renamed 2 to _v2
This commit is contained in:
parent
802d6f8495
commit
8d6c148fff
7 changed files with 74 additions and 588 deletions
|
|
@ -53,6 +53,7 @@ if (!exists("infile_params") && exists("gene")){
|
|||
cat("\nReading mcsm combined data file: ", infile_params)
|
||||
mcsm_df = read.csv(infile_params, header = T)
|
||||
pd_df = plotting_data(mcsm_df
|
||||
, gene = gene # ADDED
|
||||
, lig_dist_colname = LigDist_colname
|
||||
, lig_dist_cutoff = LigDist_cutoff)
|
||||
|
||||
|
|
@ -87,6 +88,7 @@ cat("\nDim of meta data file: ", dim(gene_metadata))
|
|||
|
||||
all_plot_dfs = combining_dfs_plotting(my_df_u
|
||||
, gene_metadata
|
||||
, gene = gene # ADDED
|
||||
, lig_dist_colname = LigDist_colname
|
||||
, lig_dist_cutoff = LigDist_cutoff)
|
||||
|
||||
|
|
|
|||
|
|
@ -92,8 +92,8 @@ if (tolower(gene)%in%geneL_na){
|
|||
naDist_colname,
|
||||
"mcsm_na_affinity", "mcsm_na_scaled", "mcsm_na_outcome")
|
||||
|
||||
raw_affinity_cols = c(common_raw_affinity_cols , "mcsm_na_affinity")
|
||||
scaled_affinity_cols = c(common_scaled_affinity_cols , "mcsm_na_scaled")
|
||||
raw_affinity_cols = c(common_raw_affinity_cols , "mcsm_na_affinity")
|
||||
scaled_affinity_cols = c(common_scaled_affinity_cols , "mcsm_na_scaled")
|
||||
outcome_affinity_cols = c(common_outcome_affinity_cols , "mcsm_na_outcome")
|
||||
affinity_dist_colnames = c(LigDist_colname, ppi2Dist_colname, naDist_colname)
|
||||
|
||||
|
|
|
|||
|
|
@ -30,8 +30,8 @@
|
|||
#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/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
|
||||
|
|
|
|||
|
|
@ -1,584 +0,0 @@
|
|||
#!/usr/bin/env Rscript
|
||||
#########################################################
|
||||
# TASK: Barplots for mCSM DUET, ligand affinity, and foldX
|
||||
# basic barplots with count of mutations
|
||||
# basic barplots with frequency of count of mutations
|
||||
|
||||
# , df_colname = ""
|
||||
# , leg_title = ""
|
||||
# , ats = 25 # axis text size
|
||||
# , als = 22 # axis label size
|
||||
# , lts = 20 # legend text size
|
||||
# , ltis = 22 # label title size
|
||||
# , geom_ls = 10 # geom_label size
|
||||
# , yaxis_title = "Number of nsSNPs"
|
||||
# , bp_plot_title = ""
|
||||
# , label_categories = c("Destabilising", "Stabilising")
|
||||
# , title_colour = "chocolate4"
|
||||
# , subtitle_text = NULL
|
||||
# , sts = 20
|
||||
# , subtitle_colour = "pink"
|
||||
# #, leg_position = c(0.73,0.8) # within plot area
|
||||
# , leg_position = "top"
|
||||
# , bar_fill_values = c("#F8766D", "#00BFC4")
|
||||
#########################################################
|
||||
#=============
|
||||
# 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
|
||||
# sanity check
|
||||
|
||||
cat("\nSourced plotting cols as well:", length(plotting_cols))
|
||||
|
||||
####################################################
|
||||
class(merged_df3)
|
||||
merged_df3 = as.data.frame(merged_df3)
|
||||
|
||||
class(merged_df3)
|
||||
head(merged_df3$pos_count)
|
||||
|
||||
nc_pc_CHANGE = which(colnames(merged_df3)== "pos_count"); nc_pc_CHANGE
|
||||
colnames(merged_df3)[nc_pc_CHANGE] = "df2_pos_count_all"
|
||||
head(merged_df3$pos_count)
|
||||
head(merged_df3$df2_pos_count_all)
|
||||
|
||||
# DROP pos_count column
|
||||
# merged_df3$pos_count <-NULL
|
||||
merged_df3 = merged_df3[, !colnames(merged_df3)%in%c("pos_count")]
|
||||
head(merged_df3$pos_count)
|
||||
|
||||
df3 = merged_df3[, colnames(merged_df3)%in%plotting_cols]
|
||||
"nca_distance"%in%colnames(df3)
|
||||
|
||||
#=======
|
||||
# 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
|
||||
}
|
||||
|
||||
|
||||
#===============================================================
|
||||
# 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
|
||||
|
||||
# ------------------------------
|
