dadded v2 of barplot layput
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7 changed files with 800 additions and 79 deletions
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@ -39,12 +39,12 @@ class(merged_df3)
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merged_df3 = as.data.frame(merged_df3)
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class(df3)
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head(df3$pos_count)
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head(merged_df3$pos_count)
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nc_pc_CHANGE = which(colnames(merged_df3)== "pos_count")
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colnames(merged_df3)[nc_pc_CHANGE] = "df2_pos_count_all"
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head(merged_df3$pos_count)
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head(merged_df3$pos_count_all)
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head(merged_df3$df2_pos_count_all)
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# DROP pos_count column
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# merged_df3$pos_count <-NULL
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@ -247,11 +247,6 @@ snap2P = stability_count_bp(plotdf = df3
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#
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# dev.off()
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###########################################################
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#=========================
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# Affinity outcome
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@ -285,7 +280,7 @@ mLigP = stability_count_bp(plotdf = df3_lig
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, bar_fill_values = c("#F8766D", "#00BFC4")
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, sts = sts
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, subtitle_colour= subtitle_colour
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, bp_plot_title = paste(common_bp_title, "ligand")
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#, bp_plot_title = paste(common_bp_title, "ligand")
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)
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#------------------------------
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@ -304,7 +299,7 @@ mmLigP = stability_count_bp(plotdf = df3_lig
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, bar_fill_values = c("#F8766D", "#00BFC4")
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, sts = sts
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, subtitle_colour= subtitle_colour
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, bp_plot_title = paste(common_bp_title, "ligand")
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#, bp_plot_title = paste(common_bp_title, "ligand")
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)
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#------------------------------
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@ -485,46 +480,43 @@ dev.off()
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#####################################################################
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# test
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setDT(df3)[, pos_count2 := .N, by = .(eval(parse(text = "position")))]
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foo = df3[, c("mutationinformation", "position")]
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df4 = foo[, c("mutationinformation", "position")]
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var_pos = "position"
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df4 =
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df4 %>%
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dplyr::add_count(eval(parse(text = var_pos)))
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class(df4)
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df4 = as.data.frame(df4)
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class(df4)
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nc_change = which(colnames(df4) == "n")
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colnames(df4)[nc_change] <- "pos_count"
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class(df4)
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setDT(df4)[, pos_count2 := .N, by = .(eval(parse(text = "position")))]
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class(df4)
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all(df4$pos_count==df4$pos_count2)
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# %>%
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#group_by(pos_count = position)
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#
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# setDT(df3)[, pos_count2 := .N, by = .(eval(parse(text = "position")))]
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# foo = df3[, c("mutationinformation", "position")]
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# df4 = foo[, c("mutationinformation", "position")]
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#
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#
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# var_pos = "position"
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# df4 =
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# df4 %>%
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# dplyr::group_by(position) %>%
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# count(position)
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foo2 = df4[, c("mutationinformation", "position", "pos_count")]
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# dplyr::add_count(eval(parse(text = var_pos)))
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#
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# class(df4)
