
|
||||
df3_lig = df3[df3[[LigDist_colname]]<DistCutOff,]
|
||||
df3_ppi2 = df3[df3[[ppi2Dist_colname]]<DistCutOff,]
|
||||
df3_na = df3[df3[[naDist_colname]]<DistCutOff,]
|
||||
common_bp_title = paste0("Sites <", DistCutOff, angstroms_symbol)
|
||||
|
||||
#------------------------------
|
||||
# barplot for ligand affinity:
|
||||
# <10 Ang of ligand
|
||||
#------------------------------
|
||||
mLigP = stability_count_bp(plotdf = df3_lig
|
||||
, df_colname = "ligand_outcome"
|
||||
#, leg_title = "mCSM-lig"
|
||||
#, bp_plot_title = paste(common_bp_title, "ligand")
|
||||
, yaxis_title = "Number of nsSNPs"
|
||||
, leg_position = "none"
|
||||
, subtitle_text = "mCSM\nLig"
|
||||
, bar_fill_values = c("#F8766D", "#00BFC4")
|
||||
, subtitle_colour= "black"
|
||||
, sts = sts
|
||||
, lts = lts
|
||||
, ats = ats
|
||||
, als = als
|
||||
, ltis = ltis
|
||||
, geom_ls = geom_ls
|
||||
)
|
||||
mLigP
|
||||
#------------------------------
|
||||
# barplot for ligand affinity:
|
||||
# <10 Ang of ligand
|
||||
# mmCSM-lig: will be the same no. of sites but the effect will be different
|
||||
#------------------------------
|
||||
mmLigP = stability_count_bp(plotdf = df3_lig
|
||||
, df_colname = "mmcsm_lig_outcome"
|
||||
#, leg_title = "mmCSM-lig"
|
||||
#, label_categories = labels_mmlig
|
||||
#, bp_plot_title = paste(common_bp_title, "ligand")
|
||||
|
||||
, yaxis_title = ""
|
||||
, leg_position = "none"
|
||||
, subtitle_text = "mmCSM\nLig"
|
||||
, bar_fill_values = c("#F8766D", "#00BFC4")
|
||||
, subtitle_colour= "black"
|
||||
, sts = sts
|
||||
, lts = lts
|
||||
, ats = ats
|
||||
, als = als
|
||||
, ltis = ltis
|
||||
, geom_ls = geom_ls
|
||||
)
|
||||
mmLigP
|
||||
#------------------------------
|
||||
# barplot for ppi2 affinity
|
||||
# <10 Ang of interface
|
||||
#------------------------------
|
||||
if (tolower(gene)%in%geneL_ppi2){
|
||||
ppi2P = stability_count_bp(plotdf = df3_ppi2
|
||||
, df_colname = "mcsm_ppi2_outcome"
|
||||
#, leg_title = "mCSM-ppi2"
|
||||
#, label_categories = labels_ppi2
|
||||
#, bp_plot_title = paste(common_bp_title, "PP-interface")
|
||||
|
||||
, yaxis_title = "Number of nsSNPs"
|
||||
, leg_position = "none"
|
||||
, subtitle_text = "mCSM\nPPI2"
|
||||
, bar_fill_values = c("#F8766D", "#00BFC4")
|
||||
, subtitle_colour= "black"
|
||||
, sts = sts
|
||||
, lts = lts
|
||||
, ats = ats
|
||||
, als = als
|
||||
, ltis = ltis
|
||||
, geom_ls = geom_ls
|
||||
)
|
||||
ppi2P
|
||||
}
|
||||
#----------------------------
|
||||
# barplot for ppi2 affinity
|
||||
# <10 Ang of interface
|
||||
#------------------------------
|
||||
if (tolower(gene)%in%geneL_na){
|
||||
nca_distP = stability_count_bp(plotdf = df3_na
|
||||
, df_colname = "mcsm_na_outcome"
|
||||
#, leg_title = "mCSM-NA"
|
||||
#, label_categories =
|
||||
#, bp_plot_title = paste(common_bp_title, "Dist to NA")
|
||||
|
||||
, yaxis_title = "Number of nsSNPs"
|
||||
, leg_position = "none"
|
||||
, subtitle_text = "mCSM\nNA"
|
||||
, bar_fill_values = c("#F8766D", "#00BFC4")
|
||||
, subtitle_colour= "black"
|
||||
, sts = sts
|
||||
, lts = lts
|
||||
, ats = ats
|
||||
, als = als
|
||||
, ltis = ltis
|
||||
, geom_ls = geom_ls
|
||||
)
|
||||
nca_distP
|
||||
}
|
||||
|
||||
#####################################################################
|
||||
# ------------------------------
|
||||
# bp site site count: mCSM-lig
|
||||
# < 10 Ang ligand
|
||||
# ------------------------------
|
||||
common_bp_title = paste0("Sites <", DistCutOff, angstroms_symbol)
|
||||
|
||||
posC_lig = site_snp_count_bp(plotdf = df3_lig
|
||||
, df_colname = "position"
|
||||
, xaxis_title = "Number of nsSNPs"
|
||||
, yaxis_title = "Number of Sites"
|
||||
, subtitle_colour = "chocolate4"
|
||||
, subtitle_text = ""
|
||||
, subtitle_size = subtitle_size
|
||||
, geom_ls = geom_ls_pc
|
||||
, leg_text_size = leg_text_size
|
||||
, axis_text_size = axis_text_size
|
||||
, axis_label_size = axis_label_size)
|
||||
|
||||
posC_lig
|
||||
#------------------------------
|
||||
# bp site site count: ppi2
|
||||
# < 10 Ang interface
|
||||
#------------------------------
|
||||
if (tolower(gene)%in%geneL_ppi2){
|
||||
posC_ppi2 = site_snp_count_bp(plotdf = df3_ppi2
|
||||
, df_colname = "position"
|
||||
, xaxis_title = "Number of nsSNPs"
|
||||
, yaxis_title = "Number of Sites"
|
||||
, subtitle_colour = "chocolate4"
|
||||
, subtitle_text = ""
|
||||
, subtitle_size = subtitle_size
|
||||
, geom_ls = geom_ls_pc
|
||||
, leg_text_size = leg_text_size
|
||||
, axis_text_size = axis_text_size
|
||||
, axis_label_size = axis_label_size)
|
||||
posC_ppi2
|
||||
}
|
||||
|
||||
#------------------------------
|
||||
# bp site site count: NCA dist
|
||||
# < 10 Ang nca
|
||||
#------------------------------
|
||||
if (tolower(gene)%in%geneL_na){
|
||||
posC_nca = site_snp_count_bp(plotdf = df3_na
|
||||
, df_colname = "position"
|
||||
, xaxis_title = "Number of nsSNPs"
|
||||
, yaxis_title = "Number of Sites"
|
||||
, subtitle_colour = "chocolate4"
|
||||
, subtitle_text = ""
|
||||
, subtitle_size = subtitle_size
|
||||
, geom_ls = geom_ls_pc
|
||||
, leg_text_size = leg_text_size
|
||||
, axis_text_size = axis_text_size
|
||||
, axis_label_size = axis_label_size)
|
||||
posC_nca
|
||||
}
|
||||
#===============================================================
|
||||
#------------------------------
|
||||
# bp site site count: ALL
|
||||
# <10 Ang ligand
|
||||
#------------------------------
|
||||
posC_all = site_snp_count_bp(plotdf = df3
|
||||
, df_colname = "position"
|
||||
, xaxis_title = "Number of nsSNPs"
|
||||
, yaxis_title = "Number of Sites"
|
||||
, subtitle_colour = "chocolate4"
|
||||
, subtitle_text = "All mutations sites"
|
||||
, subtitle_size = subtitle_size
|
||||
, geom_ls = geom_ls_pc
|
||||
, leg_text_size = leg_text_size
|
||||
, axis_text_size = axis_text_size
|
||||
, axis_label_size = axis_label_size)
|
||||
posC_all
|
||||
##################################################################
|
||||
consurfP = stability_count_bp(plotdf = df3
|
||||
, df_colname = "consurf_outcome"
|
||||
#, leg_title = "ConSurf"
|
||||
#, label_categories = labels_consurf
|
||||
, yaxis_title = "Number of nsSNPs"
|
||||
, leg_position = "top"
|
||||
, subtitle_text = "ConSurf"
|
||||
, bar_fill_values = consurf_colours # from globals
|
||||
, subtitle_colour= "black"
|
||||
, sts = sts
|
||||
, lts = lts
|
||||
, ats = ats
|
||||
, als = als
|
||||
, ltis = ltis
|
||||
, geom_ls = geom_ls)
|
||||
|
||||
consurfP
|
||||
|
||||
##############################################################
|
||||
sts_so = 10
|
||||
lts_so = 10
|
||||
ats_so = 10
|
||||
als_so = 10
|
||||
ltis_so = 10
|
||||
geom_ls_so = 2.5
|
||||
#===================
|
||||
# Stability
|
||||
#===================
|
||||
# duetP
|
||||
duetP = stability_count_bp(plotdf = df3
|
||||
, df_colname = "duet_outcome"
|
||||
, leg_title = "mCSM-DUET"
|
||||
#, label_categories = labels_duet
|
||||
, yaxis_title = "Number of nsSNPs"
|
||||
, leg_position = "none"
|
||||
, subtitle_text = "mCSM-DUET"
|
||||
, bar_fill_values = c("#F8766D", "#00BFC4")
|
||||
, subtitle_colour= "black"
|
||||
, sts = sts_so
|
||||
, lts = lts_so
|
||||
, ats = ats_so
|
||||
, als = als_so
|
||||
, ltis = ltis_so
|
||||
, geom_ls = geom_ls_so)
|
||||
duetP
|
||||
|
||||
# foldx
|
||||
foldxP = stability_count_bp(plotdf = df3
|
||||
, df_colname = "foldx_outcome"
|
||||
#, leg_title = "FoldX"
|
||||
#, label_categories = labels_foldx
|
||||
, yaxis_title = ""
|
||||
, leg_position = "none"
|
||||
, subtitle_text = "FoldX"
|
||||
, bar_fill_values = c("#F8766D", "#00BFC4")
|
||||
, sts = sts_so
|
||||
, lts = lts_so
|
||||
, ats = ats_so
|
||||
, als = als_so
|
||||
, ltis = ltis_so
|
||||
, geom_ls = geom_ls_so)
|
||||
foldxP
|
||||
|
||||
# deepddg
|
||||
deepddgP = stability_count_bp(plotdf = df3
|
||||
, df_colname = "deepddg_outcome"
|
||||
#, leg_title = "DeepDDG"
|
||||
#, label_categories = labels_deepddg
|
||||
, yaxis_title = ""
|
||||
, leg_position = "none"
|
||||
, subtitle_text = "DeepDDG"
|
||||
, bar_fill_values = c("#F8766D", "#00BFC4")
|
||||
, sts = sts_so
|
||||
, lts = lts_so
|
||||
, ats = ats_so
|
||||
, als = als_so
|
||||
, ltis = ltis_so
|
||||
, geom_ls = geom_ls_so)
|
||||
deepddgP
|
||||
|
||||
# deepddg
|
||||
dynamut2P = stability_count_bp(plotdf = df3
|
||||
, df_colname = "ddg_dynamut2_outcome"
|
||||
#, leg_title = "Dynamut2"
|
||||
#, label_categories = labels_ddg_dynamut2_outcome
|
||||
, yaxis_title = ""
|
||||
, leg_position = "none"
|
||||
, subtitle_text = "Dynamut2"
|
||||
, bar_fill_values = c("#F8766D", "#00BFC4")
|
||||
, sts = sts_so
|
||||
, lts = lts_so
|
||||
, ats = ats_so
|
||||
, als = als_so
|
||||
, ltis = ltis_so
|
||||
, geom_ls = geom_ls_so)
|
||||
dynamut2P
|
||||
|
||||
# provean
|
||||
proveanP = stability_count_bp(plotdf = df3
|
||||
, df_colname = "provean_outcome"
|
||||
#, leg_title = "PROVEAN"
|
||||
#, label_categories = labels_provean
|
||||
, yaxis_title = "Number of nsSNPs"
|
||||
, leg_position = "none" # top
|
||||
, subtitle_text = "PROVEAN"
|
||||
, bar_fill_values = c("#D01C8B", "#F1B6DA") # light pink and deep
|
||||
, sts = sts_so
|
||||
, lts = lts_so
|
||||
, ats = ats_so
|
||||
, als = als_so
|
||||
, ltis = ltis_so
|
||||
, geom_ls = geom_ls_so)
|
||||
proveanP
|
||||
|
||||
# snap2
|
||||
snap2P = stability_count_bp(plotdf = df3
|
||||
, df_colname = "snap2_outcome"
|
||||
#, leg_title = "SNAP2"
|
||||
#, label_categories = labels_snap2
|
||||
, yaxis_title = ""
|
||||
, leg_position = "none" # top
|
||||
, subtitle_text = "SNAP2"
|
||||
, bar_fill_values = c("#D01C8B", "#F1B6DA") # light pink and deep
|
||||
, sts = sts_so
|
||||
, lts = lts_so
|
||||
, ats = ats_so
|
||||
, als = als_so
|
||||
, ltis = ltis_so
|
||||
, geom_ls = geom_ls_so)
|
||||
snap2P
|
||||
#####################################################################################
|
||||
|
|
@ -0,0 +1,309 @@
|
|||
#=============
|
||||
# Data: Input
|
||||
#==============
|
||||
#source("~/git/LSHTM_analysis/config/katg.R")
|
||||
#source("~/git/LSHTM_analysis/scripts/plotting/get_plotting_dfs.R")
|
||||
source("/home/tanu/git/LSHTM_analysis/scripts/plotting/plotting_thesis/katg/basic_barplots_katg.R")
|
||||
source("/home/tanu/git/LSHTM_analysis/scripts/plotting/plotting_thesis/katg/pe_sens_site_count_katg.R")
|
||||
|
||||
if ( tolower(gene)%in%c("katg") ){
|
||||
cat("\nPlots available for layout are:")
|
||||
|
||||
duetP
|
||||
foldxP
|
||||
deepddgP
|
||||
dynamut2P
|
||||
proveanP
|
||||
snap2P
|
||||
|
||||
mLigP
|
||||
mmLigP
|
||||
posC_lig
|
||||
|
||||
ppi2P
|
||||
posC_ppi2
|
||||
|
||||
peP2
|
||||
sens_siteP
|
||||
peP # not used
|
||||
sensP # not used
|
||||
}
|
||||
|
||||
|
||||
#========================
|
||||
# Common title settings
|
||||
#=========================
|
||||
theme_georgia <- function(...) {
|
||||
theme_gray(base_family = "sans", ...) +
|
||||
theme(plot.title = element_text(face = "bold"))
|
||||
}
|
||||
title_theme <- calc_element("plot.title", theme_georgia())
|
||||
|
||||
###############################################################
|
||||
common_bp_title = paste0("Sites <", DistCutOff, angstroms_symbol)
|
||||
|
||||
# extract common legends
|
||||
# lig affinity
|
||||
common_legend_outcome = get_legend(mLigP +
|
||||
guides(color = guide_legend(nrow = 1)) +
|
||||
theme(legend.position = "top"))
|
||||
|
||||
# stability
|
||||
common_legend_outcome = get_legend(duetP +
|
||||
guides(color = guide_legend(nrow = 1)) +
|
||||
theme(legend.position = "top"))
|
||||
# conservation
|
||||
cons_common_legend_outcome = get_legend(snap2P +
|
||||
guides(color = guide_legend(nrow = 1)) +
|
||||
theme(legend.position = "top"))
|
||||
###################################################################
|
||||
#==================================
|
||||
# Stability+Conservation: COMBINE
|
||||
#==================================
|
||||
tt_size = 10
|
||||
#----------------------------
|
||||
# stability and consv title
|
||||
#----------------------------
|
||||
tt_stab = ggdraw() +
|
||||
draw_label(
|
||||
paste0("Stability outcome"),
|
||||
fontfamily = title_theme$family,
|
||||
fontface = title_theme$face,
|
||||
#size = title_theme$size
|
||||
size = tt_size
|
||||
)
|
||||
|
||||
tt_cons = ggdraw() +
|
||||
draw_label(
|
||||
paste0("Conservation outcome"),
|
||||
fontfamily = title_theme$family,
|
||||
fontface = title_theme$face,
|
||||
size = tt_size
|
||||
)
|
||||
|
||||
#----------------------
|
||||
# Output plot
|
||||
#-----------------------
|
||||
stab_cons_CLP = paste0(outdir_images
|
||||
,tolower(gene)
|
||||
,"_stab_cons_BP_CLP.png")
|
||||
|
||||
print(paste0("plot filename:", stab_cons_CLP))
|
||||
png(stab_cons_CLP, units = "in", width = 10, height = 5, res = 300 )
|
||||
|
||||
cowplot::plot_grid(
|
||||
cowplot::plot_grid(
|
||||
cowplot::plot_grid(
|
||||
tt_stab,
|
||||
common_legend_outcome,
|
||||
nrow = 2
|
||||
),
|
||||
cowplot::plot_grid(
|
||||
duetP,
|
||||
foldxP,
|
||||
deepddgP,
|
||||
dynamut2P,
|
||||
nrow = 1,
|
||||
labels = c("A", "B", "C", "D"),
|
||||
label_size = 12),
|
||||
nrow = 2,
|
||||
rel_heights=c(1,10)
|
||||
),
|
||||
NULL,
|
||||
cowplot::plot_grid(
|
||||
cowplot::plot_grid(
|
||||
cowplot::plot_grid(
|
||||
tt_cons,
|
||||
cons_common_legend_outcome,
|
||||
nrow = 2
|
||||
),
|
||||
cowplot::plot_grid(
|
||||
proveanP,
|
||||
snap2P,
|
||||
nrow=1,
|
||||
labels = c("E", "F"),
|
||||
align = "hv"),
|
||||
nrow = 2,
|
||||
rel_heights = c(1, 10),
|
||||
label_size = 12),
|
||||
nrow=1
|
||||
),
|
||||
rel_widths = c(2,0.15,1),
|
||||
nrow=1
|
||||
)
|
||||
|
||||
dev.off()
|
||||
|
||||
#################################################################
|
||||
#=======================================
|
||||
# Affinity barplots: COMBINE ALL four
|
||||
#========================================
|
||||
ligT = paste0(common_bp_title, " ligand")
|
||||
lig_affT = ggdraw() +
|
||||
draw_label(
|
||||
ligT,
|
||||
fontfamily = title_theme$family,
|
||||
fontface = title_theme$face,
|
||||
#size = title_theme$size
|
||||
size = 8
|
||||
)
|
||||
|
||||
p1 = cowplot::plot_grid(cowplot::plot_grid(lig_affT
|
||||
, common_legend_outcome
|
||||
, nrow=2),
|
||||
cowplot::plot_grid(mLigP, mmLigP, posC_lig
|
||||
, nrow = 1
|
||||
, rel_widths = c(1,0.65,1.8)
|
||||
, align = "h"),
|
||||
nrow = 2,
|
||||
rel_heights = c(1,8)
|
||||
|
||||
)
|
||||
p1
|
||||
|
||||
###########################################################
|
||||
ppi2T = paste0(common_bp_title, " PP-interface")
|
||||
ppi2_affT = ggdraw() +
|
||||
draw_label(
|
||||
ppi2T,
|
||||
fontfamily = title_theme$family,
|
||||
fontface = title_theme$face,
|
||||
size = 8
|
||||
)
|
||||
|
||||
p3 = cowplot::plot_grid(cowplot::plot_grid(ppi2_affT, common_legend_outcome, nrow=2),
|
||||
cowplot::plot_grid(ppi2P, posC_ppi2
|
||||
, nrow = 1
|
||||
, rel_widths = c(1,1.9)
|
||||
, align = "h"),
|
||||
nrow = 2,
|
||||
rel_heights = c(1,8)
|
||||
)
|
||||
p3
|
||||
|
||||
# PE + All position count
|
||||
peT_allT = ggdraw() +
|
||||
draw_label(
|
||||
paste0("All mutation sites"),
|
||||
fontfamily = title_theme$family,
|
||||
fontface = title_theme$face,
|
||||
#size = title_theme$size
|
||||
size = 8
|
||||
)
|
||||
|
||||
p4 = cowplot::plot_grid(cowplot::plot_grid(peT_allT, nrow = 2
|
||||
, rel_widths = c(1,3),axis = "lr"),
|
||||
cowplot::plot_grid(
|
||||
peP2, posC_all,
|
||||
nrow = 2,
|
||||
rel_widths = c(1,1),
|
||||
align = "v",
|
||||
axis = "lr",
|
||||
rel_heights = c(1,8)
|
||||
),
|
||||
rel_heights = c(1,18),
|
||||
nrow = 2,axis = "lr")
|
||||
p4
|
||||
|
||||
|
||||
#### Combine p1+p3+p4 ####
|
||||
w = 11.79
|
||||
h = 3.5
|
||||
mut_impact_CLP = paste0(outdir_images
|
||||
,tolower(gene)
|
||||
,"_mut_impactCLP.png")
|
||||
|
||||
#svg(affP, width = 20, height = 5.5)
|
||||
print(paste0("plot filename:", mut_impact_CLP))
|
||||
png(mut_impact_CLP, units = "in", width = w, height = h, res = 300 )
|
||||
|
||||
cowplot::plot_grid(p1,
|
||||
p3,
|
||||
p4,
|
||||
nrow = 1,
|
||||
labels = "AUTO",
|
||||
label_size = 12,
|
||||
rel_widths = c(2.5,2,2)
|
||||
#, rel_heights = c(1)
|
||||
)
|
||||
|
||||
dev.off()
|
||||
w = 11.79
|
||||
h = 3.5
|
||||
mut_impact_CLP = paste0(outdir_images
|
||||
,tolower(gene)
|
||||
,"_mut_impactCLP.png")
|
||||
|
||||
#svg(affP, width = 20, height = 5.5)
|
||||
print(paste0("plot filename:", mut_impact_CLP))
|
||||
png(mut_impact_CLP, units = "in", width = w, height = h, res = 300 )
|
||||
|
||||
cowplot::plot_grid(p1,
|
||||
p3,
|
||||
p4,
|
||||
nrow = 1,
|
||||
labels = "AUTO",
|
||||
label_size = 12,
|
||||
rel_widths = c(2.5,2,2)
|
||||
#, rel_heights = c(1)
|
||||
)
|
||||
|
||||
dev.off()
|
||||
|
||||
##################################################
|
||||
sensP
|
||||
consurfP
|
||||
#=================
|
||||
#### Combine sensitivity + ConSurf ####
|
||||
# or ConSurf
|
||||
#=================
|
||||
w = 3
|
||||
h = 3
|
||||
# sens_conP = paste0(outdir_images
|
||||
# ,tolower(gene)
|
||||
# ,"_sens_cons_CLP.png")
|
||||
#
|
||||
# print(paste0("plot filename:", sens_conP))
|
||||
# png(sens_conP, units = "in", width = w, height = h, res = 300 )
|
||||
#
|
||||
# cowplot::plot_grid(sensP, consurfP,
|