||||
# 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
|
||||
##################################################################
|
||||
|
||||
#------------------------------
|
||||
# barplot for sensitivity:
|
||||
#------------------------------
|
||||
# sensP = stability_count_bp(plotdf = df3
|
||||
# , df_colname = "sensitivity"
|
||||
# #, 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 = "Sensitivity"
|
||||
# , bar_fill_values = c("red", "blue")
|
||||
# , subtitle_colour= "black"
|
||||
# , sts = 10
|
||||
# , lts = 8
|
||||
# , ats = 8
|
||||
# , als =8
|
||||
# , ltis = 11
|
||||
# , geom_ls =2
|
||||
# )
|
||||
|
||||
|
||||
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
|
||||
|
||||
####################
|
||||
# Sensitivity count: Mutations
|
||||
####################
|
||||
table(df3$sensitivity)
|
||||
|
||||
rect_sens=data.frame(mutation_class=c("Resistant","Sensitive")
|
||||
, tile_colour =c("red","blue")
|
||||
, numbers = c(table(df3$sensitivity)[[1]], table(df3$sensitivity)[[2]]))
|
||||
|
||||
|
||||
|
||||
sensP = ggplot(rect_sens, aes(mutation_class, y = 0
|
||||
, fill = tile_colour
|
||||
, label = paste0("n=", numbers)
|
||||
)) +
|
||||
geom_tile(width = 1, height = 1) + # make square tiles
|
||||
geom_label(color = "black", size = 1.7,fill = "white", alpha=0.7) + # 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
|
||||
sensP
|
||||
|
||||
|
||||
# sensP2 = sensP +
|
||||
# coord_flip() + scale_x_reverse()
|
||||
# sensP2
|
||||
#===============================
|
||||
# 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
|
||||
|
||||
##############################################################
|
||||
#===================
|
||||
# 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
|
||||
|
||||
##############################################################
|
||||
|
||||
##############################
|
||||
# FIXME for other genes: ATTEMPTED to derive numbers
|
||||
##############################
|
||||
#
|
||||
# table(str_df_short$pe_effect_outcome)
|
||||
# # extract the numbers
|
||||
# DD_lig_n = table(str_df_short$pe_effect_outcome)[[1]]
|
||||
# SS_lig_n = 0
|
||||
# DD_ppi2_n = table(str_df_short$pe_effect_outcome)[[2]]
|
||||
# SS_ppi2_n = table(str_df_short$pe_effect_outcome)[[4]]
|
||||
# DD_stability_n = table(str_df_short$pe_effect_outcome)[[3]]
|
||||
# SS_stability_n = table(str_df_short$pe_effect_outcome)[[5]]
|
||||
#
|
||||
# nums = c(DD_lig_n, SS_lig_n,DD_ppi2_n,SS_ppi2_n, DD_stability_n, SS_stability_n )
|
||||
#
|
||||
# rect_pe = data.frame(x = 1:6
|
||||
# , pe_effect_type=c("-ve Lig aff"
|
||||
# , "+ve Lig aff"
|
||||
# , "-ve PPI2 aff"
|
||||
# , " +ve PPI2 aff"
|
||||
# , "-ve stability"
|
||||
# , "+ve stability")
|
||||
#
|
||||
# , tile_colour =c("#ffd700" #gold
|
||||
# ,"#f0e68c" # khaki
|
||||
# , "#ff1493" #deeppink
|
||||
# , "#da70d6" #orchid
|
||||
# , "#F8766D" # Sred
|
||||
# , "#00BFC4") #Sblue
|
||||
# # , numbers = c(DD_lig_n
|
||||
# # , SS_lig_n
|
||||
# # , DD_ppi2_n
|
||||
# # , SS_ppi2_n
|
||||
# # , DD_stability_n
|
||||
# # , SS_stability_n )
|
||||
# , numbers = nums
|
||||
# )
|
||||
#
|
||||
# rect_pe$num_labels = paste0("n=", rect_pe$numbers)
|
||||
# rect_pe
|
||||
#
|
||||
# # create plot
|
||||
# peP = ggplot(rect_pe, aes(x=pe_effect_type , y = 0, fill = tile_colour
|
||||
# , label = paste0(pe_effect_type,"\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(rect_pe, aes(x=pe_effect_type, y = 0, fill = tile_colour
|
||||
# , label = paste0(pe_effect_type,"\n", num_labels))) +
|
||||
# geom_tile() +
|
||||
# geom_text(color = "black", size = 1.6) +
|
||||
# scale_fill_identity(guide = "none") +
|
||||
# coord_fixed() +
|
||||
# theme_nothing()
|
||||
# peP2
|
||||
Loading…
Add table
Add a link
Reference in a new issue