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# df4 = as.data.frame(df4)
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# class(df4)
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# nc_change = which(colnames(df4) == "n")
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# colnames(df4)[nc_change] <- "pos_count"
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# class(df4)
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#
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# setDT(df4)[, pos_count2 := .N, by = .(eval(parse(text = "position")))]
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# class(df4)
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#
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# all(df4$pos_count==df4$pos_count2)
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#
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# # %>%
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# #group_by(pos_count = position)
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#
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# # df4 =
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# # df4 %>%
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# # dplyr::group_by(position) %>%
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# # count(position)
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#foo2 = df4[, c("mutationinformation", "position", "pos_count")]
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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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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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@ -541,9 +533,10 @@ posC_lig = site_snp_count_bp(plotdf = df3_lig
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, df_colname = "position"
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, xaxis_title = "Number of nsSNPs"
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, yaxis_title = "Number of Sites"#+ annotate("text", x = 1.5, y = 2.2, label = "Text No. 1")
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, subtitle_text = paste0(common_bp_title, " ligand")
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, subtitle_size = 20
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#, subtitle_text = paste0(common_bp_title, " ligand")
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, subtitle_size = 8
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, subtitle_colour = subtitle_colour)
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posC_lig
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# ------------------------------
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# bp site site count: ppi2
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# < 10 Ang interface
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@ -556,7 +549,7 @@ posC_ppi2 = site_snp_count_bp(plotdf = df3_ppi2
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, subtitle_text = paste0(common_bp_title, " interface")
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, subtitle_size = 20
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, subtitle_colour = subtitle_colour)
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posC_ppi2
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# ------------------------------
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#FIXME: bp site site count: na
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# < 10 Ang TBC
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@ -571,23 +564,23 @@ posC_ppi2 = site_snp_count_bp(plotdf = df3_ppi2
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# output: SITE SNP count:
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# all + affinity
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#==========================
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my_label_size = 25
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pos_count_combined_CLP = paste0(outdir_images
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,tolower(gene)
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,"_pos_count_PS_AFF.svg")
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svg(pos_count_combined_CLP, width = 20, height = 5.5)
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print(paste0("plot filename:", pos_count_combined_CLP))
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cowplot::plot_grid(posC_all, posC_lig, posC_ppi2
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#, posC_na
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, nrow = 1
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, ncol = 3
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, labels = "AUTO"
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, label_size = my_label_size)
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dev.off()
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# my_label_size = 25
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# pos_count_combined_CLP = paste0(outdir_images
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# ,tolower(gene)
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# ,"_pos_count_PS_AFF.svg")
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#
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#
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# svg(pos_count_combined_CLP, width = 20, height = 5.5)
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# print(paste0("plot filename:", pos_count_combined_CLP))
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#
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# cowplot::plot_grid(posC_all, posC_lig, posC_ppi2
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# #, posC_na
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# , nrow = 1
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# , ncol = 3
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# , labels = "AUTO"
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# , label_size = my_label_size)