||||
# nrow = 2,
|
||||
# rel_heights = c(1, 1.5)
|
||||
# )
|
||||
#
|
||||
# dev.off()
|
||||
|
||||
conCLP = paste0(outdir_images
|
||||
,tolower(gene)
|
||||
,"_consurf_BP.png")
|
||||
|
||||
print(paste0("plot filename:", conCLP))
|
||||
png(conCLP, units = "in", width = w, height = h, res = 300 )
|
||||
consurfP
|
||||
|
||||
dev.off()
|
||||
#================================
|
||||
# Sensitivity mutation numbers: geom_tile
|
||||
#================================
|
||||
sensCLP = paste0(outdir_images
|
||||
,tolower(gene)
|
||||
,"_sensN_tile.png")
|
||||
|
||||
print(paste0("plot filename:", sensCLP))
|
||||
png(sensCLP, units = "in", width = 1, height = 1, res = 300 )
|
||||
sensP
|
||||
dev.off()
|
||||
#================================
|
||||
# Sensitivity SITE numbers: geom_tile
|
||||
#================================
|
||||
sens_siteCLP = paste0(outdir_images
|
||||
,tolower(gene)
|
||||
,"_sens_siteC_tile.png")
|
||||
|
||||
print(paste0("plot filename:", sens_siteCLP))
|
||||
png(sens_siteCLP, units = "in", width = 1.2, height = 1, res = 300 )
|
||||
sens_siteP
|
||||
dev.off()
|
||||
|
||||
###########################################################
|
||||
|
||||
179
scripts/plotting/plotting_thesis/katg/dm_om_plots_layout_katg.R
Normal file
179
scripts/plotting/plotting_thesis/katg/dm_om_plots_layout_katg.R
Normal file
|
|
@ -0,0 +1,179 @@
|
|||
# source dm_om_plots.R
|
||||
source("/home/tanu/git/LSHTM_analysis/scripts/plotting/plotting_thesis/dm_om_plots.R")
|
||||
|
||||
##### plots to combine ####
|
||||
duetP
|
||||
foldxP
|
||||
deepddgP
|
||||
dynamut2P
|
||||
genomicsP
|
||||
consurfP
|
||||
proveanP
|
||||
snap2P
|
||||
mcsmligP
|
||||
mcsmlig2P
|
||||
mcsmppi2P
|
||||
mcsmnaP
|
||||
|
||||
# Plot labels
|
||||
tit1 = "Stability changes"
|
||||
tit2 = "Genomic measure"
|
||||
tit3 = "Distance to partners"
|
||||
tit4 = "Evolutionary Conservation"
|
||||
tit5 = "Affinity changes"
|
||||
pt_size = 30
|
||||
|
||||
theme_georgia <- function(...) {
|
||||
theme_gray(base_family = "sans", ...) +
|
||||
theme(plot.title = element_text(face = "bold"))
|
||||
}
|
||||
|
||||
|
||||
title_theme <- calc_element("plot.title", theme_georgia())
|
||||
|
||||
pt1 = ggdraw() +
|
||||
draw_label(
|
||||
tit1,
|
||||
fontfamily = title_theme$family,
|
||||
fontface = title_theme$face,
|
||||
#size = title_theme$size
|
||||
size = pt_size
|
||||
)
|
||||
|
||||
pt2 = ggdraw() +
|
||||
draw_label(
|
||||
tit2,
|
||||
fontfamily = title_theme$family,
|
||||
fontface = title_theme$face,
|
||||
size = pt_size
|
||||
)
|
||||
|
||||
pt3 = ggdraw() +
|
||||
draw_label(
|
||||
tit3,
|
||||
fontfamily = title_theme$family,
|
||||
fontface = title_theme$face,
|
||||
size = pt_size
|
||||
)
|
||||
|
||||
pt4 = ggdraw() +
|
||||
draw_label(
|
||||
tit4,
|
||||
fontfamily = title_theme$family,
|
||||
fontface = title_theme$face,
|
||||
size = pt_size
|
||||
)
|
||||
|
||||
|
||||
pt5 = ggdraw() +
|
||||
draw_label(
|
||||
tit5,
|
||||
fontfamily = title_theme$family,
|
||||
fontface = title_theme$face,
|
||||
size = pt_size
|
||||
)
|
||||
|
||||
#======================
|
||||
# Output plot function
|
||||
#======================
|
||||
OutPlot_dm_om = function(x){
|
||||
|
||||
# dist b/w plot title and plot
|
||||
relH_tp = c(0.08, 0.92)
|
||||
|
||||
my_label_size = 25
|
||||
#----------------
|
||||
# Top panel
|
||||
#----------------
|
||||
top_panel = cowplot::plot_grid(
|
||||
cowplot::plot_grid(pt1,
|
||||
cowplot::plot_grid(duetP, foldxP, deepddgP, dynamut2P
|
||||
, nrow = 1
|
||||
, labels = c("A", "B", "C", "D")
|
||||
, label_size = my_label_size)
|
||||
, ncol = 1
|
||||
, rel_heights = relH_tp
|
||||
),
|
||||
NULL,
|
||||
cowplot::plot_grid(pt2,
|
||||
cowplot::plot_grid(genomicsP
|
||||
, nrow = 1
|
||||
, labels = c("E")
|
||||
, label_size = my_label_size)
|
||||
, ncol = 1
|
||||
, rel_heights = relH_tp
|
||||
),
|
||||
NULL,
|
||||
cowplot::plot_grid(pt3,
|
||||
cowplot::plot_grid( #distanceP
|
||||
distanceP_lig
|
||||
#, distanceP_ppi2
|
||||
, distanceP_na
|
||||
, nrow = 1
|
||||
, labels = c("F", "G")
|
||||
, label_size = my_label_size)
|
||||
, ncol = 1
|
||||
, rel_heights = relH_tp
|
||||
),
|
||||
nrow = 1,
|
||||
rel_widths = c(2/7, 0.1/7, 0.5/7, 0.1/7, 1/7)
|
||||
)
|
||||
|
||||
#----------------
|
||||
# Bottom panel
|
||||
#----------------
|
||||
bottom_panel = cowplot::plot_grid(
|
||||
cowplot::plot_grid(pt4,
|
||||
cowplot::plot_grid(consurfP, proveanP, snap2P
|
||||
, nrow = 1
|
||||
, labels = c("H", "I", "J")
|
||||
, label_size = my_label_size)
|
||||
, ncol = 1
|
||||
, rel_heights =relH_tp
|
||||
),NULL,
|
||||
cowplot::plot_grid(pt5,
|
||||
cowplot::plot_grid(mcsmligP
|
||||
, mcsmlig2P
|
||||
, mcsmppi2P
|
||||
, mcsmnaP
|
||||
, nrow = 1
|
||||
, labels = c("K", "L", "M")
|
||||
, label_size = my_label_size)
|
||||
, ncol = 1
|
||||
, rel_heights = relH_tp
|
||||
),NULL,
|
||||
nrow = 1,
|
||||
rel_widths = c(3/6,0.1/6,3/6, 0.1/6 )
|
||||
)
|
||||
|
||||
#-------------------------------
|
||||
# combine: Top and Bottom panel
|
||||
#-------------------------------
|
||||
cowplot::plot_grid (top_panel, bottom_panel
|
||||
, nrow =2
|
||||
, rel_widths = c(1, 1)
|
||||
, align = "hv")
|
||||
}
|
||||
|
||||
#=====================
|
||||
# OutPlot: svg and png
|
||||
#======================
|
||||
dm_om_combinedP = paste0(outdir_images
|
||||
,tolower(gene)
|
||||
,"_dm_om_all.svg")
|
||||
|
||||
cat("DM OM plots with stats:", dm_om_combinedP)
|
||||
svg(dm_om_combinedP, width = 32, height = 18)
|
||||
|
||||
OutPlot_dm_om()
|
||||
dev.off()
|
||||
|
||||
|
||||
dm_om_combinedP_png = paste0(outdir_images
|
||||
,tolower(gene)
|
||||
,"_dm_om_all.png")
|
||||
cat("DM OM plots with stats:", dm_om_combinedP_png)
|
||||
png(dm_om_combinedP_png, width = 32, height = 18, units = "in", res = 300)
|
||||
|
||||
OutPlot_dm_om()
|
||||
dev.off()
|
||||
203
scripts/plotting/plotting_thesis/katg/gg_pairs_all_katg.R
Normal file
203
scripts/plotting/plotting_thesis/katg/gg_pairs_all_katg.R
Normal file
|
|
@ -0,0 +1,203 @@
|
|||
#source("~/git/LSHTM_analysis/config/embb.R")
|
||||
#source("~/git/LSHTM_analysis/scripts/plotting/plotting_colnames.R")
|
||||
#source("~/git/LSHTM_analysis/scripts/plotting/get_plotting_dfs.R")
|
||||
|
||||
my_gg_pairs=function(plot_df, plot_title
|
||||
, tt_args_size = 2.5
|
||||
, gp_args_size = 2.5){
|
||||
ggpairs(plot_df,
|
||||
columns = 1:(ncol(plot_df)-1),
|
||||
upper = list(
|
||||
continuous = wrap('cor', # ggally_cor()
|
||||
method = "spearman",
|
||||
use = "pairwise.complete.obs",
|
||||
title="ρ",
|
||||
digits=2,
|
||||
justify_labels = "centre",
|
||||
title_args=list(size=tt_args_size, colour="black"),#2.5
|
||||
group_args=list(size=gp_args_size)#2.5
|
||||
)
|
||||
),
|
||||
lower = list(
|
||||
continuous = wrap("points",
|
||||
alpha = 0.7,
|
||||
size=0.125),
|
||||
combo = wrap("dot",
|
||||
alpha = 0.7,
|
||||
size=0.125)
|
||||
),
|
||||
aes(colour = factor(ifelse(dst_mode==0,
|
||||
"S",
|
||||
"R") ),
|
||||
alpha = 0.5),
|
||||
title=plot_title) +
|
||||
|
||||
scale_colour_manual(values = c("red", "blue")) +
|
||||
scale_fill_manual(values = c("red", "blue")) #+
|
||||
# theme(text = element_text(size=7,
|
||||
# face="bold"))
|
||||
}
|
||||
|
||||
DistCutOff = 10
|
||||
###########################################################################
|
||||
geneL_normal = c("pnca")
|
||||
geneL_na = c("gid", "rpob")
|
||||
geneL_ppi2 = c("alr", "embb", "katg", "rpob")
|
||||
|
||||
merged_df3 = as.data.frame(merged_df3)
|
||||
|
||||
corr_plotdf = corr_data_extract(merged_df3
|
||||
, gene = gene
|
||||
, drug = drug
|
||||
, extract_scaled_cols = F)
|
||||
|
||||
aff_dist_cols = colnames(corr_plotdf)[grep("Dist", colnames(corr_plotdf))]
|
||||
static_cols = c("Log10(MAF)"
|
||||
, "Log10(OR)"
|
||||
)
|
||||
############################################################
|
||||
#=============================================
|
||||
# Creating masked df for affinity data
|
||||
#=============================================
|
||||
corr_affinity_df = corr_plotdf
|
||||
|
||||
#----------------------
|
||||
# Mask affinity columns
|
||||
#-----------------------
|
||||
corr_affinity_df[corr_affinity_df["Lig-Dist"]>DistCutOff,"mCSM-lig"]=0
|
||||
corr_affinity_df[corr_affinity_df["Lig-Dist"]>DistCutOff,"mmCSM-lig"]=0
|
||||
|
||||
if (tolower(gene)%in%geneL_ppi2){
|
||||
corr_affinity_df[corr_affinity_df["PPI-Dist"]>DistCutOff,"mCSM-PPI2"]=0
|
||||
}
|
||||
|
||||
if (tolower(gene)%in%geneL_na){
|
||||
corr_affinity_df[corr_affinity_df["NCA-Dist"]>DistCutOff,"mCSM-NA"]=0
|
||||
}
|
||||
|
||||
# count 0
|
||||
#res <- colSums(corr_affinity_df==0)/nrow(corr_affinity_df)*100
|
||||
unmasked_vals <- nrow(corr_affinity_df) - colSums(corr_affinity_df==0)
|
||||
unmasked_vals
|
||||
|
||||
##########################################################
|
||||
#================
|
||||
# Stability
|
||||
#================
|
||||
corr_ps_colnames = c(static_cols
|
||||
, "DUET"
|
||||
, "FoldX"
|
||||
, "DeepDDG"
|
||||
, "Dynamut2"
|
||||
, aff_dist_cols
|
||||
, "dst_mode")
|
||||
|
||||
corr_df_ps = corr_plotdf[, corr_ps_colnames]
|
||||
|
||||
# Plot #1
|
||||
plot_corr_df_ps = my_gg_pairs(corr_df_ps, plot_title="Stability estimates")
|
||||
|
||||
##########################################################
|
||||
#================
|
||||
# Conservation
|
||||
#================
|
||||
corr_conservation_cols = c( static_cols
|
||||
, "ConSurf"
|
||||
, "SNAP2"
|
||||
, "PROVEAN"
|
||||
#, aff_dist_cols
|
||||
, "dst_mode"
|
||||
)
|
||||
|
||||
corr_df_cons = corr_plotdf[, corr_conservation_cols]
|
||||
|
||||
# Plot #2
|
||||
plot_corr_df_cons = my_gg_pairs(corr_df_cons, plot_title="Conservation estimates")
|
||||
|
||||
##########################################################
|
||||
#================
|
||||
# Affinity: lig, ppi and na as applicable
|
||||
#================
|
||||
#corr_df_lig = corr_plotdf[corr_plotdf["Lig-Dist"]<DistCutOff,]
|
||||
common_aff_colnames = c("mCSM-lig"
|
||||
, "mmCSM-lig")
|
||||
|
||||
if (tolower(gene)%in%geneL_normal){
|
||||
aff_colnames = common_aff_colnames
|
||||
}
|
||||
if (tolower(gene)%in%geneL_ppi2){
|
||||
aff_colnames = c(common_aff_colnames, "mCSM-PPI2")
|
||||
}
|
||||
|
||||
if (tolower(gene)%in%geneL_na){
|
||||
aff_colnames = c(common_aff_colnames, "mCSM-NA")
|
||||
}
|
||||
|
||||
# building ffinal affinity colnames for correlation
|
||||
corr_aff_colnames = c(static_cols
|
||||
, aff_colnames
|
||||
, "dst_mode") # imp
|
||||
|
||||
corr_df_aff = corr_affinity_df[, corr_aff_colnames]
|
||||
colnames(corr_df_aff)
|
||||
|
||||
# Plot #3
|
||||
plot_corr_df_aff = my_gg_pairs(corr_df_aff
|
||||
, plot_title="Affinity estimates"
|
||||
#, tt_args_size = 4
|
||||
#, gp_args_size = 4
|
||||
)
|
||||
|
||||
#### Combine plots #####
|
||||
# #png("/home/tanu/tmp/gg_pairs_all.png", height = 6, width=11.75, unit="in",res=300)
|
||||
# png(paste0(outdir_images
|
||||
# ,tolower(gene)
|
||||
# ,"_CorrAB.png"), height = 6, width=11.75, unit="in",res=300)
|
||||
#
|
||||
# cowplot::plot_grid(ggmatrix_gtable(plot_corr_df_ps),
|
||||
# ggmatrix_gtable(plot_corr_df_cons),
|
||||
# # ggmatrix_gtable(plot_corr_df_aff),
|
||||
# # nrow=1, ncol=3, rel_heights = 7,7,3
|
||||
# nrow=1,
|
||||
# #rel_heights = 1,1
|
||||
# labels = "AUTO",
|
||||
# label_size = 12)
|
||||
# dev.off()
|
||||
#
|
||||
# # affinity corr
|
||||
# #png("/home/tanu/tmp/gg_pairs_affinity.png", height =7, width=7, unit="in",res=300)
|
||||
# png(paste0(outdir_images
|
||||
# ,tolower(gene)
|
||||
# ,"_CorrC.png"), height =7, width=7, unit="in",res=300)
|
||||
#
|
||||
# cowplot::plot_grid(ggmatrix_gtable(plot_corr_df_aff),
|
||||
# labels = "C",
|
||||
# label_size = 12)
|
||||
# dev.off()
|
||||
|
||||
#### Combine A ####
|
||||
png(paste0(outdir_images
|
||||
,tolower(gene)
|
||||
,"_CorrA.png"), height =8, width=8, unit="in",res=300)
|
||||
|
||||
cowplot::plot_grid(ggmatrix_gtable(plot_corr_df_ps),
|
||||
labels = "A",
|
||||
label_size = 12)
|
||||
dev.off()
|
||||
|
||||
#### Combine B+C ####
|
||||
# B + C
|
||||
png(paste0(outdir_images
|
||||
,tolower(gene)
|
||||
,"_CorrBC.png"), height = 6, width=11.75, unit="in",res=300)
|
||||
|
||||
cowplot::plot_grid(ggmatrix_gtable(plot_corr_df_cons),
|
||||
ggmatrix_gtable(plot_corr_df_aff),
|
||||
# ggmatrix_gtable(plot_corr_df_aff),
|
||||
# nrow=1, ncol=3, rel_heights = 7,7,3
|
||||
nrow=1,
|
||||
#rel_heights = 1,1
|
||||
labels = c("B", "C"),
|
||||
label_size = 12)
|
||||
dev.off()
|
||||
|
||||
179
scripts/plotting/plotting_thesis/katg/pe_sens_site_count_katg.R
Normal file
179
scripts/plotting/plotting_thesis/katg/pe_sens_site_count_katg.R
Normal file
|
|
@ -0,0 +1,179 @@
|
|||
source("/home/tanu/git/LSHTM_analysis/scripts/plotting/plotting_thesis/katg/prominent_effects_katg.R")
|
||||
source("/home/tanu/git/LSHTM_analysis/scripts/plotting/plotting_thesis/katg/sensitivity_count_katg.R")
|
||||
|
||||
##############################################################
|
||||
# PE count
|
||||
#lig-- na--ppi2--stab
|
||||
# pe_colour_map = c("DD_lig" = "#ffd700" # gold
|
||||
# , "SS_lig" = "#f0e68c" # khaki
|
||||
#
|
||||
# , "DD_nucleic_acid"= "#a0522d" # sienna
|
||||
# , "SS_nucleic_acid"= "#d2b48c" # tan
|
||||
#
|
||||
# , "DD_ppi2" = "#da70d6" # orchid
|
||||
# , "SS_ppi2" = "#ff1493" # deeppink
|
||||
#
|
||||
# , "DD_stability" = "#f8766d" # red
|
||||
# , "SS_stability" = "#00BFC4") # blue
|
||||
table(str_df_plot_cols$pe_effect_outcome)
|
||||
##############################################################
|
||||
#===========
|
||||
#PE count
|
||||
#===========
|
||||
rects <- data.frame(x=1:6,
|
||||
colors = c("#ffd700" ,
|
||||
"#f0e68c" ,
|
||||
|
||||
#"#a0522d" ,
|
||||
#"#d2b48c" ,
|
||||
|
||||
"#da70d6" ,
|
||||
"#ff1493" ,
|
||||
|
||||
"#f8766d" ,
|
||||
"#00BFC4")
|
||||
)
|
||||
|
||||
rects$text = c("-ve Lig"
|
||||
, "+ve Lig"
|
||||
|
||||
#, "-ve\nNuc.Acid"
|
||||
#, "+ve\nNuc.Acid"
|
||||
|
||||
, "-ve PPI2"
|
||||
, "+ve PPI2"
|
||||
|
||||
, "-ve stability"
|
||||
, "+ve stability"
|
||||
)
|
||||
|
||||
cell1 = table(str_df_plot_cols$pe_effect_outcome)[["DD_lig"]]
|
||||
cell2 = 0
|
||||
|
||||
#cell3 = table(str_df_plot_cols$pe_effect_outcome)[["DD_nucleic_acid"]]
|
||||
#cell4 = table(str_df_plot_cols$pe_effect_outcome)[["SS_nucleic_acid"]]
|
||||
|
||||
cell5 = table(str_df_plot_cols$pe_effect_outcome)[["DD_ppi2"]]
|
||||
cell6 = table(str_df_plot_cols$pe_effect_outcome)[["SS_ppi2"]]
|
||||
|
||||
cell7 = table(str_df_plot_cols$pe_effect_outcome)[["DD_stability"]]
|
||||
cell8 = table(str_df_plot_cols$pe_effect_outcome)[["SS_stability"]]
|
||||
|
||||
|
||||
#rects$numbers = c(38, 0, 22, 9, 108, 681) #for embb
|
||||
rects$numbers = c(cell1, cell2,
|
||||
#cell3, cell4,
|
||||
cell5, cell6,
|
||||
cell7, cell8)
|
||||
|
||||
rects$num_labels = paste0("n=", rects$numbers)
|
||||
|
||||
rects
|
||||
#------
|
||||
# Plot
|
||||
#------
|
||||
#https://stackoverflow.com/questions/47986055/create-a-rectangle-filled-with-text
|
||||
peP = ggplot(rects, aes(x, y = 0, fill = colors, label = paste0(text,"\n", num_labels))) +
|
||||
geom_tile(width = 1, height = 1) + # make square tiles
|
||||
geom_text(color = "black", size = 1.7) + # add white text in the middle
|
||||
scale_fill_identity(guide = "none") + # color the tiles with the colors in the data frame
|
||||
coord_fixed() + # make sure tiles are square
|
||||
coord_flip()+ scale_x_reverse() +
|
||||
# theme_void() # remove any axis markings
|
||||
theme_nothing() # remove any axis markings
|
||||
peP
|
||||
|
||||
#------
|
||||
# Plot: this one is better
|
||||
#------
|
||||
peP2 = ggplot(rects, aes(x, y = 0, fill = colors, label = paste0(text,"\n", num_labels))) +
|
||||
geom_tile() + # make square tiles
|
||||
geom_text(color = "black", size = 1.6) + # add white text in the middle
|
||||
scale_fill_identity(guide = "none") + # color the tiles with the colors in the data frame
|
||||
coord_fixed() + # make sure tiles are square
|
||||
theme_nothing() # remove any axis markings
|
||||
peP2
|
||||
|
||||
########################################################
|
||||
# From: script sensitivity_count per gene
|
||||
#===============================
|
||||
# Sensitivity count: SITE
|
||||
#===============================
|
||||
#--------
|
||||
# embb
|
||||
#--------
|
||||
#rsc = 54
|
||||
#ccc = 46
|
||||
#ssc = 470
|
||||
|
||||
rsc = site_Rc; rsc
|
||||
ccc = site_Cc; ccc
|
||||
ssc = site_Sc; ssc
|
||||
|
||||
rect_rs_siteC <- data.frame(x=1:3,
|
||||
colors = c("red",
|
||||
"purple",
|
||||
"blue")
|
||||
)
|
||||
|
||||
rect_rs_siteC
|
||||
rect_rs_siteC$text = c("Resistant",
|
||||
"Common",
|
||||
"Sensitive")
|
||||
|
||||
rect_rs_siteC$numbers = c(rsc,ccc,ssc)
|
||||
rect_rs_siteC$num_labels = paste0("n=", rect_rs_siteC$numbers)
|
||||
rect_rs_siteC
|
||||
|
||||
#------
|
||||
# Plot
|
||||
#------
|
||||
sens_siteP = ggplot(rect_rs_siteC, aes(x, y = 0,
|
||||
fill = colors,
|
||||
label = num_labels
|
||||
#,label = paste0(text,"\n", num_labels)
|
||||
)) +
|
||||
geom_tile(width = 1, height = 1) +
|
||||
#geom_text(color = "black", size = 1.7) +
|
||||
geom_label(color = "black", size = 1.7,fill = "white", alpha=0.7) +
|
||||
