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starting corr plots

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Tanushree Tunstall 2022-08-09 21:55:24 +01:00
parent cd86fcf8e8
commit a6d93b3fa8
2 changed files with 92 additions and 107 deletions
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24
scripts/functions/corr_plot_data.R
View file

@ -1,28 +1,7 @@
#!/usr/bin/env Rscript
#########################################################
# TASK: Script to format data for Correlation plots:
# corr_data_extract()
# INPUT:
# df: data with all parameters (my_use case)
# merged_df3 or merged_df2!?
# gene: [sanity check]
# drug: relates to a column name that will need to extracted
# ligand_dist_colname = LigDist_colname (variable from plotting_globals()
# colnames_to_extract = c("mutationinformation", "duet_affinity_change")
# display_colnames_key = c(mutationinformation = "MUT" , duet_affinity_change = "DUET")
# extract_scaled_cols = T or F, so that parameters with the _scaled suffix can be extracted.
# NOTE*: No formatting applied to these cols i.e display name
# RETURNS: DF
# containing all the columns required for generating downstream correlation plots
# TODO: ADD
#lineage_count_all
#lineage_count_unique
#my_df['lineage_proportion'] = my_df['lineage_count_unique']/my_df['lineage_count_all']
#my_df['dist_lineage_proportion'] = my_df['lineage_count_unique']/total_mtblineage_uc
##################################################################
# LigDist_colname #from globals: plotting_globals.R
@ -31,14 +10,11 @@
corr_data_extract <- function(df
, gene
, drug
#, ligand_dist_colname = LigDist_colname
, colnames_to_extract
, colnames_display_key
, extract_scaled_cols = F){
if ( missing(colnames_to_extract) || missing(colnames_display_key) ){
#if ( missing(colnames_to_extract) ){
cat("\n=========================================="
, "\nCORR PLOTS data: ALL params"
, "\n=========================================")