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#
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# dev.off()
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#===============================================================
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292
scripts/plotting/plotting_thesis/basic_barplots2.R
Normal file
292
scripts/plotting/plotting_thesis/basic_barplots2.R
Normal file
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@ -0,0 +1,292 @@
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#!/usr/bin/env Rscript
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#########################################################
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# TASK: Barplots for mCSM DUET, ligand affinity, and foldX
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# basic barplots with count of mutations
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# basic barplots with frequency of count of mutations
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# , df_colname = ""
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# , leg_title = ""
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# , ats = 25 # axis text size
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# , als = 22 # axis label size
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# , lts = 20 # legend text size
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# , ltis = 22 # label title size
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# , geom_ls = 10 # geom_label size
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# , yaxis_title = "Number of nsSNPs"
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# , bp_plot_title = ""
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# , label_categories = c("Destabilising", "Stabilising")
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# , title_colour = "chocolate4"
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# , subtitle_text = NULL
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# , sts = 20
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# , subtitle_colour = "pink"
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# #, leg_position = c(0.73,0.8) # within plot area
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# , leg_position = "top"
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# , bar_fill_values = c("#F8766D", "#00BFC4")
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#########################################################
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#=============
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# Data: Input
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#==============
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#source("~/git/LSHTM_analysis/config/alr.R")
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source("~/git/LSHTM_analysis/config/embb.R")
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#source("~/git/LSHTM_analysis/config/katg.R")
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#source("~/git/LSHTM_analysis/config/gid.R")
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#source("~/git/LSHTM_analysis/config/pnca.R")
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#source("~/git/LSHTM_analysis/config/rpob.R")
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source("~/git/LSHTM_analysis/scripts/plotting/get_plotting_dfs.R")
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source("~/git/LSHTM_analysis/scripts/plotting/plotting_colnames.R")
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class(merged_df3)
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merged_df3 = as.data.frame(merged_df3)
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class(df3)
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head(merged_df3$pos_count)
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nc_pc_CHANGE = which(colnames(merged_df3)== "pos_count"); nc_pc_CHANGE
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colnames(merged_df3)[nc_pc_CHANGE] = "df2_pos_count_all"
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head(merged_df3$pos_count)
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head(merged_df3$df2_pos_count_all)
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# DROP pos_count column
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# merged_df3$pos_count <-NULL
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merged_df3 = merged_df3[, !colnames(merged_df3)%in%c("pos_count")]
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head(merged_df3$pos_count)
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df3 = merged_df3[, colnames(merged_df3)%in%plotting_cols]
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#=======
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# output
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#=======
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outdir_images = paste0("~/git/Writing/thesis/images/results/", tolower(gene), "/")
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cat("plots will output to:", outdir_images)
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###########################################################
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#------------------------------
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# plot default sizes
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#------------------------------
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#=========================
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# Affinity outcome
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# check this var: outcome_cols_affinity
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# get from preformatting or put in globals
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#==========================
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DistCutOff