scale_fill_identity(guide = "none") +
|
||||
coord_fixed()+
|
||||
theme_nothing() # remove any axis markings
|
||||
sens_siteP
|
||||
|
||||
################################################################
|
||||
#===============================
|
||||
# Sensitivity count: Mutations
|
||||
#===============================
|
||||
table(sensP_df$sensitivity)
|
||||
muts_Rc = table(sensP_df$sensitivity)[["R"]]
|
||||
muts_Sc = table(sensP_df$sensitivity)[["S"]]
|
||||
rect_sens <- data.frame(x=1:2,
|
||||
colors = c("red",
|
||||
"blue")
|
||||
)
|
||||
|
||||
rect_sens$text = c("Resistant",
|
||||
"Sensitive")
|
||||
rect_sens$numbers = c(muts_Rc,muts_Sc)
|
||||
rect_sens$num_labels = paste0("n=", rect_sens$numbers)
|
||||
rect_sens
|
||||
#------
|
||||
# Plot
|
||||
#------
|
||||
sensP = ggplot(rect_sens, aes(x, y = 0,
|
||||
fill = colors,
|
||||
label = paste0(text,"\n", num_labels))) +
|
||||
geom_tile(width = 1, height = 1) +
|
||||
#geom_text(color = "black", size = 1.7) +
|
||||
geom_label(color = "black", size = 1.7,fill = "white", alpha=0.7) +
|
||||
scale_fill_identity(guide = "none") +
|
||||
coord_fixed()+
|
||||
theme_nothing() # remove any axis markings
|
||||
sensP
|
||||
|
||||
sensP2 = sensP +
|
||||
coord_flip() + scale_x_reverse()
|
||||
sensP2
|
||||
|
||||
|
||||
331
scripts/plotting/plotting_thesis/katg/prominent_effects_katg.R
Normal file
331
scripts/plotting/plotting_thesis/katg/prominent_effects_katg.R
Normal file
|
|
@ -0,0 +1,331 @@
|
|||
########################################################
|
||||
pos_colname = "position"
|
||||
|
||||
#-------------
|
||||
# from ~/git/LSHTM_analysis/scripts/plotting/plotting_colnames.R
|
||||
#-------------
|
||||
length(all_stability_cols); length(raw_stability_cols)
|
||||
length(scaled_stability_cols); length(outcome_stability_cols)
|
||||
length(affinity_dist_colnames)
|
||||
|
||||
|
||||
static_cols = c("mutationinformation",
|
||||
#"position",
|
||||
pos_colname,
|
||||
"sensitivity")
|
||||
|
||||
other_cols_all = c(scaled_stability_cols, scaled_affinity_cols, affinity_dist_colnames)
|
||||
|
||||
#omit avg cols and foldx_scaled_signC cols
|
||||
other_cols = other_cols_all[grep("avg", other_cols_all, invert = T)]
|
||||
other_cols = other_cols[grep("foldx_scaled_signC",other_cols, invert = T )]
|
||||
other_cols
|
||||
|
||||
cols_to_extract = c(static_cols, other_cols)
|
||||
cat("\nExtracting cols:", cols_to_extract)
|
||||
expected_ncols = length(static_cols) + length(other_cols)
|
||||
expected_ncols
|
||||
|
||||
str_df = merged_df3[, cols_to_extract]
|
||||
|
||||
if (ncol(str_df) == expected_ncols){
|
||||
cat("\nPASS: successfully extracted cols for calculating prominent effects")
|
||||
}else{
|
||||
stop("\nAbort: Could not extract cols for calculating prominent effects")
|
||||
}
|
||||
|
||||
#=========================
|
||||
# Masking affinity columns
|
||||
#=========================
|
||||
# First make values for affinity cols 0 when their corresponding dist >10
|
||||
head(str_df)
|
||||
|
||||
# replace in place affinity values >10
|
||||
str_df[str_df["ligand_distance"]>10,"affinity_scaled"]=0
|
||||
str_df[str_df["ligand_distance"]>10,"mmcsm_lig_scaled"]=0
|
||||
|
||||
#ppi2 gene: replace in place ppi2 affinity values where ppi2 dist >10
|
||||
if (tolower(gene)%in%geneL_ppi2){
|
||||
str_df[str_df["interface_dist"]>10,"mcsm_ppi2_scaled"]=0
|
||||
}
|
||||
|
||||
# na gene: replace in place na affinity values where na dist >10
|
||||
if (tolower(gene)%in%geneL_na){
|
||||
str_df[str_df["nca_distance"]>10,"mcsm_na_scaled"]=0
|
||||
}
|
||||
|
||||
colnames(str_df)
|
||||
head(str_df)
|
||||
|
||||
scaled_cols_tc = other_cols[grep("scaled", other_cols)]
|
||||
|
||||
|
||||
################################################
|
||||
#===============
|
||||
# whole df
|
||||
#===============
|
||||
give_col=function(x,y,df=str_df){
|
||||
df[df[[pos_colname]]==x,y]
|
||||
}
|
||||
|
||||
for (i in unique(str_df[[pos_colname]]) ){
|
||||
print(i)
|
||||
#cat(length(unique(str_df[[pos_colname]])))
|
||||
|
||||
biggest = max(abs(give_col(i,scaled_cols_tc)))
|
||||
|
||||
str_df[str_df[[pos_colname]]==i,'abs_max_effect'] = biggest
|
||||
str_df[str_df[[pos_colname]]==i,'effect_type']= names(
|
||||
give_col(i,scaled_cols_tc)[which(
|
||||
abs(
|
||||
give_col(i,scaled_cols_tc)
|
||||
) == biggest, arr.ind=T
|
||||
)[, "col"]])[1]
|
||||
|
||||
effect_name = unique(str_df[str_df[[pos_colname]]==i,'effect_type'])#[1] # pick first one in case we have multiple exact values
|
||||
|
||||
# get index/rowname for value of max effect, and then use it to get the original sign
|
||||
# here
|
||||
#ind = rownames(which(abs(str_df[str_df[[pos_colname]]==i,c('position',effect_name)][effect_name])== biggest, arr.ind=T))
|
||||
ind = rownames(which(abs(str_df[str_df[[pos_colname]]==i,c(pos_colname,effect_name)][effect_name])== biggest, arr.ind=T))
|
||||
|
||||
str_df[str_df[[pos_colname]]==i,'effect_sign'] = sign(str_df[effect_name][ind,])[1]
|
||||
}
|
||||
|
||||
# ends with suffix 2 if dups
|
||||
str_df$effect_type = sub("\\.[0-9]+", "", str_df$effect_type) # cull duplicate effect types that happen when there are exact duplicate values
|
||||
colnames(str_df)
|
||||
table(str_df$effect_type)
|
||||
|
||||
# check
|
||||
str_df_check = str_df[str_df[[pos_colname]]%in%c(24, 32, 160, 303, 334),]
|
||||
|
||||
#================
|
||||
# for Plots
|
||||
#================
|
||||
str_df_short = str_df[, c("mutationinformation",
|
||||
#"position",
|
||||
pos_colname,
|
||||
"sensitivity"
|
||||
, "effect_type"
|
||||
, "effect_sign")]
|
||||
|
||||
table(str_df_short$effect_type)
|
||||
table(str_df_short$effect_sign)
|
||||
str(str_df_short)
|
||||
|
||||
# assign pe outcome
|
||||
str_df_short$pe_outcome = ifelse(str_df_short$effect_sign<0, "DD", "SS")
|
||||
table(str_df_short$pe_outcome )
|
||||
table(str_df_short$effect_sign)
|
||||
|
||||
#==============
|
||||
# group effect type:
|
||||
# lig, ppi2, nuc. acid, stability
|
||||
#==============
|
||||
affcols = c("affinity_scaled", "mmcsm_lig_scaled")
|
||||
ppi2_cols = c("mcsm_ppi2_scaled")
|
||||
|
||||
#lig
|
||||
table(str_df_short$effect_type)
|
||||
str_df_short$effect_grouped = ifelse(str_df_short$effect_type%in%affcols
|
||||
, "lig"
|
||||
, str_df_short$effect_type)
|
||||
table(str_df_short$effect_grouped)
|
||||
|
||||
#ppi2
|
||||
str_df_short$effect_grouped = ifelse(str_df_short$effect_grouped%in%ppi2_cols
|
||||
, "ppi2"
|
||||
, str_df_short$effect_grouped)
|
||||
table(str_df_short$effect_grouped)
|
||||
|
||||
|
||||
#stability
|
||||
str_df_short$effect_grouped = ifelse(!str_df_short$effect_grouped%in%c("lig",
|
||||
"ppi2"
|
||||
)
|
||||
, "stability"
|
||||
, str_df_short$effect_grouped)
|
||||
|
||||
table(str_df_short$effect_grouped)
|
||||
|
||||
# create a sign as well
|
||||
str_df_short$pe_effect_outcome = paste0(str_df_short$pe_outcome, "_"
|
||||
, str_df_short$effect_grouped)
|
||||
|
||||
table(str_df_short$pe_effect_outcome)
|
||||
|
||||
#####################################################################
|
||||
# Chimera: for colouring
|
||||
####################################################################
|
||||
|
||||
#-------------------------------------
|
||||
# get df with unique position
|
||||
#--------------------------------------
|
||||
#data[!duplicated(data$x), ]
|
||||
str_df_plot = str_df_short[!duplicated(str_df[[pos_colname]]),]
|
||||
|
||||
if (nrow(str_df_plot) == length(unique(str_df[[pos_colname]]))){
|
||||
cat("\nPASS: successfully extracted df with unique positions")
|
||||
}else{
|
||||
stop("\nAbort: Could not extract df with unique positions")
|
||||
}
|
||||
|
||||
#-------------------------------------
|
||||
# generate colours for effect types
|
||||
#--------------------------------------
|
||||
str_df_plot_cols = str_df_plot[, c(pos_colname,
|
||||
"sensitivity",
|
||||
"pe_outcome",
|
||||
"effect_grouped",
|
||||
"pe_effect_outcome")]
|
||||
head(str_df_plot_cols)
|
||||
|
||||
# colour intensity based on sign
|
||||
#str_df_plot_cols$colour_hue = ifelse(str_df_plot_cols$effect_sign<0, "bright", "light")
|
||||
str_df_plot_cols$colour_hue = ifelse(str_df_plot_cols$pe_outcome=="DD", "bright", "light")
|
||||
|
||||
table(str_df_plot_cols$colour_hue); table(str_df_plot$pe_outcome)
|
||||
head(str_df_plot_cols)
|
||||
|
||||
# colour based on effect
|
||||
table(str_df_plot_cols$pe_effect_outcome)
|
||||
|
||||
# colors = c("#ffd700" #gold
|
||||
# , "#f0e68c" #khaki
|
||||
# , "#da70d6"# orchid
|
||||
# , "#ff1493"# deeppink
|
||||
# , "#a0522d" #sienna
|
||||
# , "#d2b48c" # tan
|
||||
# , "#00BFC4" #, "#007d85" #blue
|
||||
# , "#F8766D" )# red
|
||||
|
||||
pe_colour_map = c("DD_lig" = "#ffd700" # gold
|
||||
, "SS_lig" = "#f0e68c" # khaki
|
||||
|
||||
, "DD_nucleic_acid"= "#a0522d" # sienna
|
||||
, "SS_nucleic_acid"= "#d2b48c" # tan
|
||||
|
||||
, "DD_ppi2" = "#da70d6" # orchid
|
||||
, "SS_ppi2" = "#ff1493" # deeppink
|
||||
|
||||
, "DD_stability" = "#f8766d" # red
|
||||
, "SS_stability" = "#00BFC4") # blue
|
||||
|
||||
#unlist(d[c('a', 'a', 'c', 'b')], use.names=FALSE)
|
||||
|
||||
#map the colours
|
||||
str_df_plot_cols$colour_map= unlist(map(str_df_plot_cols$pe_effect_outcome
|
||||
,function(x){pe_colour_map[[x]]}
|
||||
))
|
||||
head(str_df_plot_cols$colour_map)
|
||||
table(str_df_plot_cols$colour_map)
|
||||
table(str_df_plot_cols$pe_effect_outcome)
|
||||
|
||||
# str_df_plot_cols$colours = paste0(str_df_plot_cols$colour_hue
|
||||
# , "_"
|
||||
# , str_df_plot_cols$colour_map)
|
||||
# head(str_df_plot_cols$colours)
|
||||
# table(str_df_plot_cols$colours)
|
||||
#
|
||||
#
|
||||
# class(str_df_plot_cols$colour_map)
|
||||
# str(str_df_plot_cols)
|
||||
|
||||
# sort by colour
|
||||
head(str_df_plot_cols)
|
||||
str_df_plot_cols = str_df_plot_cols[order(str_df_plot_cols$colour_map), ]
|
||||
head(str_df_plot_cols)
|
||||
|
||||
#======================================
|
||||
# write file with prominent effects
|
||||
#======================================
|
||||
outdir_images = paste0("~/git/Writing/thesis/images/results/", tolower(gene), "/")
|
||||
write.csv(str_df_plot_cols, paste0(outdir_images, tolower(gene), "_prominent_effects.csv"))
|
||||
|
||||
################################
|
||||
# printing for chimera
|
||||
###############################
|
||||
chain_suffix = ".A"
|
||||
str_df_plot_cols$pos_chain = paste0(str_df_plot_cols[[pos_colname]], chain_suffix)
|
||||
table(str_df_plot_cols$colour_map)
|
||||
table(str_df_plot_cols$pe_effect_outcome)
|
||||
|
||||
#===================================================
|
||||
#-------------------
|
||||
# Ligand Affinity
|
||||
#-------------------
|
||||
# -ve Lig Aff
|
||||
dd_lig = str_df_plot_cols[str_df_plot_cols$pe_effect_outcome=="DD_lig",]
|
||||
if (nrow(dd_lig) == table(str_df_plot_cols$pe_effect_outcome)[['DD_lig']]){
|
||||
dd_lig_pos = dd_lig[[pos_colname]]
|
||||
}else{
|
||||
stop("Abort: DD affinity colour numbers mismtatch")
|
||||
}
|
||||
|
||||
# +ve Lig Aff
|
||||
ss_lig = str_df_plot_cols[str_df_plot_cols$pe_effect_outcome=="SS_lig",]
|
||||
if (!empty(ss_lig)){
|
||||
if (nrow(ss_lig) == table(str_df_plot_cols$pe_effect_outcome)[['SS_lig']]){
|
||||
ss_lig_pos = ss_lig[[pos_colname]]
|
||||
}else{
|
||||
stop("Abort: SS affinity colour numbers mismtatch")
|
||||
}
|
||||
#put in chimera cmd
|
||||
paste0(dd_lig_pos, chain_suffix)
|
||||
paste0(ss_lig_pos, chain_suffix)
|
||||
}
|
||||
|
||||
|
||||
|
||||
|
||||
#===================================================
|
||||
#-------------------
|
||||
# PPI2 Affinity
|
||||
#-------------------
|
||||
# -ve PPI2
|
||||
dd_ppi2 = str_df_plot_cols[str_df_plot_cols$pe_effect_outcome=="DD_ppi2",]
|
||||
if (nrow(dd_ppi2) == table(str_df_plot_cols$pe_effect_outcome)[['DD_ppi2']]){
|
||||
dd_ppi2_pos = dd_ppi2[[pos_colname]]
|
||||
}else{
|
||||
stop("Abort: DD PPI2 colour numbers mismtatch")
|
||||
}
|
||||
|
||||
# +ve PPI2
|
||||
ss_ppi2 = str_df_plot_cols[str_df_plot_cols$pe_effect_outcome=="SS_ppi2",]
|
||||
if (nrow(ss_ppi2) == table(str_df_plot_cols$pe_effect_outcome)[['SS_ppi2']]){
|
||||
ss_ppi2_pos = ss_ppi2[[pos_colname]]
|
||||
}else{
|
||||
stop("Abort: SS PPI2 colour numbers mismtatch")
|
||||
}
|
||||
|
||||
#put in chimera cmd
|
||||
paste0(dd_ppi2_pos,chain_suffix)
|
||||
paste0(ss_ppi2_pos,chain_suffix)
|
||||
|
||||
#=========================================================
|
||||
#------------------------
|
||||
# Stability
|
||||
#------------------------
|
||||
# -ve Stability
|
||||
dd_stability = str_df_plot_cols[str_df_plot_cols$pe_effect_outcome=="DD_stability",]
|
||||
if (nrow(dd_stability) == table(str_df_plot_cols$pe_effect_outcome)[['DD_stability']]){
|
||||
dd_stability_pos = dd_stability[[pos_colname]]
|
||||
}else{
|
||||
stop("Abort: DD Stability colour numbers mismtatch")
|
||||
}
|
||||
|
||||
# +ve Stability
|
||||
ss_stability = str_df_plot_cols[str_df_plot_cols$pe_effect_outcome=="SS_stability",]
|
||||
if (nrow(ss_stability) == table(str_df_plot_cols$pe_effect_outcome)[['SS_stability']]){
|
||||
ss_stability_pos = ss_stability[[pos_colname]]
|
||||
}else{
|
||||
stop("Abort: SS Stability colour numbers mismtatch")
|
||||
}
|
||||
|
||||
#put in chimera cmd
|
||||
# stabiliting first as it has less numbers
|
||||
paste0(ss_stability_pos, chain_suffix)
|
||||
paste0(dd_stability_pos, chain_suffix)
|
||||
####################################################################
|
||||
|
||||
|
|
@ -0,0 +1,65 @@
|
|||
#=========================
|
||||
# Count Sensitivity
|
||||
# Mutations and positions
|
||||
#=========================
|
||||
pos_colname_c ="position"
|
||||
|
||||
sensP_df = merged_df3[,c("mutationinformation",
|
||||
#"position",
|
||||
pos_colname_c,
|
||||
"sensitivity")]
|
||||
|
||||
head(sensP_df)
|
||||
table(sensP_df$sensitivity)
|
||||
|
||||
#---------------
|
||||
# Total unique positions
|
||||
#----------------
|
||||
tot_mut_pos = length(unique(sensP_df[[pos_colname_c]]))
|
||||
cat("\nNo of Tot muts sites:", tot_mut_pos)
|
||||
|
||||
# resistant mut pos
|
||||
sens_site_allR = sensP_df[[pos_colname_c]][sensP_df$sensitivity=="R"]
|
||||
sens_site_UR = unique(sens_site_allR)
|
||||
length(sens_site_UR)
|
||||
|
||||
# Sensitive mut pos
|
||||
sens_site_allS = sensP_df[[pos_colname_c]][sensP_df$sensitivity=="S"]
|
||||
sens_site_US = unique(sens_site_allS)
|
||||
length(sens_site_UR)
|
||||
|
||||
#---------------
|
||||
# Common Sites
|
||||
#----------------
|
||||
common_pos = intersect(sens_site_UR,sens_site_US)
|
||||
site_Cc = length(common_pos)
|
||||
cat("\nNo of Common sites:", site_Cc
|
||||
, "\nThese are:", common_pos)
|
||||
|
||||
#---------------
|
||||
# Resistant muts
|
||||
#----------------
|
||||
site_R = sens_site_UR[!sens_site_UR%in%common_pos]
|
||||
site_Rc = length(site_R)
|
||||
|
||||
if ( length(sens_site_allR) == table(sensP_df$sensitivity)[['R']] ){
|
||||
cat("\nNo of R muts:", length(sens_site_allR)
|
||||
, "\nNo. of R sites:",site_Rc
|
||||
, "\nThese are:", site_R
|
||||
)
|
||||
}
|
||||
|
||||
#---------------
|
||||
# Sensitive muts
|
||||
#----------------
|
||||
site_S = sens_site_US[!sens_site_US%in%common_pos]
|
||||
site_Sc = length(site_S)
|
||||
|
||||
if ( length(sens_site_allS) == table(sensP_df$sensitivity)[['S']] ){
|
||||
cat("\nNo of S muts:", length(sens_site_allS)
|
||||
, "\nNo. of S sites:", site_Sc
|
||||
, "\nThese are:", site_S)
|
||||
}
|
||||
|
||||
#########################
|
||||
|
||||
|
|
@ -34,7 +34,12 @@ if ( all(table(nsample_lin$sensitivity)== table(nsample_lin$mutation_info_labels
|
|||
###################################################
|
||||
# Lineage barplots #
|
||||
###################################################
|
||||
|
||||
my_xats = 8 # x axis text size # were 25
|
||||
my_yats = 8# y axis text sized_lab_size
|
||||
my_xals = 8 # x axis label size
|
||||
my_yals = 8 # y axis label size
|
||||
my_lls = 8 # legend label size
|
||||
d_lab_size = 2.3
|
||||
#===============================
|
||||
# lineage sample and SNP count
|
||||
#===============================
|
||||
|
|
@ -46,13 +51,13 @@ lin_countP = lin_count_bp(lf_data = lineage_dfL[['lin_lf']]
|
|||
, bar_fill_categ = "count_categ"
|
||||
, display_label_col = "p_count"
|
||||
, bar_stat_stype = "identity"
|
||||
, d_lab_size = 8
|
||||
, d_lab_size = d_lab_size
|
||||
, d_lab_col = "black"
|
||||
, my_xats = 25 # x axis text size
|
||||
, my_yats = 25 # y axis text sized_lab_size
|
||||
, my_xals = 25 # x axis label size
|
||||
, my_yals = 25 # y axis label size
|
||||
, my_lls = 25 # legend label size
|
||||
, my_xats = my_xats # x axis text size
|
||||
, my_yats = my_yats # y axis text sized_lab_size
|
||||
, my_xals = my_xals # x axis label size
|
||||
, my_yals = my_yals # y axis label size
|
||||
, my_lls = my_lls # legend label size
|
||||
, bar_col_labels = c("SNPs", "Total Samples")
|
||||
, bar_col_values = c("grey50", "gray75")
|
||||
, bar_leg_name = ""
|
||||
|
|
@ -73,12 +78,12 @@ lin_diversityP = lin_count_bp_diversity(lf_data = lineage_dfL[['lin_wf']]
|
|||
, display_label_col = "snp_diversity_f"
|
||||
, bar_stat_stype = "identity"
|
||||
, x_lab_angle = 90
|
||||
, d_lab_size =9
|
||||
, my_xats = 25 # x axis text size
|
||||