81
scripts/plotting/plotting_thesis/corr_plots_thesis.R
View file

@ -127,7 +127,8 @@ dev.off()
#================
# ppi2 affinity
#================
corr_ppi2_colnames = c("mCSM-PPI2"
if (tolower(gene)%in%geneL_ppi2){
corr_ppi2_colnames = c("mCSM-PPI2"
, "MAF"
, "Log(OR)"
, "-Log(P)"
@ -135,28 +136,28 @@ corr_ppi2_colnames = c("mCSM-PPI2"
, "dst_mode"
, drug)
corr_ppi2_colnames%in%colnames(corr_plotdf)
corr_df_ppi2 = corr_plotdf[, corr_ppi2_colnames]
corr_df_ppi2 = corr_df_ppi2[corr_df_ppi2["PPI-Dist"]<DistCutOff,]
complete_obs_ppi2 = nrow(corr_df_ppi2) - sum(is.na(corr_df_ppi2$`Log(OR)`))
cat("\nComplete muts for ppi2 affinity for", gene, ":", complete_obs_ppi2)
corr_ppi2_colnames%in%colnames(corr_plotdf)
corr_df_ppi2 = corr_plotdf[, corr_ppi2_colnames]
corr_df_ppi2 = corr_df_ppi2[corr_df_ppi2["PPI-Dist"]<DistCutOff,]
complete_obs_ppi2 = nrow(corr_df_ppi2) - sum(is.na(corr_df_ppi2$`Log(OR)`))
cat("\nComplete muts for ppi2 affinity for", gene, ":", complete_obs_ppi2)
color_coln = which(colnames(corr_df_ppi2) == "dst_mode")
end = which(colnames(corr_df_ppi2) == drug)
ncol_omit = 3 #omit dist col
corr_end = end-ncol_omit
color_coln = which(colnames(corr_df_ppi2) == "dst_mode")
end = which(colnames(corr_df_ppi2) == drug)
ncol_omit = 3 #omit dist col
corr_end = end-ncol_omit
#------------------------
# Output: ppi2 corrP
#------------------------
corr_ppi2P = paste0(outdir_images
#------------------------
# Output: ppi2 corrP
#------------------------
corr_ppi2P = paste0(outdir_images
,tolower(gene)
,"_corr_ppi2.svg" )
cat("Corr plot ppi2 with coloured dots:", corr_ppi2P)
svg(corr_ppi2P, width = 10, height = 10)
cat("Corr plot ppi2 with coloured dots:", corr_ppi2P)
svg(corr_ppi2P, width = 10, height = 10)
my_corr_pairs(corr_data_all = corr_df_ppi2
my_corr_pairs(corr_data_all = corr_df_ppi2
, corr_cols = colnames(corr_df_ppi2[1:corr_end])
, corr_method = "spearman"
, colour_categ_col = colnames(corr_df_ppi2[color_coln]) #"dst_mode"
@ -168,7 +169,8 @@ my_corr_pairs(corr_data_all = corr_df_ppi2
, corr_lab_size = 3
, corr_value_size = 1)
dev.off()
dev.off()
}
# FIXME: ADD distance
#==================
@ -177,7 +179,9 @@ dev.off()
#================
# NA affinity
#================
corr_na_colnames = c("mCSM-NA"
if (tolower(gene)%in%geneL_na){
corr_na_colnames = c("mCSM-NA"
, "MAF"
, "Log(OR)"
, "-Log(P)"
@ -185,28 +189,28 @@ corr_na_colnames = c("mCSM-NA"
, "dst_mode"
, drug)
corr_na_colnames%in%colnames(corr_plotdf)
corr_df_na = corr_plotdf[, corr_na_colnames]
corr_df_na = corr_df_na[corr_df_na["NA-Dist"]<DistCutOff,]
complete_obs_na = nrow(corr_df_na) - sum(is.na(corr_df_na$`Log(OR)`))
cat("\nComplete muts for NA affinity for", gene, ":", complete_obs_na)
corr_na_colnames%in%colnames(corr_plotdf)
corr_df_na = corr_plotdf[, corr_na_colnames]
corr_df_na = corr_df_na[corr_df_na["NA-Dist"]<DistCutOff,]
complete_obs_na = nrow(corr_df_na) - sum(is.na(corr_df_na$`Log(OR)`))
cat("\nComplete muts for NA affinity for", gene, ":", complete_obs_na)
color_coln = which(colnames(corr_df_na) == "dst_mode")
end = which(colnames(corr_df_na) == drug)
ncol_omit = 3 #omit dist col
corr_end = end-ncol_omit
color_coln = which(colnames(corr_df_na) == "dst_mode")
end = which(colnames(corr_df_na) == drug)
ncol_omit = 3 #omit dist col
corr_end = end-ncol_omit
#------------------------
# Output: mCSM-NA corrP
#------------------------
corr_naP = paste0(outdir_images
#------------------------
# Output: mCSM-NA corrP
#------------------------
corr_naP = paste0(outdir_images
,tolower(gene)
,"_corr_na.svg" )
cat("Corr plot mCSM-NA with coloured dots:", corr_naP)
svg(corr_naP, width = 10, height = 10)
cat("Corr plot mCSM-NA with coloured dots:", corr_naP)
svg(corr_naP, width = 10, height = 10)
my_corr_pairs(corr_data_all = corr_df_na
my_corr_pairs(corr_data_all = corr_df_na
, corr_cols = colnames(corr_df_na[1:corr_end])
, corr_method = "spearman"
, colour_categ_col = colnames(corr_df_na[color_coln]) #"dst_mode"
@ -218,7 +222,9 @@ my_corr_pairs(corr_data_all = corr_df_na
, corr_lab_size = 3
, corr_value_size = 1)
dev.off()
dev.off()
}
####################################################
# CONSERVATION
@ -265,3 +271,6 @@ my_corr_pairs(corr_data_all = corr_df_cons
, corr_value_size = 1)
dev.off()
#