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LigDist_colname # = "ligand_distance" # from globals
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ppi2Dist_colname
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naDist_colname
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###########################################################
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# get plotting data within the distance
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df3_lig = df3[df3[[LigDist_colname]]<DistCutOff,]
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df3_ppi2 = df3[df3[[ppi2Dist_colname]]<DistCutOff,]
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df3_na = df3[df3[[naDist_colname]]<DistCutOff,]
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common_bp_title = paste0("Sites <", DistCutOff, angstroms_symbol)
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#------------------------------
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# barplot for ligand affinity:
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# <10 Ang of ligand
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#------------------------------
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mLigP = stability_count_bp(plotdf = df3_lig
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, df_colname = "ligand_outcome"
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#, leg_title = "mCSM-lig"
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#, bp_plot_title = paste(common_bp_title, "ligand")
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, yaxis_title = "Number of nsSNPs"
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, leg_position = "none"
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, subtitle_text = "mCSM-lig"
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, bar_fill_values = c("#F8766D", "#00BFC4")
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, subtitle_colour= "black"
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, sts = 10
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, lts = 8
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, ats = 12
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, als = 11
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, ltis = 11
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, geom_ls = 2.5)
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mLigP
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#------------------------------
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# barplot for ligand affinity:
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# <10 Ang of ligand
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# mmCSM-lig: will be the same no. of sites but the effect will be different
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#------------------------------
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mmLigP = stability_count_bp(plotdf = df3_lig
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, df_colname = "mmcsm_lig_outcome"
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#, leg_title = "mmCSM-lig"
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#, label_categories = labels_mmlig
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#, bp_plot_title = paste(common_bp_title, "ligand")
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, yaxis_title = ""
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, leg_position = "none"
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, subtitle_text = "mmCSM-lig"
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, bar_fill_values = c("#F8766D", "#00BFC4")
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, subtitle_colour= "black"
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, sts = 10
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, lts = 8
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, ats = 12
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, als = 11
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, ltis = 11
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, geom_ls = 2.5
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)
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mmLigP
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#------------------------------
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# barplot for ppi2 affinity
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# <10 Ang of interface
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#------------------------------
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ppi2P = stability_count_bp(plotdf = df3_ppi2
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, df_colname = "mcsm_ppi2_outcome"
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#, leg_title = "mCSM-ppi2"
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#, label_categories = labels_ppi2
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#, bp_plot_title = paste(common_bp_title, "PP-interface")
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, yaxis_title = "Number of nsSNPs"
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, leg_position = "none"
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, subtitle_text = "mCSM-ppi2"
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, bar_fill_values = c("#F8766D", "#00BFC4")
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, subtitle_colour= "black"
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, sts = 10
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, lts = 8
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, ats = 12
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, als = 11
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, ltis = 11
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, geom_ls = 2.5
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)
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ppi2P