, my_yats = 25 # y axis text size
|
||||
, my_xals = 25 # x axis label size
|
||||
, my_yals = 25 # y axis label size
|
||||
, my_lls = 25 # legend label size
|
||||
, d_lab_size = d_lab_size
|
||||
, my_xats = my_xats # x axis text size
|
||||
, my_yats = my_yats # y axis text sized_lab_size
|
||||
, my_xals = my_xals # x axis label size
|
||||
, my_yals = my_yals # y axis label size
|
||||
, my_lls = my_lls # legend label size
|
||||
, y_log10 = F
|
||||
, y_scale_percent = F
|
||||
, leg_location = "top"
|
||||
|
|
@ -86,28 +91,9 @@ lin_diversityP = lin_count_bp_diversity(lf_data = lineage_dfL[['lin_wf']]
|
|||
, bp_plot_title = "nsSNP diversity"
|
||||
, title_colour = "black" #"chocolate4"
|
||||
, subtitle_text = NULL
|
||||
, sts = 20
|
||||
, sts = 10
|
||||
, subtitle_colour = "#350E20FF")
|
||||
lin_diversityP
|
||||
#=============================================
|
||||
# Output plots: Lineage count and Diversity
|
||||
#=============================================
|
||||
# lineage_bp_CL = paste0(outdir_images
|
||||
# ,tolower(gene)
|
||||
# ,"_lineage_bp_CL_Tall.svg")
|
||||
#
|
||||
# cat("Lineage barplots:", lineage_bp_CL)
|
||||
# svg(lineage_bp_CL, width = 8, height = 15)
|
||||
#
|
||||
# cowplot::plot_grid(lin_countP, lin_diversityP
|
||||
# #, labels = c("(a)", "(b)", "(c)", "(d)")
|
||||
# , nrow = 2
|
||||
# # , ncols = 2
|
||||
# , labels = "AUTO"
|
||||
# , label_size = 25)
|
||||
#
|
||||
# dev.off()
|
||||
########################################################################
|
||||
|
||||
|
||||
###################################################
|
||||
|
|
@ -119,6 +105,13 @@ lin_diversityP
|
|||
# , "foldx_scaled"
|
||||
# , "avg_stability_scaled")
|
||||
|
||||
my_ats = 8 # x axis text size # were 25
|
||||
my_als = 8# y axis text sized_lab_size
|
||||
my_leg_ts = 8 # x axis label size
|
||||
my_leg_title = 8 # y axis label size
|
||||
my_strip_ts = 8 #
|
||||
|
||||
|
||||
my_xlabel = paste0("Average stability ", "(", stability_suffix, ")"); my_xlabel
|
||||
#plotdf = merged_df2[merged_df2$lineage%in%c("L1", "L2", "L3", "L4"),]
|
||||
|
||||
|
|
@ -133,11 +126,11 @@ linP_dm_om = lineage_distP(merged_df2
|
|||
, fill_categ_cols = c("red", "blue")
|
||||
, label_categories = c("Resistant", "Sensitive")
|
||||
, leg_label = "Mutation group"
|
||||
, my_ats = 22 # axis text size
|
||||
, my_als = 22 # axis label size
|
||||
, my_leg_ts = 22
|
||||
, my_leg_title = 22
|
||||
, my_strip_ts = 22
|
||||
, my_ats = my_ats # axis text size
|
||||
, my_als = my_als # axis label size
|
||||
, my_leg_ts = my_leg_ts
|
||||
, my_leg_title = my_leg_title
|
||||
, my_strip_ts = my_strip_ts
|
||||
, alpha = 0.56
|
||||
)
|
||||
|
||||
|
|
|
|||
|
|
@ -1,23 +1,47 @@
|
|||
#!/usr/bin/env Rscript
|
||||
|
||||
source("/home/tanu/git/LSHTM_analysis/scripts/plotting/plotting_thesis/linage_bp_dist.R")
|
||||
###########################################
|
||||
# TASK: generate plots for lineage
|
||||
# Individual plots in
|
||||
#lineage_bp_both.R
|
||||
#linage_dist_ens_stability.R
|
||||
###########################################
|
||||
my_label_size = 25
|
||||
# svg
|
||||
# my_label_size = 12
|
||||
# linPlots_combined = paste0(outdir_images
|
||||
# , tolower(gene)
|
||||
# ,"_linP_combined.svg")
|
||||
#
|
||||
# cat("\nOutput plot:", linPlots_combined)
|
||||
# svg(linPlots_combined, width = 18, height = 12)
|
||||
#
|
||||
# cowplot::plot_grid(
|
||||
# cowplot::plot_grid(lin_countP, lin_diversityP
|
||||
# , nrow = 2
|
||||
# , rel_heights = c(1.2,1)
|
||||
# , labels = "AUTO"
|
||||
# , label_size = my_label_size),
|
||||
# NULL,
|
||||
# linP_dm_om,
|
||||
# nrow = 1,
|
||||
# labels = c("", "", "C"),
|
||||
# label_size = my_label_size,
|
||||
# rel_widths = c(35, 3, 52)
|
||||
# )
|
||||
# dev.off()
|
||||
|
||||
# png
|
||||
linPlots_combined = paste0(outdir_images
|
||||
, tolower(gene)
|
||||
,"_linP_combined.svg")
|
||||
,"_linP_combined.png")
|
||||
|
||||
cat("\nOutput plot:", linPlots_combined)
|
||||
svg(linPlots_combined, width = 18, height = 12)
|
||||
png(linPlots_combined, width = 9, height = 6, units = "in" ,res = 300)
|
||||
|
||||
cowplot::plot_grid(
|
||||
cowplot::plot_grid(lin_countP, lin_diversityP
|
||||
, nrow = 2
|
||||
, rel_heights = c(1.2,1)
|
||||
, labels = "AUTO"
|
||||
, label_size = my_label_size),
|
||||
NULL,
|
||||
|
|
|
|||
78
scripts/plotting/plotting_thesis/pe_sens_site_count.R
Normal file
78
scripts/plotting/plotting_thesis/pe_sens_site_count.R
Normal file
|
|
@ -0,0 +1,78 @@
|
|||
##############################################################
|
||||
# PE count
|
||||
##############################################################
|
||||
rects <- data.frame(x = 1:6,
|
||||
colors = c("#ffd700" #gold
|
||||
, "#f0e68c" #khaki
|
||||
, "#da70d6"# orchid
|
||||
, "#ff1493"# deeppink
|
||||
, "#00BFC4" #, "#007d85" #blue
|
||||
, "#F8766D" )# red,
|
||||
)
|
||||
rects
|
||||
|
||||
rects$text = c("-ve Lig"
|
||||
, "+ve Lig"
|
||||
, "+ve PPI2"
|
||||
, "-ve PPI2"
|
||||
, "+ve stability"
|
||||
, "-ve stability")
|
||||
|
||||
# FOR EMBB ONLY
|
||||
rects$numbers = c(38, 0, 22, 9, 108, 681)
|
||||
rects$num_labels = paste0("n=", rects$numbers)
|
||||
|
||||
rects
|
||||
|
||||
#https://stackoverflow.com/questions/47986055/create-a-rectangle-filled-with-text
|
||||
|
||||
peP = ggplot(rects, aes(x, y = 0, fill = colors, label = paste0(text,"\n", num_labels))) +
|
||||
geom_tile(width = 1, height = 1) + # make square tiles
|
||||
geom_text(color = "black", size = 1.7) + # add white text in the middle
|
||||
scale_fill_identity(guide = "none") + # color the tiles with the colors in the data frame
|
||||
coord_fixed() + # make sure tiles are square
|
||||
coord_flip()+ scale_x_reverse() +
|
||||
# theme_void() # remove any axis markings
|
||||
theme_nothing() # remove any axis markings
|
||||
peP
|
||||
|
||||
peP2 = ggplot(rects, aes(x, y = 0, fill = colors, label = paste0(text,"\n", num_labels))) +
|
||||
geom_tile() + # make square tiles
|
||||
geom_text(color = "black", size = 1.6) + # add white text in the middle
|
||||
scale_fill_identity(guide = "none") + # color the tiles with the colors in the data frame
|
||||
coord_fixed() + # make sure tiles are square
|
||||
theme_nothing() # remove any axis markings
|
||||
peP2
|
||||
|
||||
|
||||
########################################################
|
||||
# MANUAL process
|
||||
#===============================
|
||||
# Sensitivity count: Site
|
||||
#==============================
|
||||
table(df3$sensitivity)
|
||||
#--------
|
||||
# embb
|
||||
#--------
|
||||
#rsc = 54
|
||||
#ccc = 46
|
||||
#ssc = 470
|
||||
|
||||
rect_rs_siteC =data.frame(mutation_class=c("A_Resistant sites"
|
||||
, "B_Common sites"
|
||||
, "C_Sensitive sites"),
|
||||
tile_colour =c("red",
|
||||
"purple",
|
||||
"blue"),
|
||||
numbers = c(rsc, ccc, ssc),
|
||||
order = c(1, 2, 3))
|
||||
|
||||
rect_rs_siteC$labels = paste0(rect_rs_siteC$mutation_class, "\nn=", rect_rs_siteC$ numbers)
|
||||
|
||||
sens_siteP = ggplot(rect_rs_siteC, aes(mutation_class, y = 0,
|
||||
fill = tile_colour,
|
||||
label = paste0("n=", numbers))) +
|
||||
geom_tile(width = 1, height = 1) +
|
||||
geom_label(color = "black", size = 1.7,fill = "white", alpha=0.7) +
|
||||
theme_nothing()
|
||||
sens_siteP
|
||||
|
|
@ -1,405 +0,0 @@
|
|||
#!/usr/bin/env Rscript
|
||||
#source("~/git/LSHTM_analysis/config/alr.R")
|
||||
source("~/git/LSHTM_analysis/config/embb.R")
|
||||
#source("~/git/LSHTM_analysis/config/katg.R")
|
||||
#source("~/git/LSHTM_analysis/config/gid.R")
|
||||
#source("~/git/LSHTM_analysis/config/pnca.R")
|
||||
#source("~/git/LSHTM_analysis/config/rpob.R")
|
||||
|
||||
# get plotting dfs
|
||||
source("~/git/LSHTM_analysis/scripts/plotting/get_plotting_dfs.R")
|
||||
|
||||
source("~/git/LSHTM_analysis/scripts/plotting/plotting_colnames.R")
|
||||
|
||||
length(all_stability_cols); length(raw_stability_cols)
|
||||
length(scaled_stability_cols); length(outcome_stability_cols)
|
||||
length(affinity_dist_colnames)
|
||||
|
||||
static_cols = c("mutationinformation", "position", "sensitivity")
|
||||
other_cols_all = c(scaled_stability_cols, scaled_affinity_cols, affinity_dist_colnames)
|
||||
|
||||
#omit avg cols and foldx_scaled_signC cols
|
||||
other_cols = other_cols_all[grep("avg", other_cols_all, invert = T)]
|
||||
other_cols = other_cols[grep("foldx_scaled_signC",other_cols, invert = T )]
|
||||
other_cols
|
||||
|
||||
cols_to_extract = c(static_cols, other_cols)
|
||||
expected_ncols = length(static_cols) + length(other_cols)
|
||||
|
||||
str_df = merged_df3[, cols_to_extract]
|
||||
|
||||
if (ncol(str_df) == expected_ncols){
|
||||
cat("\nPASS: successfully extracted cols for calculating prominent effects")
|
||||
}else{
|
||||
stop("\nAbort: Could not extract cols for calculating prominent effects")
|
||||
}
|
||||
|
||||
#=========================
|
||||
# Masking affinity columns
|
||||
#=========================
|
||||
# First make values for affinity cols 0 when their corresponding dist >10
|
||||
head(str_df)
|
||||
|
||||
# replace in place affinity values >10
|
||||
str_df[str_df["ligand_distance"]>10,"affinity_scaled"]=0
|
||||
str_df[str_df["ligand_distance"]>10,"mmcsm_lig_scaled"]=0
|
||||
|
||||
#ppi2 gene: replace in place ppi2 affinity values where ppi2 dist >10
|
||||
|
||||
if (tolower(gene)%in%geneL_ppi2){
|
||||
str_df[str_df["interface_dist"]>10,"mcsm_ppi2_scaled"]=0
|
||||
}
|
||||
|
||||
# na gene: replace in place na affinity values where na dist >10
|
||||
if (tolower(gene)%in%geneL_na){
|
||||
str_df[str_df["XXXX"]>10,"mcsm_na_scaled"]=0
|
||||
}
|
||||
|
||||
colnames(str_df)
|
||||
|
||||
head(str_df)
|
||||
|
||||
# get names of cols to calculate the prominent effects from
|
||||
scaled_cols_tc = c("duet_scaled", "deepddg_scaled"
|
||||
, "ddg_dynamut2_scaled", "foldx_scaled","affinity_scaled"
|
||||
, "mmcsm_lig_scaled" , "mcsm_ppi2_scaled")
|
||||
|
||||
#--------------------------------
|
||||
#get rowmax for absolute values
|
||||
#--------------------------------
|
||||
#str_df$row_maximum = apply(str_df[,-1], 1, function(x){max(abs(x))})
|
||||
#str_df$row_maximum = apply(str_df[,scaled_cols_tc], 1, function(x){max(abs(x))})
|
||||
|
||||
#correct
|
||||
#BOO= abs(str_df[,scaled_cols_tc]) == str_df[,'row_maximum']; head(BOO)
|
||||
#also corr but weird
|
||||
#POO = which(abs(str_df[,scaled_cols_tc]) == str_df[,'row_maximum'], arr.ind =T); head(POO)
|
||||
|
||||
################################################
|
||||
# #-------------
|
||||
# # short df: try
|
||||
# #-------------
|
||||
# df2_short = str_df[str_df$position%in%c(167, 423, 427),]
|
||||
# df2_short = str_df[str_df$position%in%c(170, 167, 493, 453, 435, 433, 480, 456, 445),]
|
||||
# df2_short = str_df[str_df$position%in%c(435, 480),]
|
||||
#
|
||||
#
|
||||
# give_col=function(x,y,df=df2_short){
|
||||
# df[df$position==x,y]
|
||||
# }
|
||||
#
|
||||
# for (i in unique(df2_short$position) ){
|
||||
# print(i)
|
||||
# #print(paste0("\nNo. of unique positions:", length(unique(df2$position))) )
|
||||
# #cat(length(unique(df2$position)))
|
||||
# #df2_short[df2_short$position==i,scaled_cols_tc]
|
||||
#
|
||||
# biggest = max(abs(give_col(i,scaled_cols_tc)))
|
||||
#
|
||||
# df2_short[df2_short$position==i,'abs_max_effect'] = biggest
|
||||
# df2_short[df2_short$position==i,'effect_type']= names(
|
||||
# give_col(i,scaled_cols_tc)[which(
|
||||
# abs(
|
||||
# give_col(i,scaled_cols_tc)
|
||||
# ) == biggest, arr.ind=T
|
||||
# )[, "col"]])
|
||||
#
|
||||
# effect_name = df2_short[df2_short$position==i,'effect_type'][1] # pick first one in case we have multiple exact values
|
||||
#
|
||||
# # get index/rowname for value of max effect, and then use it to get the original sign
|
||||
# # here
|
||||
# #df2_short[df2_short$position==i,c(effect_name)]
|
||||
# #which(abs(df2_short[df2_short$position==i,c('position',effect_name)][effect_name])==biggest, arr.ind=T)
|
||||
# ind = rownames(which(abs(df2_short[df2_short$position==i,c('position',effect_name)][effect_name])== biggest, arr.ind=T))
|
||||
# df2_short[df2_short$position==i,'effect_sign'] = sign(df2_short[effect_name][ind,])
|
||||
# }
|
||||
#
|
||||
# # ends with suffix 2 if dups
|
||||
# df2_short$effect_type = sub("\\.[0-9]+", "", df2_short$effect_type) # cull duplicate effect types that happen when there are exact duplicate values
|
||||
#
|
||||
# View(df2_short)
|
||||
|
||||
|
||||
|
||||
#===============
|
||||
# whole df
|
||||
#===============
|
||||
give_col=function(x,y,df=str_df){
|
||||
df[df$position==x,y]
|
||||
}
|
||||
|
||||
for (i in unique(str_df$position) ){
|
||||
print(i)
|
||||
#cat(length(unique(str_df$position)))
|
||||
|
||||
biggest = max(abs(give_col(i,scaled_cols_tc)))
|
||||
|
||||
str_df[str_df$position==i,'abs_max_effect'] = biggest
|
||||
str_df[str_df$position==i,'effect_type']= names(
|
||||
give_col(i,scaled_cols_tc)[which(
|
||||
abs(
|
||||
give_col(i,scaled_cols_tc)
|
||||
) == biggest, arr.ind=T
|
||||
)[, "col"]])[1]
|
||||
|
||||
effect_name = unique(str_df[str_df$position==i,'effect_type'])#[1] # pick first one in case we have multiple exact values
|
||||
|
||||
# get index/rowname for value of max effect, and then use it to get the original sign
|
||||
# here
|
||||
ind = rownames(which(abs(str_df[str_df$position==i,c('position',effect_name)][effect_name])== biggest, arr.ind=T))
|
||||
str_df[str_df$position==i,'effect_sign'] = sign(str_df[effect_name][ind,])[1]
|
||||
}
|
||||
|
||||
# ends with suffix 2 if dups
|
||||
str_df$effect_type = sub("\\.[0-9]+", "", str_df$effect_type) # cull duplicate effect types that happen when there are exact duplicate values
|
||||
|
||||
colnames(str_df)
|
||||
|
||||
# check
|
||||
str_df_check = str_df[str_df$position%in%c(24, 32,160, 303, 334),]
|
||||
table(str_df$effect_type)
|
||||
|
||||
#================
|
||||
# for Plots
|
||||
#================
|
||||
str_df_short = str_df[, c("mutationinformation","position","sensitivity"
|
||||
, "effect_type"
|
||||
, "effect_sign")]
|
||||
|
||||
table(str_df_short$effect_type)
|
||||
table(str_df_short$effect_sign)
|
||||
str(str_df_short)
|
||||
|
||||
# assign pe outcome
|
||||
str_df_short$pe_outcome = ifelse(str_df_short$effect_sign<0, "DD", "SS")
|
||||
table(str_df_short$pe_outcome )
|
||||
table(str_df_short$effect_sign)
|
||||
|
||||
#==============
|
||||
# group effect type:
|
||||
# lig, ppi2, nuc. acid, stability
|
||||
#==============
|
||||
|
||||
affcols = c("affinity_scaled", "mmcsm_lig_scaled")
|
||||
ppi2_cols = c("mcsm_ppi2_scaled")
|
||||
#nuc_na_cols = c("mcsm_a_scaled")
|
||||
|
||||
|
||||
#lig
|
||||
table(str_df_short$effect_type)
|
||||
str_df_short$effect_grouped = ifelse(str_df_short$effect_type%in%affcols
|
||||
, "lig"
|
||||
, str_df_short$effect_type)
|
||||
table(str_df_short$effect_grouped)
|
||||
|
||||
#ppi2
|
||||
str_df_short$effect_grouped = ifelse(str_df_short$effect_grouped%in%ppi2_cols
|
||||
, "ppi2"
|
||||
, str_df_short$effect_grouped)
|
||||
table(str_df_short$effect_grouped)
|
||||
|
||||
#stability
|
||||
str_df_short$effect_grouped = ifelse(!str_df_short$effect_grouped%in%c("lig", "ppi2")
|
||||
, "stability"
|
||||
, str_df_short$effect_grouped)
|
||||
|
||||
table(str_df_short$effect_grouped)
|
||||
|
||||
# create a sign as well
|
||||
str_df_short$pe_effect_outcome = paste0(str_df_short$pe_outcome, "_"
|
||||
, str_df_short$effect_grouped)
|
||||
|
||||
table(str_df_short$pe_effect_outcome)
|
||||
|
||||
|
||||
|
||||
#####################################################################
|
||||
# Chimera: for colouring
|
||||
####################################################################
|
||||
|
||||
#-------------------------------------
|
||||
# get df with unique position
|
||||
#--------------------------------------
|
||||
#data[!duplicated(data$x), ]
|
||||
str_df_plot = str_df[!duplicated(str_df$position),]
|
||||
|
||||
if (nrow(str_df_plot) == length(unique(str_df$position))){
|
||||
cat("\nPASS: successfully extracted df with unique positions")
|
||||
}else{
|
||||
stop("\nAbort: Could not extract df with unique positions")
|
||||
}
|
||||
|
||||
#-------------------------------------
|
||||
# generate colours for effect types
|
||||
#--------------------------------------
|
||||
str_df_plot_cols = str_df_plot[, c("position", "sensitivity"
|
||||
, affinity_dist_colnames
|
||||
, "abs_max_effect"
|
||||
, "effect_type"
|
||||
, "effect_sign")]
|
||||
head(str_df_plot_cols)
|
||||
|
||||
# colour intensity based on sign
|
||||
str_df_plot_cols$colour_hue = ifelse(str_df_plot_cols$effect_sign<0, "bright", "light")
|
||||
table(str_df_plot_cols$colour_hue)
|
||||
head(str_df_plot_cols)
|
||||
# colour based on effect
|
||||
table(str_df_plot_cols$effect_type)
|
||||
|
||||
pe_colour_map = c("affinity_scaled" = "salmon"
|
||||
, "mmcsm_lig_scaled" = "salmon"
|
||||
, "mcsm_ppi2_scaled" = "pink"
|
||||
, "mcsm_na_scaled" = "orange"
|
||||
, "duet_scaled" = "dimgray"
|
||||
, "deepddg_scaled" = "dimgray"
|
||||
, "ddg_dynamut2_scaled"= "dimgray"
|
||||
, "foldx_scaled" = "dimgray")
|
||||
|
||||
#unlist(d[c('a', 'a', 'c', 'b')], use.names=FALSE)
|
||||
|
||||
#map the colours
|
||||
str_df_plot_cols$colour_map= unlist(map(str_df_plot_cols$effect_type
|
||||
,function(x){pe_colour_map[[x]]}
|
||||
))
|
||||
|
||||
str_df_plot_cols$colours = paste0(str_df_plot_cols$colour_hue
|
||||
, "_"
|
||||
, str_df_plot_cols$colour_map)
|
||||
head(str_df_plot_cols$colours)
|
||||
table(str_df_plot_cols$colours)
|
||||
|
||||
|
||||
class(str_df_plot_cols$colour_map)
|
||||
str(str_df_plot_cols)
|
||||
|
||||
# sort by colour
|
||||
head(str_df_plot_cols)
|
||||
str_df_plot_cols = str_df_plot_cols[order(str_df_plot_cols$colour_map), ]
|
||||
head(str_df_plot_cols)
|
||||
|
||||
#======================================
|
||||
# write file with prominent effects
|
||||
#======================================
|
||||
outdir_images = paste0("~/git/Writing/thesis/images/results/", tolower(gene), "/")
|
||||
write.csv(str_df_plot_cols, paste0(outdir_images, tolower(gene), "_prominent_effects.csv"))
|
||||
|
||||
################################
|
||||
# printing for chimera
|
||||
###############################
|
||||
str_df_plot_cols$pos_chain = paste0(str_df_plot_cols$position, ".B,")
|
||||
table(str_df_plot_cols$colour_map)
|
||||
|
||||
#===================================================
|
||||
#-------------------
|
||||
# Ligand Affinity
|
||||
#-------------------
|
||||
foo = str_df_plot_cols[str_df_plot_cols$colours=="yellow",]
|
||||
all(foo2$effect_sign == 1)
|
||||
|
||||
foo1 = str_df_plot_cols[str_df_plot_cols$colours=="bright_salmon",]
|
||||
all(foo3$effect_sign == -1)
|
||||
|
||||
#light salmon: stabilising affinity
|
||||
table(str_df_plot_cols$colours)
|
||||
|
||||
affinity_pos_l = str_df_plot_cols$pos_chain[str_df_plot_cols$colours=="light_salmon"]
|
||||
affinity_pos_lc = paste(affinity_pos_l, collapse = "")
|
||||
affinity_pos_lc
|
||||
table(str_df_plot_cols$colours)[["light_salmon"]]
|
||||
|
||||
#bright salmon: DEstabilsing affinity
|
||||
affinity_pos_b = str_df_plot_cols$pos_chain[str_df_plot_cols$colours=="bright_salmon"]
|
||||
affinity_pos_bc = paste(affinity_pos_b, collapse = "")
|
||||
affinity_pos_bc
|
||||
table(str_df_plot_cols$colours)[["bright_salmon"]]
|
||||
|
||||
c1 = length(affinity_pos_l) + length(affinity_pos_b) == table(str_df_plot_cols$colour_map)[["salmon"]]
|
||||
|
||||
if (c1){
|
||||
cat("PASS: affinity colour numbers checked")
|
||||
}else{
|
||||
stop("Abort: affinity colour numbers mismtatch")
|
||||
}
|
||||
|
||||
#put in chimera cmd
|
||||
affinity_pos_lc
|
||||
affinity_pos_bc
|
||||
|
||||
#===================================================
|
||||
#-------------------
|
||||
# ppi2 Affinity
|
||||
#-------------------
|
||||
foo2 = str_df_plot_cols[str_df_plot_cols$colours=="light_pink",]
|
||||
all(foo2$effect_sign == 1)
|
||||
|
||||
foo3 = str_df_plot_cols[str_df_plot_cols$colours=="bright_pink",]
|
||||
all(foo3$effect_sign == -1)
|
||||
|
||||
#light_pink: stabilising affinity
|
||||
table(str_df_plot_cols$colours)
|
||||
|
||||
ppi2_pos_l = str_df_plot_cols$pos_chain[str_df_plot_cols$colours=="light_pink"]
|
||||
ppi2_pos_lc = paste(ppi2_pos_l, collapse = "")
|
||||
ppi2_pos_lc
|
||||
table(str_df_plot_cols$colours)[["light_pink"]]
|
||||
|
||||
#bright pink: DEstabilsing affinity
|
||||
ppi2_pos_b = str_df_plot_cols$pos_chain[str_df_plot_cols$colours=="bright_pink"]
|
||||
ppi2_pos_bc = paste(ppi2_pos_b, collapse = "")
|
||||
ppi2_pos_bc
|
||||
table(str_df_plot_cols$colours)[["bright_pink"]]
|
||||
|
||||
c2 = length(ppi2_pos_l) + length(ppi2_pos_b) == table(str_df_plot_cols$colour_map)[["pink"]]
|
||||
|
||||
if (c2){
|
||||
cat("PASS: ppi2 colour numbers checked")
|
||||
}else{
|
||||
stop("Abort: ppi2 colour numbers mismtatch")
|
||||
}
|
||||
|
||||
#put in chimera cmd
|
||||
ppi2_pos_lc
|
||||
ppi2_pos_bc
|
||||
|
||||
#=========================================================
|
||||
#-------------------
|
||||
# Stability
|
||||
#-------------------
|
||||
foo4 = str_df_plot_cols[str_df_plot_cols$colours=="light_dimgray",]
|
||||
all(foo2$effect_sign == 1)
|
||||
|
||||
foo5 = str_df_plot_cols[str_df_plot_cols$colours=="bright_dimgray",]
|
||||
all(foo3$effect_sign == -1)
|
||||
|
||||
#light_dimgray: stabilising Stability
|
||||
table(str_df_plot_cols$colours)
|
||||
|
||||
stab_pos_l = str_df_plot_cols$pos_chain[str_df_plot_cols$colours=="light_dimgray"]
|
||||
stab_pos_lc = paste(stab_pos_l, collapse = "")
|
||||
stab_pos_lc
|
||||
table(str_df_plot_cols$colours)[["light_dimgray"]]
|
||||
|
||||
#bright_dimgray pink: DEstabilsing Stability
|
||||
stab_pos_b = str_df_plot_cols$pos_chain[str_df_plot_cols$colours=="bright_dimgray"]
|
||||
stab_pos_bc = paste(stab_pos_b, collapse = "")
|
||||
stab_pos_bc
|
||||
table(str_df_plot_cols$colours)[["bright_dimgray"]]
|
||||
|
||||
c3 = length(stab_pos_l) + length(stab_pos_b) == table(str_df_plot_cols$colour_map)[["dimgray"]]
|
||||
|
||||
if (c3){
|
||||
cat("PASS: stability colour numbers checked")
|
||||
}else{
|
||||
stop("Abort: stability colour numbers mismtatch")
|
||||
}
|
||||
|
||||
#put in chimera cmd
|
||||
stab_pos_lc
|
||||
stab_pos_bc
|
||||
|
||||
|
||||
# stab tool count
|
||||
table(str_df_plot_cols$effect_type)
|
||||
|
||||
table(str_df_plot_cols$effect_type, str_df_plot_cols$effect_sign)
|
||||
|
||||
|
|
@ -0,0 +1,348 @@
|
|||
#source("/home/tanu/git/LSHTM_analysis/scripts/plotting/plotting_thesis/pe_sens_site_count_rpob.R")
|
||||
|
||||
if ( tolower(gene)%in%c("rpob") ){
|
||||
cat("\nPlots available for layout are:")
|
||||
|
||||
duetP
|
||||
foldxP
|
||||
deepddgP
|
||||
dynamut2P
|
||||
proveanP
|
||||
snap2P
|
||||
|
||||
mLigP
|
||||
mmLigP
|
||||
posC_lig
|
||||
|
||||
ppi2P
|
||||
posC_ppi2
|
||||
|
||||
nca_distP
|
||||
posC_nca
|
||||
|
||||
peP2
|
||||
sens_siteP
|
||||
peP # not used
|
||||
sensP # not used
|
||||
}
|
||||
|
||||
|
||||
#========================
|
||||
# Common title settings
|
||||
#=========================
|
||||
theme_georgia <- function(...) {
|
||||
theme_gray(base_family = "sans", ...) +
|
||||
theme(plot.title = element_text(face = "bold"))
|
||||
}
|
||||
title_theme <- calc_element("plot.title", theme_georgia())
|
||||
|
||||
###############################################################
|
||||
common_bp_title = paste0("Sites <", DistCutOff, angstroms_symbol)
|
||||
|
||||
# extract common legends
|
||||
# lig affinity
|
||||
common_legend_outcome = get_legend(mLigP +
|
||||
guides(color = guide_legend(nrow = 1)) +
|
||||
theme(legend.position = "top"))
|
||||
|
||||
# stability
|
||||
common_legend_outcome = get_legend(duetP +
|
||||
guides(color = guide_legend(nrow = 1)) +
|
||||
theme(legend.position = "top"))
|
||||
# conservation
|
||||
cons_common_legend_outcome = get_legend(snap2P +
|
||||
guides(color = guide_legend(nrow = 1)) +
|
||||
theme(legend.position = "top"))
|
||||
###################################################################
|
||||
#==================================
|
||||
# Stability+Conservation: COMBINE
|
||||
#==================================
|
||||
tt_size = 10
|
||||
#----------------------------
|
||||
# stability and consv title
|
||||
#----------------------------
|
||||
tt_stab = ggdraw() +
|
||||
draw_label(
|
||||
paste0("Stability outcome"),
|
||||
fontfamily = title_theme$family,
|
||||
fontface = title_theme$face,
|
||||
#size = title_theme$size
|
||||
size = tt_size
|
||||
)
|
||||
|
||||
tt_cons = ggdraw() +
|
||||
draw_label(
|
||||
paste0("Conservation outcome"),
|
||||
fontfamily = title_theme$family,
|
||||
fontface = title_theme$face,
|
||||
size = tt_size
|
||||
)
|
||||
|
||||
#----------------------
|
||||
# Output plot
|
||||
#-----------------------
|
||||
stab_cons_CLP = paste0(outdir_images
|
||||
,tolower(gene)
|
||||
,"_stab_cons_BP_CLP.png")
|
||||
|
||||
print(paste0("plot filename:", stab_cons_CLP))
|
||||
png(stab_cons_CLP, units = "in", width = 10, height = 5, res = 300 )
|
||||
|
||||
cowplot::plot_grid(
|
||||
cowplot::plot_grid(
|
||||
cowplot::plot_grid(
|
||||
tt_stab,
|
||||
common_legend_outcome,
|
||||
nrow = 2
|
||||
),
|
||||
cowplot::plot_grid(
|
||||
duetP,
|
||||
foldxP,
|
||||
deepddgP,
|
||||
dynamut2P,
|
||||
nrow = 1,
|
||||
labels = c("A", "B", "C", "D"),
|
||||
label_size = 12),
|
||||
nrow = 2,
|
||||
rel_heights=c(1,10)
|
||||
),
|
||||
NULL,
|
||||
cowplot::plot_grid(
|
||||
cowplot::plot_grid(
|
||||
cowplot::plot_grid(
|
||||
tt_cons,
|
||||
cons_common_legend_outcome,
|
||||
nrow = 2
|
||||
),
|
||||
cowplot::plot_grid(
|
||||
proveanP,
|
||||
snap2P,
|
||||
nrow=1,
|
||||
labels = c("E", "F"),
|
||||
align = "hv"),
|
||||
nrow = 2,
|
||||
rel_heights = c(1, 10),
|
||||
label_size = 12),
|
||||
nrow=1
|
||||
),
|
||||
rel_widths = c(2,0.15,1),
|
||||
nrow=1
|
||||
)
|
||||
|
||||
dev.off()
|
||||
|
||||
#################################################################
|
||||
#=======================================
|
||||
# Affinity barplots: COMBINE ALL four
|
||||
#========================================
|
||||
ligT = paste0(common_bp_title, " ligand")
|
||||
lig_affT = ggdraw() +
|
||||
draw_label(
|
||||
ligT,
|
||||
fontfamily = title_theme$family,
|
||||
fontface = title_theme$face,
|
||||
#size = title_theme$size
|
||||
size = 8
|
||||
)
|
||||
|
||||
p1 = cowplot::plot_grid(cowplot::plot_grid(lig_affT
|
||||
, common_legend_outcome
|
||||
, nrow=2),
|
||||
cowplot::plot_grid(mLigP, mmLigP, posC_lig
|
||||
, nrow = 1
|
||||
, rel_widths = c(1,0.65,1.8)
|
||||
, align = "h"),
|
||||
nrow = 2,
|
||||
rel_heights = c(1,8)
|
||||
|
||||
)
|
||||
p1
|
||||
###########################################################
|
||||
ncaT = paste0(common_bp_title, " Nucleic Acid")
|
||||
nca_affT = ggdraw() +
|
||||
draw_label(
|
||||
ncaT,
|
||||
fontfamily = title_theme$family,
|
||||
fontface = title_theme$face,
|
||||
#size = title_theme$size
|
||||
size = 8
|
||||
)
|
||||
|
||||
p2 = cowplot::plot_grid(cowplot::plot_grid(nca_affT
|
||||
, common_legend_outcome
|
||||
, nrow=2),
|
||||
cowplot::plot_grid(nca_distP, posC_nca
|
||||
, nrow = 1
|
||||
, rel_widths = c(1,1.8)
|
||||
, align = "h"),
|
||||
nrow = 2,
|
||||
rel_heights = c(1,8)
|
||||
)
|
||||
p2
|
||||
###########################################################
|
||||
ppi2T = paste0(common_bp_title, " PP-interface")
|
||||
ppi2_affT = ggdraw() +
|
||||
draw_label(
|
||||
ppi2T,
|
||||
fontfamily = title_theme$family,
|
||||
fontface = title_theme$face,
|
||||
size = 8
|
||||
)
|
||||
|
||||
p3 = cowplot::plot_grid(cowplot::plot_grid(ppi2_affT, common_legend_outcome, nrow=2),
|
||||
cowplot::plot_grid(ppi2P, posC_ppi2
|
||||
, nrow = 1
|
||||
, rel_widths = c(1,1.9)
|
||||
, align = "h"),
|
||||
nrow = 2,
|
||||
rel_heights = c(1,8)
|
||||
)
|
||||
p3
|
||||
|
||||
#### Combine p1+p2+p3 ####
|
||||
w = 11.79
|
||||
h = 3.5
|
||||
mut_impact_CLP = paste0(outdir_images
|
||||
,tolower(gene)
|
||||
,"_mut_impactCLP.png")
|
||||
|
||||
#svg(affP, width = 20, height = 5.5)
|
||||
print(paste0("plot filename:", mut_impact_CLP))
|
||||
png(mut_impact_CLP, units = "in", width = w, height = h, res = 300 )
|
||||
|
||||
cowplot::plot_grid(p1,
|
||||
p2,
|
||||
p3,
|
||||
#p4,
|
||||
nrow = 1,
|
||||
labels = "AUTO",
|
||||
label_size = 12,
|
||||
rel_widths = c(2.5,2,2)
|
||||
#, rel_heights = c(1)
|
||||
)
|
||||
|
||||
dev.off()
|
||||
w = 11.79
|
||||
h = 3.5
|
||||
mut_impact_CLP = paste0(outdir_images
|
||||
,tolower(gene)
|
||||
,"_mut_impactCLP.png")
|
||||
|
||||
#svg(affP, width = 20, height = 5.5)
|
||||
print(paste0("plot filename:", mut_impact_CLP))
|
||||
png(mut_impact_CLP, units = "in", width = w, height = h, res = 300 )
|
||||
|
||||
cowplot::plot_grid(p1,
|
||||
p2,
|
||||
p3,
|
||||
#p4,
|
||||
nrow = 1,
|
||||
labels = "AUTO",
|
||||
label_size = 12,
|
||||
rel_widths = c(2.5,2,2)
|
||||
#, rel_heights = c(1)
|
||||
)
|
||||
|
||||
dev.off()
|
||||
|
||||
#### Combine p4: ALL pos count ####
|
||||
# PE + All position count
|
||||
peT_allT = ggdraw() +
|
||||
draw_label(
|
||||
paste0("All mutation sites"),
|
||||
fontfamily = title_theme$family,
|
||||
fontface = title_theme$face,
|
||||
#size = title_theme$size
|
||||
size = 8
|
||||
)
|
||||
|
||||
p4 = cowplot::plot_grid(cowplot::plot_grid(peT_allT, nrow = 2
|
||||
, rel_widths = c(1,3),axis = "lr"),
|
||||
cowplot::plot_grid(
|
||||
peP2, posC_all,
|
||||
nrow = 2,
|
||||
rel_widths = c(1,1),
|
||||
align = "v",
|
||||
axis = "lr",
|
||||
rel_heights = c(1,8)
|
||||
),
|
||||
rel_heights = c(1,18),
|
||||
nrow = 2,axis = "lr")
|
||||
p4
|
||||
|
||||
w = 3.5
|
||||
h = 4
|
||||
mut_impact_CLP_PE = paste0(outdir_images
|
||||
,tolower(gene)
|
||||
,"_mut_impactCLP_PE.png")
|
||||
|
||||
print(paste0("plot filename:", mut_impact_CLP_PE))
|
||||
png(mut_impact_CLP_PE, units = "in", width = w, height = h, res = 300 )
|
||||
|
||||
cowplot::plot_grid(p4,
|
||||
nrow = 1,
|
||||
labels = c("D"),
|
||||
label_size = 12#,
|
||||
#rel_widths = c(2.5,2,2)
|
||||
#, rel_heights = c(1)
|
||||
)
|
||||
|
||||
dev.off()
|
||||
|
||||
##################################################
|
||||
sensP
|
||||
consurfP
|
||||
#=================
|
||||
#### Combine sensitivity + ConSurf ####
|
||||
# or ConSurf
|
||||
#=================
|
||||
w = 3
|
||||
h = 3
|
||||
# sens_conP = paste0(outdir_images
|
||||
# ,tolower(gene)
|
||||
# ,"_sens_cons_CLP.png")
|
||||
#
|
||||
# print(paste0("plot filename:", sens_conP))
|
||||
# png(sens_conP, units = "in", width = w, height = h, res = 300 )
|
||||
#
|
||||
# cowplot::plot_grid(sensP, consurfP,
|
||||
# nrow = 2,
|
||||
# rel_heights = c(1, 1.5)
|
||||
# )
|
||||
#
|
||||
# dev.off()
|
||||
|
||||
conCLP = paste0(outdir_images
|
||||
,tolower(gene)
|
||||
,"_consurf_BP.png")
|
||||
|
||||
print(paste0("plot filename:", conCLP))
|
||||
png(conCLP, units = "in", width = w, height = h, res = 300 )
|
||||
consurfP
|
||||
|
||||
dev.off()
|
||||
#================================
|
||||
# Sensitivity mutation numbers: geom_tile
|
||||
#================================
|
||||
sensCLP = paste0(outdir_images
|
||||
,tolower(gene)
|
||||
,"_sensN_tile.png")
|
||||
|
||||
print(paste0("plot filename:", sensCLP))
|
||||
png(sensCLP, units = "in", width = 1, height = 1, res = 300 )
|
||||
sensP
|
||||
dev.off()
|
||||
#================================
|
||||
# Sensitivity SITE numbers: geom_tile
|
||||
#================================
|
||||
sens_siteCLP = paste0(outdir_images
|
||||
,tolower(gene)
|
||||
,"_sens_siteC_tile.png")
|
||||
|
||||
print(paste0("plot filename:", sens_siteCLP))
|
||||
png(sens_siteCLP, units = "in", width = 1.2, height = 1, res = 300 )
|
||||
sens_siteP
|
||||
dev.off()
|
||||
|
||||
###########################################################
|
||||
|
||||
372
scripts/plotting/plotting_thesis/rpob/basic_barplots_rpob.R
Normal file
372
scripts/plotting/plotting_thesis/rpob/basic_barplots_rpob.R
Normal file
|
|
@ -0,0 +1,372 @@
|
|||
#!/usr/bin/env Rscript
|
||||
#########################################################
|
||||
# TASK: Barplots
|
||||
# basic barplots with outcome
|
||||
# basic barplots with frequency of count of mutations
|
||||
#########################################################
|
||||
#=============
|
||||
# Data: Input
|
||||
#==============
|
||||
#source("~/git/LSHTM_analysis/config/pnca.R")
|
||||
#source("~/git/LSHTM_analysis/config/embb.R")
|
||||
#source("~/git/LSHTM_analysis/config/gid.R")
|
||||
|
||||
#source("~/git/LSHTM_analysis/config/alr.R")
|
||||
#source("~/git/LSHTM_analysis/config/katg.R")
|
||||
#source("~/git/LSHTM_analysis/config/rpob.R")
|
||||
|
||||
#source("~/git/LSHTM_analysis/scripts/plotting/get_plotting_dfs.R")
|
||||
#source("~/git/LSHTM_analysis/scripts/plotting/plotting_colnames.R") sourced by above
|
||||
|
||||
#cat("\nSourced plotting cols as well:", length(plotting_cols))
|
||||
|
||||
####################################################
|
||||
class(merged_df3)
|
||||
|
||||
df3 = subset(merged_df3, select = -c(pos_count))
|
||||
|
||||
#=======
|
||||
# output
|
||||
#=======
|
||||
outdir_images = paste0("~/git/Writing/thesis/images/results/", tolower(gene), "/")
|
||||
cat("plots will output to:", outdir_images)
|
||||
|
||||
##########################################################
|
||||
# blue, red bp
|
||||
sts = 8
|
||||
lts = 8
|
||||
ats = 8
|
||||
als = 8
|
||||
ltis = 8
|
||||
geom_ls = 2.2
|
||||
|
||||
#pos_count
|
||||
subtitle_size = 8
|
||||
geom_ls_pc = 2.2
|
||||
leg_text_size = 8
|
||||
axis_text_size = 8
|
||||
axis_label_size = 8
|
||||
|
||||
###########################################################
|
||||
#------------------------------
|
||||
# plot default sizes
|
||||
#------------------------------
|
||||
#=========================
|
||||
# Affinity outcome
|
||||
# check this var: outcome_cols_affinity
|
||||
# get from preformatting or put in globals
|
||||
#==========================
|
||||
DistCutOff
|
||||
LigDist_colname # = "ligand_distance" # from globals
|
||||
ppi2Dist_colname
|
||||
naDist_colname
|
||||
|
||||
###########################################################
|
||||
# get plotting data within the distance
|
||||
df3_lig = df3[df3[[LigDist_colname]]<DistCutOff,]
|
||||
df3_ppi2 = df3[df3[[ppi2Dist_colname]]<DistCutOff,]
|
||||
df3_na = df3[df3[[naDist_colname]]<DistCutOff,]
|
||||
common_bp_title = paste0("Sites <", DistCutOff, angstroms_symbol)
|
||||
|
||||