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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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# ------------------------------
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common_bp_title = paste0("Sites <", DistCutOff, angstroms_symbol)
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posC_lig = site_snp_count_bp(plotdf = df3_lig
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, df_colname = "position"
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, xaxis_title = "Number of nsSNPs"
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, yaxis_title = "Number of Sites"
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, subtitle_colour = "chocolate4"
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, subtitle_text = ""
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, subtitle_size = 8
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, geom_ls = 2.6
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, leg_text_size = 10
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, axis_text_size = 10
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, axis_label_size = 10)
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posC_lig
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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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posC_ppi2 = site_snp_count_bp(plotdf = df3_ppi2
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, df_colname = "position"
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, xaxis_title = "Number of nsSNPs"
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, yaxis_title = "Number of Sites"
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, subtitle_colour = "chocolate4"
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, subtitle_text = ""
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, subtitle_size = 8
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, geom_ls = 2.6
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, leg_text_size = 10
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, axis_text_size = 10
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, axis_label_size = 10)
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posC_ppi2
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#===============================================================
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# PE count
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rects <- data.frame(x = 1:6,
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colors = c("#ffd700" #gold
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, "#f0e68c" #khaki
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, "#da70d6"# orchid
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, "#ff1493"# deeppink
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, "#00BFC4" #, "#007d85" #blue
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, "#F8766D" )# red,
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)
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rects
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rects$text = c("-ve Lig affinty"
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, "+ve Lig affinity"
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, "+ve PPI2 affinity"
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, "-ve PPI2 affinity"
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, "+ve stability"
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, "-ve stability")
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# FOR EMBB ONLY
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rects$numbers = c(38, 0, 22, 9, 108, 681)
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rects$num_labels = paste0("n=", rects$numbers)
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rects
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#https://stackoverflow.com/questions/47986055/create-a-rectangle-filled-with-text
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peP = ggplot(rects, aes(x, y = 0, fill = colors, label = paste0(text,"\n", num_labels))) +
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geom_tile(width = 1, height = 1) + # make square tiles
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geom_text(color = "black", size = 1.7) + # 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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peP2 = ggplot(rects, aes(x, y = 0, fill = colors, label = paste0(text,"\n", num_labels))) +
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geom_tile() + # make square tiles
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geom_text(color = "black", size = 1.6) + # 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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theme_nothing() # remove any axis markings
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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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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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, yaxis_title = "Number of Sites"
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, subtitle_colour = "chocolate4"
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, subtitle_text = "All mutations sites"
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, subtitle_size = 8
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, geom_ls = 2.6
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, leg_text_size = 10
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, axis_text_size = 10
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, axis_label_size = 10)
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##################################################################
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#------------------------------
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# barplot for sensitivity:
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#------------------------------
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senP = stability_count_bp(plotdf = df3
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, df_colname = "sensitivity"
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#, leg_title = "mCSM-ppi2"
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#, label_categories = labels_ppi2
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#, bp_plot_title = paste(common_bp_title, "PP-interface")
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, yaxis_title = "Number of nsSNPs"
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, leg_position = "none"
|
||||
, subtitle_text = "Sensitivity"
|
||||
, bar_fill_values = c("red", "blue")
|
||||
, subtitle_colour= "black"
|
||||
, sts = 10
|
||||
, lts = 8
|
||||
, ats = 12
|
||||
, als = 11
|
||||
, ltis = 11
|
||||
, geom_ls = 2.5
|
||||
)
|
||||
|
||||
|
||||
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 = 12
|
||||
, als = 11
|
||||
, ltis = 11
|
||||
, geom_ls = 2.5)
|
||||
|
||||
consurfP
|
||||
|
||||
237
scripts/plotting/plotting_thesis/basic_barplots_layout_v2.R
Normal file
237
scripts/plotting/plotting_thesis/basic_barplots_layout_v2.R
Normal file
|
|
@ -0,0 +1,237 @@
|
|||
mLigP
|
||||
mmLigP
|
||||
posC_lig
|
||||
ppi2P
|
||||
posC_ppi2
|
||||
peP
|
||||
pe_allCL
|
||||
|
||||
|
||||
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 legend
|
||||
common_legend_outcome = get_legend(mLigP +
|
||||
guides(color = guide_legend(nrow = 1)) +
|
||||
theme(legend.position = "top"))
|
||||
|
||||
###############################################################
|
||||
#================================
|
||||
# Lig Affinity: outcome + site
|
||||
#================================
|
||||
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
|
||||
)
|
||||
|
||||
#-------------
|
||||
# Outplot
|
||||
#-------------
|
||||
ligaffP = paste0(outdir_images
|
||||
,tolower(gene)
|
||||
,"_lig_oc.png")
|
||||
|
||||
#svg(affP, width = 20, height = 5.5)
|
||||
print(paste0("plot filename:", ligaffP))
|
||||
png(ligaffP, units = "in", width = 6, height = 4, res = 300 )
|
||||
cowplot::plot_grid(cowplot::plot_grid(lig_affT,common_legend_outcome,
|
||||
nrow = 2,
|
||||
rel_heights = c(1,1)
|
||||
),
|
||||
cowplot::plot_grid(mLigP, mmLigP, posC_lig
|
||||
, nrow = 1
|
||||
#, labels = c("A", "B", "C","D")
|
||||
, rel_widths = c(1,1,1.8)
|
||||
, align = "h"),
|
||||
nrow = 2,
|
||||
labels = c("A", ""),
|
||||
label_size = 12,
|
||||
rel_heights = c(1,8))
|
||||
dev.off()
|
||||
#############################################################
|
||||
#================================
|
||||
# PPI2 Affinity: outcome + site
|
||||
#================================
|
||||
ppi2T = paste0(common_bp_title, " PP-interface")
|
||||
ppi2_affT = ggdraw() +
|
||||
draw_label(
|
||||
ppi2T,
|
||||
fontfamily = title_theme$family,
|
||||
fontface = title_theme$face,
|
||||
#size = title_theme$size
|
||||
size = 8
|
||||
)
|
||||
|
||||
|
||||
#-------------
|
||||
# Outplot: PPI2
|
||||
#-------------
|
||||
ppiaffP = paste0(outdir_images
|
||||
,tolower(gene)
|
||||
,"_ppi2_oc.png")
|
||||
|
||||
#svg(affP, width = 20, height = 5.5)
|
||||
print(paste0("plot filename:", ppiaffP))
|
||||
png(ppiaffP, units = "in", width = 6, height = 4, res = 300 )
|
||||
|
||||
|
||||
cowplot::plot_grid(cowplot::plot_grid(ppi2_affT, common_legend_outcome,
|
||||
nrow = 2,
|
||||
rel_heights = c(1,1)),
|
||||
cowplot::plot_grid(ppi2P, posC_ppi2
|
||||
, nrow = 1
|
||||
, rel_widths = c(1.2,1.8)
|
||||
, align = "h"
|
||||
, label_size = my_label_size),
|
||||
nrow = 2,
|
||||
labels = c("B", ""),
|
||||
label_size = 12,
|
||||
rel_heights = c(1,8)
|
||||
)
|
||||
|
||||
dev.off()
|
||||
#############################################################
|
||||
peP # pe counts
|
||||
#================================
|
||||
# PE + All position count
|
||||
#================================
|
||||
peT_allT = ggdraw() +
|
||||
draw_label(
|
||||
paste0("All mutation sites"),
|
||||
fontfamily = title_theme$family,
|
||||
fontface = title_theme$face,
|
||||
#size = title_theme$size
|
||||
size = 8
|
||||
)
|
||||
|
||||
|
||||
#-------------
|
||||
# Outplot: PPI2
|
||||
#-------------
|
||||
pe_allCL = paste0(outdir_images
|
||||
,tolower(gene)
|
||||
,"_pe_oc.png")
|
||||
|
||||
#svg(affP, width = 20, height = 5.5)
|
||||
print(paste0("plot filename:", pe_allCL))
|
||||
png(pe_allCL, units = "in", width = 6, height = 4, res = 300 )
|
||||
|
||||
|
||||
cowplot::plot_grid(peT_allT,
|
||||
cowplot::plot_grid(peP, posC_all
|
||||
, nrow = 1
|
||||
, rel_widths = c(1, 2)
|
||||
, align = "h"),
|
||||
nrow = 2,
|
||||
labels = c("C", "", ""),
|
||||
label_size = 12,
|
||||
rel_heights = c(1,8))
|
||||
|
||||
dev.off()
|
||||
#===========================================
|
||||
# COMBINE ALL three
|
||||
#==========================================
|
||||
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)
|
||||
|
||||
)
|
||||
|
||||
|
||||
p2 = cowplot::plot_grid(cowplot::plot_grid(ppi2_affT, common_legend_outcome, nrow=2),
|
||||
cowplot::plot_grid(ppi2P, posC_ppi2
|
||||
, nrow = 1
|
||||
, rel_widths = c(1.2,1.8)
|
||||
, align = "h"),
|
||||
nrow = 2,
|
||||
rel_heights = c(1,8)
|
||||
)
|
||||
|
||||
|
||||
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(1,10),
|
||||
nrow = 2,axis = "lr")
|
||||
|
||||
|
||||
|
||||
#===============
|
||||
# Final combine
|
||||
#===============
|
||||
w = 11.75
|
||||
h = 3.7
|
||||
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:", sens_conP))
|
||||
png(sens_conP, units = "in", width = w, height = h, res = 300 )
|
||||
|
||||
consurfP
|
||||
|
||||
dev.off()
|
||||
182
scripts/plotting/plotting_thesis/bp_PE.R
Normal file
182
scripts/plotting/plotting_thesis/bp_PE.R
Normal file
|
|
@ -0,0 +1,182 @@
|
|||
colnames(str_df_short)
|
||||
table(str_df_short$effect_type)
|
||||
table(str_df_short$effect_sign)
|
||||
|
||||
str(str_df_short)
|
||||
|
||||
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)
|
||||
|
||||