#------------------------------
|
||||
# barplot for ligand affinity:
|
||||
# <10 Ang of ligand
|
||||
#------------------------------
|
||||
mLigP = stability_count_bp(plotdf = df3_lig
|
||||
, df_colname = "ligand_outcome"
|
||||
#, leg_title = "mCSM-lig"
|
||||
#, bp_plot_title = paste(common_bp_title, "ligand")
|
||||
, yaxis_title = "Number of nsSNPs"
|
||||
, leg_position = "none"
|
||||
, subtitle_text = "mCSM\nLig"
|
||||
, bar_fill_values = c("#F8766D", "#00BFC4")
|
||||
, subtitle_colour= "black"
|
||||
, sts = sts
|
||||
, lts = lts
|
||||
, ats = ats
|
||||
, als = als
|
||||
, ltis = ltis
|
||||
, geom_ls = geom_ls
|
||||
)
|
||||
mLigP
|
||||
#------------------------------
|
||||
# barplot for ligand affinity:
|
||||
# <10 Ang of ligand
|
||||
# mmCSM-lig: will be the same no. of sites but the effect will be different
|
||||
#------------------------------
|
||||
mmLigP = stability_count_bp(plotdf = df3_lig
|
||||
, df_colname = "mmcsm_lig_outcome"
|
||||
#, leg_title = "mmCSM-lig"
|
||||
#, label_categories = labels_mmlig
|
||||
#, bp_plot_title = paste(common_bp_title, "ligand")
|
||||
|
||||
, yaxis_title = ""
|
||||
, leg_position = "none"
|
||||
, subtitle_text = "mmCSM\nLig"
|
||||
, bar_fill_values = c("#F8766D", "#00BFC4")
|
||||
, subtitle_colour= "black"
|
||||
, sts = sts
|
||||
, lts = lts
|
||||
, ats = ats
|
||||
, als = als
|
||||
, ltis = ltis
|
||||
, geom_ls = geom_ls
|
||||
)
|
||||
mmLigP
|
||||
#------------------------------
|
||||
# barplot for ppi2 affinity
|
||||
# <10 Ang of interface
|
||||
#------------------------------
|
||||
if (tolower(gene)%in%geneL_ppi2){
|
||||
ppi2P = stability_count_bp(plotdf = df3_ppi2
|
||||
, df_colname = "mcsm_ppi2_outcome"
|
||||
#, leg_title = "mCSM-ppi2"
|
||||
#, label_categories = labels_ppi2
|
||||
#, bp_plot_title = paste(common_bp_title, "PP-interface")
|
||||
|
||||
, yaxis_title = "Number of nsSNPs"
|
||||
, leg_position = "none"
|
||||
, subtitle_text = "mCSM\nPPI2"
|
||||
, bar_fill_values = c("#F8766D", "#00BFC4")
|
||||
, subtitle_colour= "black"
|
||||
, sts = sts
|
||||
, lts = lts
|
||||
, ats = ats
|
||||
, als = als
|
||||
, ltis = ltis
|
||||
, geom_ls = geom_ls
|
||||
)
|
||||
ppi2P
|
||||
}
|
||||
#----------------------------
|
||||
# barplot for ppi2 affinity
|
||||
# <10 Ang of interface
|
||||
#------------------------------
|
||||
if (tolower(gene)%in%geneL_na){
|
||||
nca_distP = stability_count_bp(plotdf = df3_na
|
||||
, df_colname = "mcsm_na_outcome"
|
||||
#, leg_title = "mCSM-NA"
|
||||
#, label_categories =
|
||||
#, bp_plot_title = paste(common_bp_title, "Dist to NA")
|
||||
|
||||
, yaxis_title = "Number of nsSNPs"
|
||||
, leg_position = "none"
|
||||
, subtitle_text = "mCSM\nNA"
|
||||
, bar_fill_values = c("#F8766D", "#00BFC4")
|
||||
, subtitle_colour= "black"
|
||||
, sts = sts
|
||||
, lts = lts
|
||||
, ats = ats
|
||||
, als = als
|
||||
, ltis = ltis
|
||||
, geom_ls = geom_ls
|
||||
)
|
||||
nca_distP
|
||||
}
|
||||
|
||||
#####################################################################
|
||||
# ------------------------------
|
||||
# bp site site count: mCSM-lig
|
||||
# < 10 Ang ligand
|
||||
# ------------------------------
|
||||
common_bp_title = paste0("Sites <", DistCutOff, angstroms_symbol)
|
||||
|
||||
posC_lig = site_snp_count_bp(plotdf = df3_lig
|
||||
, df_colname = "position"
|
||||
, xaxis_title = "Number of nsSNPs"
|
||||
, yaxis_title = "Number of Sites"
|
||||
, subtitle_colour = "chocolate4"
|
||||
, subtitle_text = ""
|
||||
, subtitle_size = subtitle_size
|
||||
, geom_ls = geom_ls_pc
|
||||
, leg_text_size = leg_text_size
|
||||
, axis_text_size = axis_text_size
|
||||
, axis_label_size = axis_label_size)
|
||||
|
||||
posC_lig
|
||||
#------------------------------
|
||||
# bp site site count: ppi2
|
||||
# < 10 Ang interface
|
||||
#------------------------------
|
||||
if (tolower(gene)%in%geneL_ppi2){
|
||||
posC_ppi2 = site_snp_count_bp(plotdf = df3_ppi2
|
||||
, df_colname = "position"
|
||||
, xaxis_title = "Number of nsSNPs"
|
||||
, yaxis_title = "Number of Sites"
|
||||
, subtitle_colour = "chocolate4"
|
||||
, subtitle_text = ""
|
||||
, subtitle_size = subtitle_size
|
||||
, geom_ls = geom_ls_pc
|
||||
, leg_text_size = leg_text_size
|
||||
, axis_text_size = axis_text_size
|
||||
, axis_label_size = axis_label_size)
|
||||
posC_ppi2
|
||||
}
|
||||
|
||||
#------------------------------
|
||||
# bp site site count: NCA dist
|
||||
# < 10 Ang nca
|
||||
#------------------------------
|
||||
if (tolower(gene)%in%geneL_na){
|
||||
posC_nca = site_snp_count_bp(plotdf = df3_na
|
||||
, df_colname = "position"
|
||||
, xaxis_title = "Number of nsSNPs"
|
||||
, yaxis_title = "Number of Sites"
|
||||
, subtitle_colour = "chocolate4"
|
||||
, subtitle_text = ""
|
||||
, subtitle_size = subtitle_size
|
||||
, geom_ls = geom_ls_pc
|
||||
, leg_text_size = leg_text_size
|
||||
, axis_text_size = axis_text_size
|
||||
, axis_label_size = axis_label_size)
|
||||
posC_nca
|
||||
}
|
||||
#===============================================================
|
||||
#------------------------------
|
||||
# bp site site count: ALL
|
||||
# <10 Ang ligand
|
||||
#------------------------------
|
||||
posC_all = site_snp_count_bp(plotdf = df3
|
||||
, df_colname = "position"
|
||||
, xaxis_title = "Number of nsSNPs"
|
||||
, yaxis_title = "Number of Sites"
|
||||
, subtitle_colour = "chocolate4"
|
||||
, subtitle_text = "All mutations sites"
|
||||
, subtitle_size = subtitle_size
|
||||
, geom_ls = geom_ls_pc
|
||||
, leg_text_size = leg_text_size
|
||||
, axis_text_size = axis_text_size
|
||||
, axis_label_size = axis_label_size)
|
||||
posC_all
|
||||
##################################################################
|
||||
consurfP = stability_count_bp(plotdf = df3
|
||||
, df_colname = "consurf_outcome"
|
||||
#, leg_title = "ConSurf"
|
||||
#, label_categories = labels_consurf
|
||||
, yaxis_title = "Number of nsSNPs"
|
||||
, leg_position = "top"
|
||||
, subtitle_text = "ConSurf"
|
||||
, bar_fill_values = consurf_colours # from globals
|
||||
, subtitle_colour= "black"
|
||||
, sts = sts
|
||||
, lts = lts
|
||||
, ats = ats
|
||||
, als = als
|
||||
, ltis = ltis
|
||||
, geom_ls = geom_ls)
|
||||
|
||||
consurfP
|
||||
|
||||
##############################################################
|
||||
sts_so = 10
|
||||
lts_so = 10
|
||||
ats_so = 10
|
||||
als_so = 10
|
||||
ltis_so = 10
|
||||
geom_ls_so = 2.5
|
||||
#===================
|
||||
# Stability
|
||||
#===================
|
||||
# duetP
|
||||
duetP = stability_count_bp(plotdf = df3
|
||||
, df_colname = "duet_outcome"
|
||||
, leg_title = "mCSM-DUET"
|
||||
#, label_categories = labels_duet
|
||||
, yaxis_title = "Number of nsSNPs"
|
||||
, leg_position = "none"
|
||||
, subtitle_text = "mCSM-DUET"
|
||||
, bar_fill_values = c("#F8766D", "#00BFC4")
|
||||
, subtitle_colour= "black"
|
||||
, sts = sts_so
|
||||
, lts = lts_so
|
||||
, ats = ats_so
|
||||
, als = als_so
|
||||
, ltis = ltis_so
|
||||
, geom_ls = geom_ls_so)
|
||||
duetP
|
||||
|
||||
# foldx
|
||||
foldxP = stability_count_bp(plotdf = df3
|
||||
, df_colname = "foldx_outcome"
|
||||
#, leg_title = "FoldX"
|
||||
#, label_categories = labels_foldx
|
||||
, yaxis_title = ""
|
||||
, leg_position = "none"
|
||||
, subtitle_text = "FoldX"
|
||||
, bar_fill_values = c("#F8766D", "#00BFC4")
|
||||
, sts = sts_so
|
||||
, lts = lts_so
|
||||
, ats = ats_so
|
||||
, als = als_so
|
||||
, ltis = ltis_so
|
||||
, geom_ls = geom_ls_so)
|
||||
foldxP
|
||||
|
||||
# deepddg
|
||||
deepddgP = stability_count_bp(plotdf = df3
|
||||
, df_colname = "deepddg_outcome"
|
||||
#, leg_title = "DeepDDG"
|
||||
#, label_categories = labels_deepddg
|
||||
, yaxis_title = ""
|
||||
, leg_position = "none"
|
||||
, subtitle_text = "DeepDDG"
|
||||
, bar_fill_values = c("#F8766D", "#00BFC4")
|
||||
, sts = sts_so
|
||||
, lts = lts_so
|
||||
, ats = ats_so
|
||||
, als = als_so
|
||||
, ltis = ltis_so
|
||||
, geom_ls = geom_ls_so)
|
||||
deepddgP
|
||||
|
||||
# deepddg
|
||||
dynamut2P = stability_count_bp(plotdf = df3
|
||||
, df_colname = "ddg_dynamut2_outcome"
|
||||
#, leg_title = "Dynamut2"
|
||||
#, label_categories = labels_ddg_dynamut2_outcome
|
||||
, yaxis_title = ""
|
||||
, leg_position = "none"
|
||||
, subtitle_text = "Dynamut2"
|
||||
, bar_fill_values = c("#F8766D", "#00BFC4")
|
||||
, sts = sts_so
|
||||
, lts = lts_so
|
||||
, ats = ats_so
|
||||
, als = als_so
|
||||
, ltis = ltis_so
|
||||
, geom_ls = geom_ls_so)
|
||||
dynamut2P
|
||||
|
||||
# provean
|
||||
proveanP = stability_count_bp(plotdf = df3
|
||||
, df_colname = "provean_outcome"
|
||||
#, leg_title = "PROVEAN"
|
||||
#, label_categories = labels_provean
|
||||
, yaxis_title = "Number of nsSNPs"
|
||||
, leg_position = "none" # top
|
||||
, subtitle_text = "PROVEAN"
|
||||
, bar_fill_values = c("#D01C8B", "#F1B6DA") # light pink and deep
|
||||
, sts = sts_so
|
||||
, lts = lts_so
|
||||
, ats = ats_so
|
||||
, als = als_so
|
||||
, ltis = ltis_so
|
||||
, geom_ls = geom_ls_so)
|
||||
proveanP
|
||||
|
||||
# snap2
|
||||
snap2P = stability_count_bp(plotdf = df3
|
||||
, df_colname = "snap2_outcome"
|
||||
#, leg_title = "SNAP2"
|
||||
#, label_categories = labels_snap2
|
||||
, yaxis_title = ""
|
||||
, leg_position = "none" # top
|
||||
, subtitle_text = "SNAP2"
|
||||
, bar_fill_values = c("#D01C8B", "#F1B6DA") # light pink and deep
|
||||
, sts = sts_so
|
||||
, lts = lts_so
|
||||
, ats = ats_so
|
||||
, als = als_so
|
||||
, ltis = ltis_so
|
||||
, geom_ls = geom_ls_so)
|
||||
snap2P
|
||||
#####################################################################################
|
||||
179
scripts/plotting/plotting_thesis/rpob/dm_om_plots_layout_rpob.R
Normal file
179
scripts/plotting/plotting_thesis/rpob/dm_om_plots_layout_rpob.R
Normal file
|
|
@ -0,0 +1,179 @@
|
|||
# source dm_om_plots.R
|
||||
source("/home/tanu/git/LSHTM_analysis/scripts/plotting/plotting_thesis/dm_om_plots.R")
|
||||
|
||||
##### plots to combine ####
|
||||
duetP
|
||||
foldxP
|
||||
deepddgP
|
||||
dynamut2P
|
||||
genomicsP
|
||||
consurfP
|
||||
proveanP
|
||||
snap2P
|
||||
mcsmligP
|
||||
mcsmlig2P
|
||||
mcsmppi2P
|
||||
mcsmnaP
|
||||
|
||||
# Plot labels
|
||||
tit1 = "Stability changes"
|
||||
tit2 = "Genomic measure"
|
||||
tit3 = "Distance to partners"
|
||||
tit4 = "Evolutionary Conservation"
|
||||
tit5 = "Affinity changes"
|
||||
pt_size = 30
|
||||
|
||||
theme_georgia <- function(...) {
|
||||
theme_gray(base_family = "sans", ...) +
|
||||
theme(plot.title = element_text(face = "bold"))
|
||||
}
|
||||
|
||||
|
||||
title_theme <- calc_element("plot.title", theme_georgia())
|
||||
|
||||
pt1 = ggdraw() +
|
||||
draw_label(
|
||||
tit1,
|
||||
fontfamily = title_theme$family,
|
||||
fontface = title_theme$face,
|
||||
#size = title_theme$size
|
||||
size = pt_size
|
||||
)
|
||||
|
||||
pt2 = ggdraw() +
|
||||
draw_label(
|
||||
tit2,
|
||||
fontfamily = title_theme$family,
|
||||
fontface = title_theme$face,
|
||||
size = pt_size
|
||||
)
|
||||
|
||||
pt3 = ggdraw() +
|
||||
draw_label(
|
||||
tit3,
|
||||
fontfamily = title_theme$family,
|
||||
fontface = title_theme$face,
|
||||
size = pt_size
|
||||
)
|
||||
|
||||
pt4 = ggdraw() +
|
||||
draw_label(
|
||||
tit4,
|
||||
fontfamily = title_theme$family,
|
||||
fontface = title_theme$face,
|
||||
size = pt_size
|
||||
)
|
||||
|
||||
|
||||
pt5 = ggdraw() +
|
||||
draw_label(
|
||||
tit5,
|
||||
fontfamily = title_theme$family,
|
||||
fontface = title_theme$face,
|
||||
size = pt_size
|
||||
)
|
||||
|
||||
#======================
|
||||
# Output plot function
|
||||
#======================
|
||||
OutPlot_dm_om = function(x){
|
||||
|
||||
# dist b/w plot title and plot
|
||||
relH_tp = c(0.08, 0.92)
|
||||
|
||||
my_label_size = 25
|
||||
#----------------
|
||||
# Top panel
|
||||
#----------------
|
||||
top_panel = cowplot::plot_grid(
|
||||
cowplot::plot_grid(pt1,
|
||||
cowplot::plot_grid(duetP, foldxP, deepddgP, dynamut2P
|
||||
, nrow = 1
|
||||
, labels = c("A", "B", "C", "D")
|
||||
, label_size = my_label_size)
|
||||
, ncol = 1
|
||||
, rel_heights = relH_tp
|
||||
),
|
||||
NULL,
|
||||
cowplot::plot_grid(pt2,
|
||||
cowplot::plot_grid(genomicsP
|
||||
, nrow = 1
|
||||
, labels = c("E")
|
||||
, label_size = my_label_size)
|
||||
, ncol = 1
|
||||
, rel_heights = relH_tp
|
||||
),
|
||||
NULL,
|
||||
cowplot::plot_grid(pt3,
|
||||
cowplot::plot_grid( #distanceP
|
||||
distanceP_lig
|
||||
#, distanceP_ppi2
|
||||
, distanceP_na
|
||||
, nrow = 1
|
||||
, labels = c("F", "G")
|
||||
, label_size = my_label_size)
|
||||
, ncol = 1
|
||||
, rel_heights = relH_tp
|
||||
),
|
||||
nrow = 1,
|
||||
rel_widths = c(2/7, 0.1/7, 0.5/7, 0.1/7, 1/7)
|
||||
)
|
||||
|
||||
#----------------
|
||||
# Bottom panel
|
||||
#----------------
|
||||
bottom_panel = cowplot::plot_grid(
|
||||
cowplot::plot_grid(pt4,
|
||||
cowplot::plot_grid(consurfP, proveanP, snap2P
|
||||
, nrow = 1
|
||||
, labels = c("H", "I", "J")
|
||||
, label_size = my_label_size)
|
||||
, ncol = 1
|
||||
, rel_heights =relH_tp
|
||||
),NULL,
|
||||
cowplot::plot_grid(pt5,
|
||||
cowplot::plot_grid(mcsmligP
|
||||
, mcsmlig2P
|
||||
, mcsmppi2P
|
||||
, mcsmnaP
|
||||
, nrow = 1
|
||||
, labels = c("K", "L", "M")
|
||||
, label_size = my_label_size)
|
||||
, ncol = 1
|
||||
, rel_heights = relH_tp
|
||||
),NULL,
|
||||
nrow = 1,
|
||||
rel_widths = c(3/6,0.1/6,3/6, 0.1/6 )
|
||||
)
|
||||
|
||||
#-------------------------------
|
||||
# combine: Top and Bottom panel
|
||||
#-------------------------------
|
||||
cowplot::plot_grid (top_panel, bottom_panel
|
||||
, nrow =2
|
||||
, rel_widths = c(1, 1)
|
||||
, align = "hv")
|
||||
}
|
||||
|
||||
#=====================
|
||||
# OutPlot: svg and png
|
||||
#======================
|
||||
dm_om_combinedP = paste0(outdir_images
|
||||
,tolower(gene)
|
||||
,"_dm_om_all.svg")
|
||||
|
||||
cat("DM OM plots with stats:", dm_om_combinedP)
|
||||
svg(dm_om_combinedP, width = 32, height = 18)
|
||||
|
||||
OutPlot_dm_om()
|
||||
dev.off()
|
||||
|
||||
|
||||
dm_om_combinedP_png = paste0(outdir_images
|
||||
,tolower(gene)
|
||||
,"_dm_om_all.png")
|
||||
cat("DM OM plots with stats:", dm_om_combinedP_png)
|
||||
png(dm_om_combinedP_png, width = 32, height = 18, units = "in", res = 300)
|
||||
|
||||
OutPlot_dm_om()
|
||||
dev.off()
|
||||
203
scripts/plotting/plotting_thesis/rpob/gg_pairs_all_rpob.R
Normal file
203
scripts/plotting/plotting_thesis/rpob/gg_pairs_all_rpob.R
Normal file
|
|
@ -0,0 +1,203 @@
|
|||
#source("~/git/LSHTM_analysis/config/embb.R")
|
||||
#source("~/git/LSHTM_analysis/scripts/plotting/plotting_colnames.R")
|
||||
#source("~/git/LSHTM_analysis/scripts/plotting/get_plotting_dfs.R")
|
||||
|
||||
my_gg_pairs=function(plot_df, plot_title
|
||||
, tt_args_size = 2.5
|
||||
, gp_args_size = 2.5){
|
||||
ggpairs(plot_df,
|
||||
columns = 1:(ncol(plot_df)-1),
|
||||
upper = list(
|
||||
continuous = wrap('cor', # ggally_cor()
|
||||
method = "spearman",
|
||||
use = "pairwise.complete.obs",
|
||||
title="ρ",
|
||||
digits=2,
|
||||
justify_labels = "centre",
|
||||
title_args=list(size=tt_args_size, colour="black"),#2.5
|
||||
group_args=list(size=gp_args_size)#2.5
|
||||
)
|
||||
),
|
||||
lower = list(
|
||||
continuous = wrap("points",
|
||||
alpha = 0.7,
|
||||
size=0.125),
|
||||
combo = wrap("dot",
|
||||
alpha = 0.7,
|
||||
size=0.125)
|
||||
),
|
||||
aes(colour = factor(ifelse(dst_mode==0,
|
||||
"S",
|
||||
"R") ),
|
||||
alpha = 0.5),
|
||||
title=plot_title) +
|
||||
|
||||
scale_colour_manual(values = c("red", "blue")) +
|
||||
scale_fill_manual(values = c("red", "blue")) #+
|
||||
# theme(text = element_text(size=7,
|
||||
# face="bold"))
|
||||
}
|
||||
|
||||
DistCutOff = 10
|
||||
###########################################################################
|
||||
geneL_normal = c("pnca")
|
||||
geneL_na = c("gid", "rpob")
|
||||
geneL_ppi2 = c("alr", "embb", "katg", "rpob")
|
||||
|
||||
merged_df3 = as.data.frame(merged_df3)
|
||||
|
||||
corr_plotdf = corr_data_extract(merged_df3
|
||||
, gene = gene
|
||||
, drug = drug
|
||||
, extract_scaled_cols = F)
|
||||
|
||||
aff_dist_cols = colnames(corr_plotdf)[grep("Dist", colnames(corr_plotdf))]
|
||||
static_cols = c("Log10(MAF)"
|
||||
, "Log10(OR)"
|
||||
)
|
||||
############################################################
|
||||
#=============================================
|
||||
# Creating masked df for affinity data
|
||||
#=============================================
|
||||
corr_affinity_df = corr_plotdf
|
||||
|
||||
#----------------------
|
||||
# Mask affinity columns
|
||||
#-----------------------
|
||||
corr_affinity_df[corr_affinity_df["Lig-Dist"]>DistCutOff,"mCSM-lig"]=0
|
||||
corr_affinity_df[corr_affinity_df["Lig-Dist"]>DistCutOff,"mmCSM-lig"]=0
|
||||
|
||||
if (tolower(gene)%in%geneL_ppi2){
|
||||
corr_affinity_df[corr_affinity_df["PPI-Dist"]>DistCutOff,"mCSM-PPI2"]=0
|
||||
}
|
||||
|
||||
if (tolower(gene)%in%geneL_na){
|
||||
corr_affinity_df[corr_affinity_df["NCA-Dist"]>DistCutOff,"mCSM-NA"]=0
|
||||
}
|
||||
|
||||
# count 0
|
||||
#res <- colSums(corr_affinity_df==0)/nrow(corr_affinity_df)*100
|
||||
unmasked_vals <- nrow(corr_affinity_df) - colSums(corr_affinity_df==0)
|
||||
unmasked_vals
|
||||