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$effect_type%in%affcols
|
||||
, "affinity"
|
||||
, str_df_short$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$effect_grouped%in%c("affinity", "ppi2")
|
||||
, "stability"
|
||||
, str_df_short$effect_grouped)
|
||||
|
||||
table(str_df_short$effect_grouped)
|
||||
|
||||
|
||||
# create a sign as well
|
||||
str_df_short$effect_outcome = paste0(str_df_short$pe_outcome
|
||||
, str_df_short$effect_grouped)
|
||||
|
||||
table(str_df_short$effect_outcome)
|
||||
|
||||
pe_colour_map2 = c( "DDaffinity" = "#ffd700" # gold
|
||||
, "SSaffinity" = "#f0e68c" # khaki
|
||||
, "DDppi2" = "#ff1493" # deeppink
|
||||
, "SSppi2" = "#da70d6" # orchid
|
||||
, "DDstability " = "#ae301e"
|
||||
, "SSstability" = "#007d85"
|
||||
)
|
||||
|
||||
|
||||
str_df_short$effect_colours = str_df_short$effect_outcome
|
||||
|
||||
str_df_short = dplyr::mutate(str_df_short
|
||||
, effect_colours = case_when(effect_colours == "DDaffinity" ~ "#ffd700"
|
||||
, effect_colours == "DDppi2" ~ '#ff1493'
|
||||
, effect_colours == "SSppi2" ~ '#da70d6'
|
||||
, effect_colours == "DDstability" ~ '#ae301e'
|
||||
, effect_colours =="SSstability" ~ '#007d85'
|
||||
, TRUE ~ 'ns'))
|
||||
|
||||
"#F8766D" #red
|
||||
"#00BFC4" #blue
|
||||
table(str_df_short$effect_colours)
|
||||
|
||||
|
||||
###########################################
|
||||
|
||||
ggplot(str_df_short
|
||||
, aes( x=effect_grouped
|
||||
, fill = effect_colours)) +
|
||||
geom_bar() +
|
||||
scale_fill_manual(values = str_df_short$effect_colours)
|
||||
|
||||
|
||||
|
||||
first_col = c(38, 0)
|
||||
second_col = c(9, 22)
|
||||
third_col = c(681, 108)
|
||||
thing_df = data.frame(first_row, second_row, third_row)
|
||||
rownames(thing_df) = c("Destabilising","Stabilising")
|
||||
thing_df
|
||||
|
||||
|
||||
###############################################
|
||||
rect_colour_map = c("EMB" = "green"
|
||||
,"DSL" = "slategrey"
|
||||
, "CDL" = "navyblue"
|
||||
, "Ca" = "purple")
|
||||
|
||||
|
||||
rects <- data.frame(x = 1:6,
|
||||
colors = c("#ffd700" #gold
|
||||
, "#f0e68c" #khaki
|
||||
, "#da70d6"# orchid
|
||||
, "#ff1493"# deeppink
|
||||
, "#00BFC4" #, "#007d85" #blue
|
||||
, "#F8766D" )# red,
|
||||
)
|
||||
rects
|
||||
|
||||
rects$text = c("-ve Lig affinty"
|
||||
, "+ve Lig affinity"
|
||||
, "+ve PPI2 affinity"
|
||||
, "-ve PPI2 affinity"
|
||||
, "+ve stability"
|
||||
, "-ve stability")
|
||||
|
||||
|
||||
rects$numbers = c(38, 0, 22, 9, 108, 681)
|
||||
rects$num_labels = paste0("n=", rects$numbers)
|
||||
|
||||
rects
|
||||
|
||||
outdir_images = paste0("~/git/Writing/thesis/images/results/", tolower(gene), "/")
|
||||
|
||||
#https://stackoverflow.com/questions/47986055/create-a-rectangle-filled-with-text
|
||||
png(paste0(outdir_images, "test.png")
|
||||
, width = 0.5
|
||||
, height = 2.5
|
||||
, units = "in", res = 300)
|
||||
|
||||
ggplot(rects, aes(x, y = 0, fill = colors, label = paste0(text,"\n", num_labels))) +
|
||||
geom_tile(width = 1, height = 1) + # make square tiles
|
||||
geom_text(color = "black", size = 1.5) + # add white text in the middle
|
||||
scale_fill_identity(guide = "none") + # color the tiles with the colors in the data frame
|
||||
coord_fixed() + # make sure tiles are square
|
||||
coord_flip()+ scale_x_reverse() +
|
||||
# theme_void() # remove any axis markings
|
||||
theme_nothing() # remove any axis markings
|
||||
|
||||
|
||||
dev.off()
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
##########################################################
|
||||
tile_map=data.frame(tile=c("EMB","DSL","CDL","Ca")
|
||||
,tile_colour =c("green","darkslategrey","navyblue","purple"))
|
||||
|
||||
|
||||
# great
|
||||
tile_colour_map = c("EMB" = "green"
|
||||
,"DSL" = "darkslategrey"
|
||||
, "CDL" = "navyblue"
|
||||
, "Ca" = "purple")
|
||||
|
||||
tile_legend=get_legend(
|
||||
|
||||
ggplot(tile_map, aes(factor(tile),y=0
|
||||
, colour=tile_colour
|
||||
, fill=tile_colour))+
|
||||
geom_tile() +
|
||||
theme(legend.direction="horizontal") +
|
||||
scale_colour_manual(name=NULL
|
||||
#, values = tile_map$tile_colour
|
||||
, values=tile_colour_map) +
|
||||
scale_fill_manual(name=NULL
|
||||
#,values=tile_map$tile_colour
|
||||
, values = tile_colour_map)
|
||||
)
|
||||
#############################################################
|
||||
|
||||
|
||||
###############################################
|
||||
library(ggplot2)
|
||||
library(viridis)
|
||||
library(hrbrthemes)
|
||||
|
||||
ggplot(str_df_short, aes(fill=effect_colours,x=effect_type)) +
|
||||
geom_bar() +
|
||||
|
||||
scale_fill_viridis(discrete = T) +
|
||||
ggtitle("Studying 4 species..")
|
||||
####################################################
|
||||
|
||||
|
||||
|
||||
|
||||
|
|
@ -153,13 +153,20 @@ for (i in unique(str_df$position) ){
|
|||
# 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)
|
||||
#================
|
||||
# for Plots
|
||||
#================
|
||||
str_df_short = str_df[, c("mutationinformation","position","sensitivity"
|
||||
, "effect_type"
|
||||
, "effect_sign")]
|
||||
|
||||
# check
|
||||
str_df_check = str_df[str_df$position%in%c(24, 32,160, 303, 334 ),]
|
||||
str_df_check = str_df[str_df$position%in%c(24, 32,160, 303, 334),]
|
||||
table(str_df$effect_type)
|
||||
|
||||
#-------------------------------------
|
||||
# get df with uniqye position
|
||||
# get df with unique position
|
||||
#--------------------------------------
|
||||
#data[!duplicated(data$x), ]
|
||||
str_df_plot = str_df[!duplicated(str_df$position),]
|
||||
|
|
@ -234,7 +241,7 @@ table(str_df_plot_cols$colour_map)
|
|||
#-------------------
|
||||
# Ligand Affinity
|
||||
#-------------------
|
||||
foo = str_df_plot_cols[str_df_plot_cols$colours=="light_salmon",]
|
||||
foo = str_df_plot_cols[str_df_plot_cols$colours=="yellow",]
|
||||
all(foo2$effect_sign == 1)
|
||||
|
||||
foo1 = str_df_plot_cols[str_df_plot_cols$colours=="bright_salmon",]
|
||||
|
|
|
|||
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Reference in a new issue