|
||||
##########################################################
|
||||
#================
|
||||
# Stability
|
||||
#================
|
||||
corr_ps_colnames = c(static_cols
|
||||
, "DUET"
|
||||
, "FoldX"
|
||||
, "DeepDDG"
|
||||
, "Dynamut2"
|
||||
, aff_dist_cols
|
||||
, "dst_mode")
|
||||
|
||||
corr_df_ps = corr_plotdf[, corr_ps_colnames]
|
||||
|
||||
# Plot #1
|
||||
plot_corr_df_ps = my_gg_pairs(corr_df_ps, plot_title="Stability estimates")
|
||||
|
||||
##########################################################
|
||||
#================
|
||||
# Conservation
|
||||
#================
|
||||
corr_conservation_cols = c( static_cols
|
||||
, "ConSurf"
|
||||
, "SNAP2"
|
||||
, "PROVEAN"
|
||||
#, aff_dist_cols
|
||||
, "dst_mode"
|
||||
)
|
||||
|
||||
corr_df_cons = corr_plotdf[, corr_conservation_cols]
|
||||
|
||||
# Plot #2
|
||||
plot_corr_df_cons = my_gg_pairs(corr_df_cons, plot_title="Conservation estimates")
|
||||
|
||||
##########################################################
|
||||
#================
|
||||
# Affinity: lig, ppi and na as applicable
|
||||
#================
|
||||
#corr_df_lig = corr_plotdf[corr_plotdf["Lig-Dist"]<DistCutOff,]
|
||||
common_aff_colnames = c("mCSM-lig"
|
||||
, "mmCSM-lig")
|
||||
|
||||
if (tolower(gene)%in%geneL_normal){
|
||||
aff_colnames = common_aff_colnames
|
||||
}
|
||||
if (tolower(gene)%in%geneL_ppi2){
|
||||
aff_colnames = c(common_aff_colnames, "mCSM-PPI2")
|
||||
}
|
||||
|
||||
if (tolower(gene)%in%geneL_na){
|
||||
aff_colnames = c(common_aff_colnames, "mCSM-NA")
|
||||
}
|
||||
|
||||
# building ffinal affinity colnames for correlation
|
||||
corr_aff_colnames = c(static_cols
|
||||
, aff_colnames
|
||||
, "dst_mode") # imp
|
||||
|
||||
corr_df_aff = corr_affinity_df[, corr_aff_colnames]
|
||||
colnames(corr_df_aff)
|
||||
|
||||
# Plot #3
|
||||
plot_corr_df_aff = my_gg_pairs(corr_df_aff
|
||||
, plot_title="Affinity estimates"
|
||||
#, tt_args_size = 4
|
||||
#, gp_args_size = 4
|
||||
)
|
||||
|
||||
#### Combine plots #####
|
||||
# #png("/home/tanu/tmp/gg_pairs_all.png", height = 6, width=11.75, unit="in",res=300)
|
||||
# png(paste0(outdir_images
|
||||
# ,tolower(gene)
|
||||
# ,"_CorrAB.png"), height = 6, width=11.75, unit="in",res=300)
|
||||
#
|
||||
# cowplot::plot_grid(ggmatrix_gtable(plot_corr_df_ps),
|
||||
# ggmatrix_gtable(plot_corr_df_cons),
|
||||
# # ggmatrix_gtable(plot_corr_df_aff),
|
||||
# # nrow=1, ncol=3, rel_heights = 7,7,3
|
||||
# nrow=1,
|
||||
# #rel_heights = 1,1
|
||||
# labels = "AUTO",
|
||||
# label_size = 12)
|
||||
# dev.off()
|
||||
#
|
||||
# # affinity corr
|
||||
# #png("/home/tanu/tmp/gg_pairs_affinity.png", height =7, width=7, unit="in",res=300)
|
||||
# png(paste0(outdir_images
|
||||
# ,tolower(gene)
|
||||
# ,"_CorrC.png"), height =7, width=7, unit="in",res=300)
|
||||
#
|
||||
# cowplot::plot_grid(ggmatrix_gtable(plot_corr_df_aff),
|
||||
# labels = "C",
|
||||
# label_size = 12)
|
||||
# dev.off()
|
||||
|
||||
#### Combine A ####
|
||||
png(paste0(outdir_images
|
||||
,tolower(gene)
|
||||
,"_CorrA.png"), height =8, width=8, unit="in",res=300)
|
||||
|
||||
cowplot::plot_grid(ggmatrix_gtable(plot_corr_df_ps),
|
||||
labels = "A",
|
||||
label_size = 12)
|
||||
dev.off()
|
||||
|
||||
#### Combine B+C ####
|
||||
# B + C
|
||||
png(paste0(outdir_images
|
||||
,tolower(gene)
|
||||
,"_CorrBC.png"), height = 6, width=11.75, unit="in",res=300)
|
||||
|
||||
cowplot::plot_grid(ggmatrix_gtable(plot_corr_df_cons),
|
||||
ggmatrix_gtable(plot_corr_df_aff),
|
||||
# ggmatrix_gtable(plot_corr_df_aff),
|
||||
# nrow=1, ncol=3, rel_heights = 7,7,3
|
||||
nrow=1,
|
||||
#rel_heights = 1,1
|
||||
labels = c("B", "C"),
|
||||
label_size = 12)
|
||||
dev.off()
|
||||
|
||||
2
scripts/plotting/plotting_thesis/rpob/katg_other_plots.R
Normal file
2
scripts/plotting/plotting_thesis/rpob/katg_other_plots.R
Normal file
|
|
@ -0,0 +1,2 @@
|
|||
|
||||
source("/home/tanu/git/LSHTM_analysis/scripts/plotting/plotting_thesis/linage_bp_dist_layout.R")
|
||||
172
scripts/plotting/plotting_thesis/rpob/linage_bp_dist.R
Normal file
172
scripts/plotting/plotting_thesis/rpob/linage_bp_dist.R
Normal file
|
|
@ -0,0 +1,172 @@
|
|||
#!/usr/bin/env Rscript
|
||||
|
||||
#########################################################
|
||||
# TASK: Lineage plots [merged_df2]
|
||||
# Count
|
||||
# Diversity
|
||||
# Average stability dist
|
||||
# Avergae affinity dist: optional
|
||||
#########################################################
|
||||
#=======
|
||||
# output
|
||||
#=======
|
||||
# outdir_images = paste0("~/git/Writing/thesis/images/results/"
|
||||
# , tolower(gene), "/")
|
||||
# cat("plots will output to:", outdir_images)
|
||||
#########################################################
|
||||
|
||||
#===============
|
||||
#Quick numbers checks
|
||||
#===============
|
||||
nsample_lin = merged_df2[merged_df2$lineage%in%c("L1", "L2", "L3", "L4"),]
|
||||
|
||||
if ( all(table(nsample_lin$sensitivity)== table(nsample_lin$mutation_info_labels)) ){
|
||||
cat("\nTotal no. of samples belonging to L1-l4 for", gene,":", nrow(nsample_lin)
|
||||
, "\nCounting R and S samples")
|
||||
if( sum(table(nsample_lin$sensitivity)) == nrow(nsample_lin) ){
|
||||
cat("\nPASSNumbers cross checked:")
|
||||
print(table(nsample_lin$sensitivity))
|
||||
}
|
||||
}else{
|
||||
stop("Abort: Numbers mismatch. Please check")
|
||||
}
|
||||
########################################################################
|
||||
###################################################
|
||||
# Lineage barplots #
|
||||
###################################################
|
||||
|
||||
#===============================
|
||||
# lineage sample and SNP count
|
||||
#===============================
|
||||
lin_countP = lin_count_bp(lf_data = lineage_dfL[['lin_lf']]
|
||||
, all_lineages = F
|
||||
, x_categ = "sel_lineages"
|
||||
, y_count = "p_count"
|
||||
, use_lineages = c("L1", "L2", "L3", "L4")
|
||||
, bar_fill_categ = "count_categ"
|
||||
, display_label_col = "p_count"
|
||||
, bar_stat_stype = "identity"
|
||||
, d_lab_size = 8
|
||||
, d_lab_col = "black"
|
||||
, my_xats = 25 # x axis text size
|
||||
, my_yats = 25 # y axis text sized_lab_size
|
||||
, my_xals = 25 # x axis label size
|
||||
, my_yals = 25 # y axis label size
|
||||
, my_lls = 25 # legend label size
|
||||
, bar_col_labels = c("SNPs", "Total Samples")
|
||||
, bar_col_values = c("grey50", "gray75")
|
||||
, bar_leg_name = ""
|
||||
, leg_location = "top"
|
||||
, y_log10 = F
|
||||
, y_scale_percent = FALSE
|
||||
, y_label = c("Count")
|
||||
)
|
||||
lin_countP
|
||||
#===============================
|
||||
# lineage SNP diversity count
|
||||
#===============================
|
||||
lin_diversityP = lin_count_bp_diversity(lf_data = lineage_dfL[['lin_wf']]
|
||||
, x_categ = "sel_lineages"
|
||||
, y_count = "snp_diversity"
|
||||
#, all_lineages = F
|
||||
, use_lineages = c("L1", "L2", "L3", "L4")
|
||||
, display_label_col = "snp_diversity_f"
|
||||
, bar_stat_stype = "identity"
|
||||
, x_lab_angle = 90
|
||||
, d_lab_size =9
|
||||
, my_xats = 25 # x axis text size
|
||||
, my_yats = 25 # y axis text size
|
||||
, my_xals = 25 # x axis label size
|
||||
, my_yals = 25 # y axis label size
|
||||
, my_lls = 25 # legend label size
|
||||
, y_log10 = F
|
||||
, y_scale_percent = F
|
||||
, leg_location = "top"
|
||||
, y_label = "Percent" #"SNP diversity"
|
||||
, bp_plot_title = "nsSNP diversity"
|
||||
, title_colour = "black" #"chocolate4"
|
||||
, subtitle_text = NULL
|
||||
, sts = 20
|
||||
, subtitle_colour = "#350E20FF")
|
||||
lin_diversityP
|
||||
#=============================================
|
||||
# Output plots: Lineage count and Diversity
|
||||
#=============================================
|
||||
# lineage_bp_CL = paste0(outdir_images
|
||||
# ,tolower(gene)
|
||||
# ,"_lineage_bp_CL_Tall.svg")
|
||||
#
|
||||
# cat("Lineage barplots:", lineage_bp_CL)
|
||||
# svg(lineage_bp_CL, width = 8, height = 15)
|
||||
#
|
||||
# cowplot::plot_grid(lin_countP, lin_diversityP
|
||||
# #, labels = c("(a)", "(b)", "(c)", "(d)")
|
||||
# , nrow = 2
|
||||
# # , ncols = 2
|
||||
# , labels = "AUTO"
|
||||
# , label_size = 25)
|
||||
#
|
||||
# dev.off()
|
||||
########################################################################
|
||||
|
||||
|
||||
###################################################
|
||||
# Stability dist #
|
||||
###################################################
|
||||
# scaled_cols_stability = c("duet_scaled"
|
||||
# , "deepddg_scaled"
|
||||
# , "ddg_dynamut2_scaled"
|
||||
# , "foldx_scaled"
|
||||
# , "avg_stability_scaled")
|
||||
|
||||
my_xlabel = paste0("Average stability ", "(", stability_suffix, ")"); my_xlabel
|
||||
#plotdf = merged_df2[merged_df2$lineage%in%c("L1", "L2", "L3", "L4"),]
|
||||
|
||||
linP_dm_om = lineage_distP(merged_df2
|
||||
, with_facet = F
|
||||
, x_axis = "avg_stability_scaled"
|
||||
, y_axis = "lineage_labels"
|
||||
, x_lab = my_xlabel
|
||||
, use_lineages = c("L1", "L2", "L3", "L4")
|
||||
#, fill_categ = "mutation_info_orig", fill_categ_cols = c("#E69F00", "#999999")
|
||||
, fill_categ = "sensitivity"
|
||||
, fill_categ_cols = c("red", "blue")
|
||||
, label_categories = c("Resistant", "Sensitive")
|
||||
, leg_label = "Mutation group"
|
||||
, my_ats = 22 # axis text size
|
||||
, my_als = 22 # axis label size
|
||||
, my_leg_ts = 22
|
||||
, my_leg_title = 22
|
||||
, my_strip_ts = 22
|
||||
, alpha = 0.56
|
||||
)
|
||||
|
||||
linP_dm_om
|
||||
|
||||
###################################################
|
||||
# Affinity dist [OPTIONAL] #
|
||||
###################################################
|
||||
# scaled_cols_affinity = c("affinity_scaled"
|
||||
# , "mmcsm_lig_scaled"
|
||||
# , "mcsm_ppi2_scaled"
|
||||
# , "mcsm_na_scaled"
|
||||
# , "avg_lig_affinity_scaled")
|
||||
|
||||
# lineage_distP(merged_df2
|
||||
# , with_facet = F
|
||||
# , x_axis = "avg_lig_affinity_scaled"
|
||||
# , y_axis = "lineage_labels"
|
||||
# , x_lab = my_xlabel
|
||||
# , use_lineages = c("L1", "L2", "L3", "L4")
|
||||
# #, fill_categ = "mutation_info_orig", fill_categ_cols = c("#E69F00", "#999999")
|
||||
# , fill_categ = "sensitivity"
|
||||
# , fill_categ_cols = c("red", "blue")
|
||||
# , label_categories = c("Resistant", "Sensitive")
|
||||
# , leg_label = "Mutation group"
|
||||
# , my_ats = 22 # axis text size
|
||||
# , my_als = 22 # axis label size
|
||||
# , my_leg_ts = 22
|
||||
# , my_leg_title = 22
|
||||
# , my_strip_ts = 22
|
||||
# , alpha = 0.56
|
||||
# )
|
||||
|
|
@ -0,0 +1,30 @@
|
|||
#!/usr/bin/env Rscript
|
||||
|
||||
###########################################
|
||||
# TASK: generate plots for lineage
|
||||
# Individual plots in
|
||||
#lineage_bp_both.R
|
||||
#linage_dist_ens_stability.R
|
||||
###########################################
|
||||
my_label_size = 25
|
||||
|
||||
linPlots_combined = paste0(outdir_images
|
||||
, tolower(gene)
|
||||
,"_linP_combined.svg")
|
||||
|
||||
cat("\nOutput plot:", linPlots_combined)
|
||||
svg(linPlots_combined, width = 18, height = 12)
|
||||
|
||||
cowplot::plot_grid(
|
||||
cowplot::plot_grid(lin_countP, lin_diversityP
|
||||
, nrow = 2
|
||||
, labels = "AUTO"
|
||||
, label_size = my_label_size),
|
||||
NULL,
|
||||
linP_dm_om,
|
||||
nrow = 1,
|
||||
labels = c("", "", "C"),
|
||||
label_size = my_label_size,
|
||||
rel_widths = c(35, 3, 52)
|
||||
)
|
||||
dev.off()
|
||||
179
scripts/plotting/plotting_thesis/rpob/pe_sens_site_count_rpob.R
Normal file
179
scripts/plotting/plotting_thesis/rpob/pe_sens_site_count_rpob.R
Normal file
|
|
@ -0,0 +1,179 @@
|
|||
source("/home/tanu/git/LSHTM_analysis/scripts/plotting/plotting_thesis/prominent_effects_rpob.R")
|
||||
source("/home/tanu/git/LSHTM_analysis/scripts/plotting/plotting_thesis/sensitivity_count_rpob.R")
|
||||
|
||||
##############################################################
|
||||
# PE count
|
||||
#lig-- na--ppi2--stab
|
||||
# pe_colour_map = c("DD_lig" = "#ffd700" # gold
|
||||
# , "SS_lig" = "#f0e68c" # khaki
|
||||
#
|
||||
# , "DD_nucleic_acid"= "#a0522d" # sienna
|
||||
# , "SS_nucleic_acid"= "#d2b48c" # tan
|
||||
#
|
||||
# , "DD_ppi2" = "#da70d6" # orchid
|
||||
# , "SS_ppi2" = "#ff1493" # deeppink
|
||||
#
|
||||
# , "DD_stability" = "#f8766d" # red
|
||||
# , "SS_stability" = "#00BFC4") # blue
|
||||
table(str_df_plot_cols$pe_effect_outcome)
|
||||
##############################################################
|
||||
#===========
|
||||
#PE count
|
||||
#===========
|
||||
rects <- data.frame(x=1:8,
|
||||
colors = c("#ffd700" ,
|
||||
"#f0e68c" ,
|
||||
|
||||
"#a0522d" ,
|
||||
"#d2b48c" ,
|
||||
|
||||
"#da70d6" ,
|
||||
"#ff1493" ,
|
||||
|
||||
"#f8766d" ,
|
||||
"#00BFC4")
|
||||
)
|
||||
|
||||
rects$text = c("-ve Lig"
|
||||
, "+ve Lig"
|
||||
|
||||
, "-ve\nNuc.Acid"
|
||||
, "+ve\nNuc.Acid"
|
||||
|
||||
, "-ve PPI2"
|
||||
, "+ve PPI2"
|
||||
|
||||
, "-ve stability"
|
||||
, "+ve stability"
|
||||
)
|
||||
|
||||
cell1 = table(str_df_plot_cols$pe_effect_outcome)[["DD_lig"]]
|
||||
cell2 = table(str_df_plot_cols$pe_effect_outcome)[["SS_lig"]]
|
||||
|
||||
cell3 = table(str_df_plot_cols$pe_effect_outcome)[["DD_nucleic_acid"]]
|
||||
cell4 = table(str_df_plot_cols$pe_effect_outcome)[["SS_nucleic_acid"]]
|
||||
|
||||
cell5 = table(str_df_plot_cols$pe_effect_outcome)[["DD_ppi2"]]
|
||||
cell6 = table(str_df_plot_cols$pe_effect_outcome)[["SS_ppi2"]]
|
||||
|
||||
cell7 = table(str_df_plot_cols$pe_effect_outcome)[["DD_stability"]]
|
||||
cell8 = table(str_df_plot_cols$pe_effect_outcome)[["SS_stability"]]
|
||||
|
||||
|
||||
#rects$numbers = c(38, 0, 22, 9, 108, 681) #for embb
|
||||
rects$numbers = c(cell1, cell2,
|
||||
cell3, cell4,
|
||||
cell5, cell6,
|
||||
cell7, cell8)
|
||||
|
||||
rects$num_labels = paste0("n=", rects$numbers)
|
||||
|
||||
rects
|
||||
#------
|
||||
# Plot
|
||||
#------
|
||||
#https://stackoverflow.com/questions/47986055/create-a-rectangle-filled-with-text
|
||||
peP = ggplot(rects, aes(x, y = 0, fill = colors, label = paste0(text,"\n", num_labels))) +
|
||||
geom_tile(width = 1, height = 1) + # make square tiles
|
||||
geom_text(color = "black", size = 1.7) + # add white text in the middle
|
||||
scale_fill_identity(guide = "none") + # color the tiles with the colors in the data frame
|
||||
coord_fixed() + # make sure tiles are square
|
||||
coord_flip()+ scale_x_reverse() +
|
||||
# theme_void() # remove any axis markings
|
||||
theme_nothing() # remove any axis markings
|
||||
peP
|
||||
|
||||
#------
|
||||
# Plot: this one is better
|
||||
#------
|
||||
peP2 = ggplot(rects, aes(x, y = 0, fill = colors, label = paste0(text,"\n", num_labels))) +
|
||||
geom_tile() + # make square tiles
|
||||
geom_text(color = "black", size = 1.6) + # add white text in the middle
|
||||
scale_fill_identity(guide = "none") + # color the tiles with the colors in the data frame
|
||||
coord_fixed() + # make sure tiles are square
|
||||
theme_nothing() # remove any axis markings
|
||||
peP2
|
||||
|
||||
########################################################
|
||||
# From: script sensitivity_count per gene
|
||||
#===============================
|
||||
# Sensitivity count: SITE
|
||||
#===============================
|
||||
#--------
|
||||
# embb
|
||||
#--------
|
||||
#rsc = 54
|
||||
#ccc = 46
|
||||
#ssc = 470
|
||||
|
||||
rsc = site_Rc; rsc
|
||||
ccc = site_Cc; ccc
|
||||
ssc = site_Sc; ssc
|
||||
|
||||
rect_rs_siteC <- data.frame(x=1:3,
|
||||
colors = c("red",
|
||||
"purple",
|
||||
"blue")
|
||||
)
|
||||
|
||||
rect_rs_siteC
|
||||
rect_rs_siteC$text = c("Resistant",
|
||||
"Common",
|
||||
"Sensitive")
|
||||
|
||||
rect_rs_siteC$numbers = c(rsc,ccc,ssc)
|
||||
rect_rs_siteC$num_labels = paste0("n=", rect_rs_siteC$numbers)
|
||||
rect_rs_siteC
|
||||
|
||||
#------
|
||||
# Plot
|
||||
#------
|
||||
sens_siteP = ggplot(rect_rs_siteC, aes(x, y = 0,
|
||||
fill = colors,
|
||||
label = num_labels
|
||||
#,label = paste0(text,"\n", num_labels)
|
||||
)) +
|
||||
geom_tile(width = 1, height = 1) +
|
||||
#geom_text(color = "black", size = 1.7) +
|
||||
geom_label(color = "black", size = 1.7,fill = "white", alpha=0.7) +
|
||||
scale_fill_identity(guide = "none") +
|
||||
coord_fixed()+
|
||||
theme_nothing() # remove any axis markings
|
||||
sens_siteP
|
||||
|
||||
################################################################
|
||||
#===============================
|
||||
# Sensitivity count: Mutations
|
||||
#===============================
|
||||
table(sensP_df$sensitivity)
|
||||
muts_Rc = table(sensP_df$sensitivity)[["R"]]
|
||||
muts_Sc = table(sensP_df$sensitivity)[["S"]]
|
||||
rect_sens <- data.frame(x=1:2,
|
||||
colors = c("red",
|
||||
"blue")
|
||||
)
|
||||
|
||||
rect_sens$text = c("Resistant",
|
||||
"Sensitive")
|
||||
rect_sens$numbers = c(muts_Rc,muts_Sc)
|
||||
rect_sens$num_labels = paste0("n=", rect_sens$numbers)
|
||||
rect_sens
|
||||
#------
|
||||
# Plot
|
||||
#------
|
||||
sensP = ggplot(rect_sens, aes(x, y = 0,
|
||||
fill = colors,
|
||||
label = paste0(text,"\n", num_labels))) +
|
||||
geom_tile(width = 1, height = 1) +
|
||||
#geom_text(color = "black", size = 1.7) +
|
||||
geom_label(color = "black", size = 1.7,fill = "white", alpha=0.7) +
|
||||
scale_fill_identity(guide = "none") +
|
||||
coord_fixed()+
|
||||
theme_nothing() # remove any axis markings
|
||||
sensP
|
||||
|
||||
sensP2 = sensP +
|
||||
coord_flip() + scale_x_reverse()
|
||||
sensP2
|
||||
|
||||
|
||||
398
scripts/plotting/plotting_thesis/rpob/prominent_effects_rpob.R
Normal file
398
scripts/plotting/plotting_thesis/rpob/prominent_effects_rpob.R
Normal file
|
|
@ -0,0 +1,398 @@
|
|||
#!/usr/bin/env Rscript
|
||||
#source("~/git/LSHTM_analysis/config/alr.R")
|
||||
#source("~/git/LSHTM_analysis/config/embb.R")
|
||||
#source("~/git/LSHTM_analysis/config/katg.R")
|
||||
#source("~/git/LSHTM_analysis/config/gid.R")
|
||||
#source("~/git/LSHTM_analysis/config/pnca.R")
|
||||
#source("~/git/LSHTM_analysis/config/rpob.R")
|
||||
|
||||
# get plotting dfs
|
||||
#source("~/git/LSHTM_analysis/scripts/plotting/get_plotting_dfs.R")
|
||||
########################################################
|
||||
if (length(merged_df3$position) == length(merged_df3$X5uhc_position) ){
|
||||
pos_colname = "X5uhc_position"
|
||||
}else{
|
||||
stop("Abort: position colname could not be found")
|
||||
}
|
||||
|
||||
#-------------
|
||||
# from ~/git/LSHTM_analysis/scripts/plotting/plotting_colnames.R
|
||||
#-------------
|
||||
length(all_stability_cols); length(raw_stability_cols)
|
||||
length(scaled_stability_cols); length(outcome_stability_cols)
|
||||
length(affinity_dist_colnames)
|
||||
|
||||
|
||||
static_cols = c("mutationinformation",
|
||||
#"position",
|
||||
pos_colname,
|
||||
"sensitivity")
|
||||
|
||||
other_cols_all = c(scaled_stability_cols, scaled_affinity_cols, affinity_dist_colnames)
|
||||
|
||||
#omit avg cols and foldx_scaled_signC cols
|
||||
other_cols = other_cols_all[grep("avg", other_cols_all, invert = T)]
|
||||
other_cols = other_cols[grep("foldx_scaled_signC",other_cols, invert = T )]
|
||||
other_cols
|
||||
|
||||
cols_to_extract = c(static_cols, other_cols)
|
||||
cat("\nExtracting cols:", cols_to_extract)
|
||||
expected_ncols = length(static_cols) + length(other_cols)
|
||||
expected_ncols
|
||||
|
||||
str_df = merged_df3[, cols_to_extract]
|
||||
|
||||
if (ncol(str_df) == expected_ncols){
|
||||
cat("\nPASS: successfully extracted cols for calculating prominent effects")
|
||||
}else{
|
||||
stop("\nAbort: Could not extract cols for calculating prominent effects")
|
||||
}
|
||||
|
||||
#=========================
|
||||
# Masking affinity columns
|
||||
#=========================
|
||||
# First make values for affinity cols 0 when their corresponding dist >10
|
||||
head(str_df)
|
||||
|
||||
# replace in place affinity values >10
|
||||
str_df[str_df["ligand_distance"]>10,"affinity_scaled"]=0
|
||||
str_df[str_df["ligand_distance"]>10,"mmcsm_lig_scaled"]=0
|
||||
|
||||
#ppi2 gene: replace in place ppi2 affinity values where ppi2 dist >10
|
||||
if (tolower(gene)%in%geneL_ppi2){
|
||||
str_df[str_df["interface_dist"]>10,"mcsm_ppi2_scaled"]=0
|
||||
}
|
||||
|
||||
# na gene: replace in place na affinity values where na dist >10
|
||||
if (tolower(gene)%in%geneL_na){
|
||||
str_df[str_df["nca_distance"]>10,"mcsm_na_scaled"]=0
|
||||
}
|
||||
|
||||
colnames(str_df)
|
||||
head(str_df)
|
||||
|
||||
# get names of cols to calculate the prominent effects from
|
||||
# scaled_cols_tc = c("duet_scaled",
|
||||
# "deepddg_scaled",
|
||||
# "ddg_dynamut2_scaled",
|
||||
# "foldx_scaled",
|
||||
# "affinity_scaled",
|
||||
# "mmcsm_lig_scaled",
|
||||
# "mcsm_ppi2_scaled", "mcsm_na_scaled")
|
||||
|
||||
scaled_cols_tc = other_cols[grep("scaled", other_cols)]
|
||||
|
||||
#--------------------------------
|
||||
#get rowmax for absolute values
|
||||
#--------------------------------
|
||||
#str_df$row_maximum = apply(str_df[,-1], 1, function(x){max(abs(x))})
|
||||
#str_df$row_maximum = apply(str_df[,scaled_cols_tc], 1, function(x){max(abs(x))})
|
||||
|
||||
#correct
|
||||
#BOO= abs(str_df[,scaled_cols_tc]) == str_df[,'row_maximum']; head(BOO)
|
||||
#also corr but weird
|
||||
#POO = which(abs(str_df[,scaled_cols_tc]) == str_df[,'row_maximum'], arr.ind =T); head(POO)
|
||||
|
||||
################################################
|
||||
|
||||
|
||||
#===============
|
||||
# whole df
|
||||
#===============
|
||||
give_col=function(x,y,df=str_df){
|
||||
df[df[[pos_colname]]==x,y]
|
||||
}
|
||||
|
||||
for (i in unique(str_df[[pos_colname]]) ){
|
||||
print(i)
|
||||
#cat(length(unique(str_df[[pos_colname]])))
|
||||
|
||||
biggest = max(abs(give_col(i,scaled_cols_tc)))
|
||||
|
||||
str_df[str_df[[pos_colname]]==i,'abs_max_effect'] = biggest
|
||||
str_df[str_df[[pos_colname]]==i,'effect_type']= names(
|
||||
give_col(i,scaled_cols_tc)[which(
|
||||
abs(
|
||||
give_col(i,scaled_cols_tc)
|
||||
) == biggest, arr.ind=T
|
||||
)[, "col"]])[1]
|
||||
|
||||
effect_name = unique(str_df[str_df[[pos_colname]]==i,'effect_type'])#[1] # pick first one in case we have multiple exact values
|
||||
|
||||
# get index/rowname for value of max effect, and then use it to get the original sign
|
||||
# here
|
||||
#ind = rownames(which(abs(str_df[str_df[[pos_colname]]==i,c('position',effect_name)][effect_name])== biggest, arr.ind=T))
|
||||
ind = rownames(which(abs(str_df[str_df[[pos_colname]]==i,c(pos_colname,effect_name)][effect_name])== biggest, arr.ind=T))
|
||||
|
||||
str_df[str_df[[pos_colname]]==i,'effect_sign'] = sign(str_df[effect_name][ind,])[1]
|
||||
}
|
||||
|
||||
# ends with suffix 2 if dups
|
||||
str_df$effect_type = sub("\\.[0-9]+", "", str_df$effect_type) # cull duplicate effect types that happen when there are exact duplicate values
|
||||
colnames(str_df)
|
||||
table(str_df$effect_type)
|
||||
|
||||
# check
|
||||
str_df_check = str_df[str_df[[pos_colname]]%in%c(24, 32, 160, 303, 334),]
|
||||
|
||||
#================
|
||||
# for Plots
|
||||
#================
|
||||
str_df_short = str_df[, c("mutationinformation",
|
||||
#"position",
|
||||
pos_colname,
|
||||
"sensitivity"
|
||||
, "effect_type"
|
||||
, "effect_sign")]
|
||||
|
||||
table(str_df_short$effect_type)
|
||||
table(str_df_short$effect_sign)
|
||||
str(str_df_short)
|
||||
|
||||
# assign pe outcome
|
||||
str_df_short$pe_outcome = ifelse(str_df_short$effect_sign<0, "DD", "SS")
|
||||
table(str_df_short$pe_outcome )
|
||||
table(str_df_short$effect_sign)
|
||||
|
||||
#==============
|
||||
# group effect type:
|
||||
# lig, ppi2, nuc. acid, stability
|
||||
#==============
|
||||
affcols = c("affinity_scaled", "mmcsm_lig_scaled")
|
||||
ppi2_cols = c("mcsm_ppi2_scaled")
|
||||
nuc_na_cols = c("mcsm_na_scaled")
|
||||
|
||||
|
||||
#lig
|
||||
table(str_df_short$effect_type)
|
||||
str_df_short$effect_grouped = ifelse(str_df_short$effect_type%in%affcols
|
||||
, "lig"
|
||||
, str_df_short$effect_type)
|
||||
table(str_df_short$effect_grouped)
|
||||
|
||||
#ppi2
|
||||
str_df_short$effect_grouped = ifelse(str_df_short$effect_grouped%in%ppi2_cols
|
||||
, "ppi2"
|
||||
, str_df_short$effect_grouped)
|
||||
table(str_df_short$effect_grouped)
|
||||
|
||||
|
||||
#na
|
||||
str_df_short$effect_grouped = ifelse(str_df_short$effect_grouped%in%nuc_na_cols
|
||||
, "nucleic_acid"
|
||||
, str_df_short$effect_grouped)
|
||||
table(str_df_short$effect_grouped)
|
||||
|
||||
#stability
|
||||
str_df_short$effect_grouped = ifelse(!str_df_short$effect_grouped%in%c("lig",
|
||||
"ppi2",
|
||||
"nucleic_acid")
|
||||
, "stability"
|
||||
, str_df_short$effect_grouped)
|
||||
|
||||
table(str_df_short$effect_grouped)
|
||||
|
||||
# create a sign as well
|
||||
str_df_short$pe_effect_outcome = paste0(str_df_short$pe_outcome, "_"
|
||||
, str_df_short$effect_grouped)
|
||||
|
||||
table(str_df_short$pe_effect_outcome)
|
||||
|
||||
#####################################################################
|
||||
# Chimera: for colouring
|
||||
####################################################################
|
||||
|
||||
#-------------------------------------
|
||||
# get df with unique position
|
||||
#pos_colname = "position"
|
||||
if (length(merged_df3[[pos_colname]]) == length(merged_df3$X5uhc_position) ){
|
||||
pos_colname = "X5uhc_position"
|
||||
}else{
|
||||
stop("Abort: position colname could not be found")
|
||||
}
|
||||
#--------------------------------------
|
||||
#data[!duplicated(data$x), ]
|
||||
str_df_plot = str_df_short[!duplicated(str_df[[pos_colname]]),]
|
||||
|
||||
if (nrow(str_df_plot) == length(unique(str_df[[pos_colname]]))){
|
||||
cat("\nPASS: successfully extracted df with unique positions")
|
||||
}else{
|
||||
stop("\nAbort: Could not extract df with unique positions")
|
||||
}
|
||||
|
||||
#-------------------------------------
|
||||
# generate colours for effect types
|
||||
#--------------------------------------
|
||||
str_df_plot_cols = str_df_plot[, c(pos_colname,
|
||||
"sensitivity",
|
||||
"pe_outcome",
|
||||
"effect_grouped",
|
||||
"pe_effect_outcome")]
|
||||
head(str_df_plot_cols)
|
||||
|
||||
# colour intensity based on sign
|
||||
#str_df_plot_cols$colour_hue = ifelse(str_df_plot_cols$effect_sign<0, "bright", "light")
|
||||
str_df_plot_cols$colour_hue = ifelse(str_df_plot_cols$pe_outcome=="DD", "bright", "light")
|
||||
|
||||
table(str_df_plot_cols$colour_hue); table(str_df_plot$pe_outcome)
|
||||
head(str_df_plot_cols)
|
||||
|
||||
# colour based on effect
|
||||
table(str_df_plot_cols$pe_effect_outcome)
|
||||
|
||||
# colors = c("#ffd700" #gold
|
||||
# , "#f0e68c" #khaki
|
||||
# , "#da70d6"# orchid
|
||||
# , "#ff1493"# deeppink
|
||||
# , "#a0522d" #sienna
|
||||
# , "#d2b48c" # tan
|
||||
# , "#00BFC4" #, "#007d85" #blue
|
||||
# , "#F8766D" )# red
|
||||
|
||||
pe_colour_map = c("DD_lig" = "#ffd700" # gold
|
||||
, "SS_lig" = "#f0e68c" # khaki
|
||||
|
||||
, "DD_nucleic_acid"= "#a0522d" # sienna
|
||||
, "SS_nucleic_acid"= "#d2b48c" # tan
|
||||
|
||||
, "DD_ppi2" = "#da70d6" # orchid
|
||||
, "SS_ppi2" = "#ff1493" # deeppink
|
||||
|
||||
, "DD_stability" = "#f8766d" # red
|
||||
, "SS_stability" = "#00BFC4") # blue
|
||||
|
||||
#unlist(d[c('a', 'a', 'c', 'b')], use.names=FALSE)
|
||||
|
||||
#map the colours
|
||||
str_df_plot_cols$colour_map= unlist(map(str_df_plot_cols$pe_effect_outcome
|
||||
,function(x){pe_colour_map[[x]]}
|
||||
))
|
||||
head(str_df_plot_cols$colour_map)
|
||||
table(str_df_plot_cols$colour_map)
|
||||
table(str_df_plot_cols$pe_effect_outcome)
|
||||
|
||||
# str_df_plot_cols$colours = paste0(str_df_plot_cols$colour_hue
|
||||
# , "_"
|
||||
# , str_df_plot_cols$colour_map)
|
||||
# head(str_df_plot_cols$colours)
|
||||
# table(str_df_plot_cols$colours)
|
||||
#
|
||||
#
|
||||
# class(str_df_plot_cols$colour_map)
|
||||
# str(str_df_plot_cols)
|
||||
|
||||
# sort by colour
|
||||
head(str_df_plot_cols)
|
||||
str_df_plot_cols = str_df_plot_cols[order(str_df_plot_cols$colour_map), ]
|
||||
head(str_df_plot_cols)
|
||||
|
||||
#======================================
|
||||
# write file with prominent effects
|
||||
#======================================
|
||||
outdir_images = paste0("~/git/Writing/thesis/images/results/", tolower(gene), "/")
|
||||
write.csv(str_df_plot_cols, paste0(outdir_images, tolower(gene), "_prominent_effects.csv"))
|
||||
|
||||
################################
|
||||
# printing for chimera
|
||||
###############################
|
||||
str_df_plot_cols$pos_chain = paste0(str_df_plot_cols[[pos_colname]], ".C,")
|
||||
table(str_df_plot_cols$colour_map)
|
||||
table(str_df_plot_cols$pe_effect_outcome)
|
||||
|
||||
#===================================================
|
||||
#-------------------
|
||||
# Ligand Affinity
|
||||
#-------------------
|
||||
# -ve Lig Aff
|
||||
dd_lig = str_df_plot_cols[str_df_plot_cols$pe_effect_outcome=="DD_lig",]
|
||||
if (nrow(dd_lig) == table(str_df_plot_cols$pe_effect_outcome)[['DD_lig']]){
|
||||
dd_lig_pos = dd_lig[[pos_colname]]
|
||||
}else{
|
||||
stop("Abort: DD affinity colour numbers mismtatch")
|
||||
}
|
||||
|
||||
# +ve Lig Aff
|
||||
ss_lig = str_df_plot_cols[str_df_plot_cols$pe_effect_outcome=="SS_lig",]
|
||||
if (nrow(ss_lig) == table(str_df_plot_cols$pe_effect_outcome)[['SS_lig']]){
|
||||
ss_lig_pos = ss_lig[[pos_colname]]
|
||||
}else{
|
||||
stop("Abort: SS affinity colour numbers mismtatch")
|
||||
}
|
||||
|
||||
#put in chimera cmd
|
||||
paste0(dd_lig_pos, ".C")
|
||||
paste0(ss_lig_pos, ".C")
|
||||
|
||||
#===================================================
|
||||
#------------------------
|
||||
# Nucleic Acid Affinity
|
||||
#------------------------
|
||||
# -ve NA aff
|
||||
dd_nca = str_df_plot_cols[str_df_plot_cols$pe_effect_outcome=="DD_nucleic_acid",]
|
||||
if (nrow(dd_nca) == table(str_df_plot_cols$pe_effect_outcome)[['DD_nucleic_acid']]){
|
||||
dd_nca_pos = dd_nca[[pos_colname]]
|
||||
}else{
|
||||
stop("Abort: DD nucleic_acid colour numbers mismtatch")
|
||||
}
|
||||
|
||||
# +ve NA aff
|
||||
ss_nca = str_df_plot_cols[str_df_plot_cols$pe_effect_outcome=="SS_nucleic_acid",]
|
||||
if (nrow(ss_nca) == table(str_df_plot_cols$pe_effect_outcome)[['SS_nucleic_acid']]){
|
||||
ss_nca_pos = ss_nca[[pos_colname]]
|
||||
}else{
|
||||
stop("Abort: SS nucleic_acid colour numbers mismtatch")
|
||||
}
|
||||
|
||||
#put in chimera cmd
|
||||
paste0(dd_nca_pos, ".C")
|
||||
paste0(ss_nca_pos, ".C")
|
||||
#===================================================
|
||||
#-------------------
|
||||
# PPI2 Affinity
|
||||
#-------------------
|
||||
# -ve PPI2
|
||||
dd_ppi2 = str_df_plot_cols[str_df_plot_cols$pe_effect_outcome=="DD_ppi2",]
|
||||
if (nrow(dd_ppi2) == table(str_df_plot_cols$pe_effect_outcome)[['DD_ppi2']]){
|
||||
dd_ppi2_pos = dd_ppi2[[pos_colname]]
|
||||
}else{
|
||||
stop("Abort: DD PPI2 colour numbers mismtatch")
|
||||
}
|
||||
|
||||
# +ve PPI2
|
||||
ss_ppi2 = str_df_plot_cols[str_df_plot_cols$pe_effect_outcome=="SS_ppi2",]
|
||||
if (nrow(ss_ppi2) == table(str_df_plot_cols$pe_effect_outcome)[['SS_ppi2']]){
|
||||
ss_ppi2_pos = ss_ppi2[[pos_colname]]
|
||||
}else{
|
||||
stop("Abort: SS PPI2 colour numbers mismtatch")
|
||||
}
|
||||
|
||||
#put in chimera cmd
|
||||
paste0(dd_ppi2_pos, ".C")
|
||||
paste0(ss_ppi2_pos, ".C")
|
||||
|
||||
#=========================================================
|
||||
#------------------------
|
||||
# Stability
|
||||
#------------------------
|
||||
# -ve Stability
|
||||
dd_stability = str_df_plot_cols[str_df_plot_cols$pe_effect_outcome=="DD_stability",]
|
||||
if (nrow(dd_stability) == table(str_df_plot_cols$pe_effect_outcome)[['DD_stability']]){
|
||||
dd_stability_pos = dd_stability[[pos_colname]]
|
||||
}else{
|
||||
stop("Abort: DD Stability colour numbers mismtatch")
|
||||
}
|
||||
|
||||
# +ve Stability
|
||||
ss_stability = str_df_plot_cols[str_df_plot_cols$pe_effect_outcome=="SS_stability",]
|
||||
if (nrow(ss_stability) == table(str_df_plot_cols$pe_effect_outcome)[['SS_stability']]){
|
||||
ss_stability_pos = ss_stability[[pos_colname]]
|
||||
}else{
|
||||
stop("Abort: SS Stability colour numbers mismtatch")
|
||||
}
|
||||
|
||||
#put in chimera cmd
|
||||
# stabiliting first as it has less numbers
|
||||
paste0(ss_stability_pos, ".C")
|
||||
paste0(dd_stability_pos, ".C")
|
||||
####################################################################
|
||||
|
||||
|
|
@ -0,0 +1,66 @@
|
|||
#=========================
|
||||
# Count Sensitivity
|
||||
# Mutations and positions
|
||||
#=========================
|
||||
pos_colname_c ="X5uhc_position"
|
||||
#pos_colname_c ="position"
|
||||
|
||||
sensP_df = merged_df3[,c("mutationinformation",
|
||||
#"position",
|
||||
pos_colname_c,
|
||||
"sensitivity")]
|
||||
|
||||
head(sensP_df)
|
||||
table(sensP_df$sensitivity)
|
||||
|
||||
#---------------
|
||||
# Total unique positions
|
||||
#----------------
|
||||
tot_mut_pos = length(unique(sensP_df[[pos_colname_c]]))
|
||||
cat("\nNo of Tot muts sites:", tot_mut_pos)
|
||||
|
||||
# resistant mut pos
|
||||
sens_site_allR = sensP_df[[pos_colname_c]][sensP_df$sensitivity=="R"]
|
||||
sens_site_UR = unique(sens_site_allR)
|
||||
length(sens_site_UR)
|
||||
|
||||
# Sensitive mut pos
|
||||
sens_site_allS = sensP_df[[pos_colname_c]][sensP_df$sensitivity=="S"]
|
||||
sens_site_US = unique(sens_site_allS)
|
||||
length(sens_site_UR)
|
||||
|
||||
#---------------
|
||||
# Common Sites
|
||||
#----------------
|
||||
common_pos = intersect(sens_site_UR,sens_site_US)
|
||||
site_Cc = length(common_pos)
|
||||
cat("\nNo of Common sites:", site_Cc
|
||||
, "\nThese are:", common_pos)
|
||||
|
||||
#---------------
|
||||
# Resistant muts
|
||||
#----------------
|
||||
site_R = sens_site_UR[!sens_site_UR%in%common_pos]
|
||||
site_Rc = length(site_R)
|
||||
|
||||
if ( length(sens_site_allR) == table(sensP_df$sensitivity)[['R']] ){
|
||||
cat("\nNo of R muts:", length(sens_site_allR)
|
||||
, "\nNo. of R sites:",site_Rc
|
||||
, "\nThese are:", site_R
|
||||
)
|
||||
}
|
||||
|
||||
#---------------
|
||||
# Sensitive muts
|
||||
#----------------
|
||||
site_S = sens_site_US[!sens_site_US%in%common_pos]
|
||||
site_Sc = length(site_S)
|
||||
|
||||
if ( length(sens_site_allS) == table(sensP_df$sensitivity)[['S']] ){
|
||||
cat("\nNo of S muts:", length(sens_site_allS)
|
||||
, "\nNo. of S sites:", site_Sc
|
||||
, "\nThese are:", site_S)
|
||||
}
|
||||
|
||||
#########################
|
||||
|
||||
Loading…
Add table
Add a link
Reference in a new issue