starting corr plots
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2 changed files with 92 additions and 107 deletions
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@ -1,28 +1,7 @@
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#!/usr/bin/env Rscript
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#########################################################
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# TASK: Script to format data for Correlation plots:
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# corr_data_extract()
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# INPUT:
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# df: data with all parameters (my_use case)
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# merged_df3 or merged_df2!?
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# gene: [sanity check]
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# drug: relates to a column name that will need to extracted
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# ligand_dist_colname = LigDist_colname (variable from plotting_globals()
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# colnames_to_extract = c("mutationinformation", "duet_affinity_change")
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# display_colnames_key = c(mutationinformation = "MUT" , duet_affinity_change = "DUET")
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# extract_scaled_cols = T or F, so that parameters with the _scaled suffix can be extracted.
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# NOTE*: No formatting applied to these cols i.e display name
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# RETURNS: DF
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# containing all the columns required for generating downstream correlation plots
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# TODO: ADD
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#lineage_count_all
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#lineage_count_unique
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#my_df['lineage_proportion'] = my_df['lineage_count_unique']/my_df['lineage_count_all']
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#my_df['dist_lineage_proportion'] = my_df['lineage_count_unique']/total_mtblineage_uc
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##################################################################
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# LigDist_colname #from globals: plotting_globals.R
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@ -31,14 +10,11 @@
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corr_data_extract <- function(df
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, gene
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, drug
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#, ligand_dist_colname = LigDist_colname
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, colnames_to_extract
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, colnames_display_key
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, extract_scaled_cols = F){
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if ( missing(colnames_to_extract) || missing(colnames_display_key) ){
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#if ( missing(colnames_to_extract) ){
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cat("\n=========================================="
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, "\nCORR PLOTS data: ALL params"
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, "\n=========================================")
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@ -127,48 +127,50 @@ dev.off()
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#================
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# ppi2 affinity
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#================
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corr_ppi2_colnames = c("mCSM-PPI2"
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, "MAF"
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, "Log(OR)"
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, "-Log(P)"
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, "PPI-Dist" # "interface_dist"
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, "dst_mode"
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, drug)
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corr_ppi2_colnames%in%colnames(corr_plotdf)
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corr_df_ppi2 = corr_plotdf[, corr_ppi2_colnames]
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corr_df_ppi2 = corr_df_ppi2[corr_df_ppi2["PPI-Dist"]<DistCutOff,]
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complete_obs_ppi2 = nrow(corr_df_ppi2) - sum(is.na(corr_df_ppi2$`Log(OR)`))
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cat("\nComplete muts for ppi2 affinity for", gene, ":", complete_obs_ppi2)
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color_coln = which(colnames(corr_df_ppi2) == "dst_mode")
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end = which(colnames(corr_df_ppi2) == drug)
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ncol_omit = 3 #omit dist col
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corr_end = end-ncol_omit
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#------------------------
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# Output: ppi2 corrP
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#------------------------
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corr_ppi2P = paste0(outdir_images
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,tolower(gene)
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,"_corr_ppi2.svg" )
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cat("Corr plot ppi2 with coloured dots:", corr_ppi2P)
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svg(corr_ppi2P, width = 10, height = 10)
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my_corr_pairs(corr_data_all = corr_df_ppi2
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, corr_cols = colnames(corr_df_ppi2[1:corr_end])
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, corr_method = "spearman"
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, colour_categ_col = colnames(corr_df_ppi2[color_coln]) #"dst_mode"
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, categ_colour = c("red", "blue")
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, density_show = F
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, hist_col = "coral4"
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, dot_size = 2
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, ats = 1.5
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, corr_lab_size = 3
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, corr_value_size = 1)
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dev.off()
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if (tolower(gene)%in%geneL_ppi2){
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corr_ppi2_colnames = c("mCSM-PPI2"
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, "MAF"
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, "Log(OR)"
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, "-Log(P)"
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, "PPI-Dist" # "interface_dist"
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, "dst_mode"
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, drug)
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corr_ppi2_colnames%in%colnames(corr_plotdf)
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corr_df_ppi2 = corr_plotdf[, corr_ppi2_colnames]
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corr_df_ppi2 = corr_df_ppi2[corr_df_ppi2["PPI-Dist"]<DistCutOff,]
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complete_obs_ppi2 = nrow(corr_df_ppi2) - sum(is.na(corr_df_ppi2$`Log(OR)`))
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cat("\nComplete muts for ppi2 affinity for", gene, ":", complete_obs_ppi2)
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color_coln = which(colnames(corr_df_ppi2) == "dst_mode")
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end = which(colnames(corr_df_ppi2) == drug)
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ncol_omit = 3 #omit dist col
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corr_end = end-ncol_omit
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#------------------------
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# Output: ppi2 corrP
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#------------------------
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corr_ppi2P = paste0(outdir_images
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,tolower(gene)
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,"_corr_ppi2.svg" )
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cat("Corr plot ppi2 with coloured dots:", corr_ppi2P)
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svg(corr_ppi2P, width = 10, height = 10)
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my_corr_pairs(corr_data_all = corr_df_ppi2
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, corr_cols = colnames(corr_df_ppi2[1:corr_end])
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, corr_method = "spearman"
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, colour_categ_col = colnames(corr_df_ppi2[color_coln]) #"dst_mode"
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, categ_colour = c("red", "blue")
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, density_show = F
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, hist_col = "coral4"
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, dot_size = 2
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, ats = 1.5
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, corr_lab_size = 3
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, corr_value_size = 1)
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dev.off()
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}
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# FIXME: ADD distance
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#==================
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@ -177,48 +179,52 @@ dev.off()
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#================
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# NA affinity
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#================
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corr_na_colnames = c("mCSM-NA"
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, "MAF"
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, "Log(OR)"
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, "-Log(P)"
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, "NA-Dist" # "NA_dist"
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, "dst_mode"
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, drug)
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if (tolower(gene)%in%geneL_na){
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corr_na_colnames = c("mCSM-NA"
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, "MAF"
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, "Log(OR)"
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, "-Log(P)"
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, "NA-Dist" # "NA_dist"
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, "dst_mode"
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, drug)
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corr_na_colnames%in%colnames(corr_plotdf)
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corr_df_na = corr_plotdf[, corr_na_colnames]
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corr_df_na = corr_df_na[corr_df_na["NA-Dist"]<DistCutOff,]
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complete_obs_na = nrow(corr_df_na) - sum(is.na(corr_df_na$`Log(OR)`))
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cat("\nComplete muts for NA affinity for", gene, ":", complete_obs_na)
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color_coln = which(colnames(corr_df_na) == "dst_mode")
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end = which(colnames(corr_df_na) == drug)
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ncol_omit = 3 #omit dist col
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corr_end = end-ncol_omit
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#------------------------
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# Output: mCSM-NA corrP
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#------------------------
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corr_naP = paste0(outdir_images
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,tolower(gene)
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,"_corr_na.svg" )
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cat("Corr plot mCSM-NA with coloured dots:", corr_naP)
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svg(corr_naP, width = 10, height = 10)
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my_corr_pairs(corr_data_all = corr_df_na
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, corr_cols = colnames(corr_df_na[1:corr_end])
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, corr_method = "spearman"
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, colour_categ_col = colnames(corr_df_na[color_coln]) #"dst_mode"
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, categ_colour = c("red", "blue")
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, density_show = F
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, hist_col = "coral4"
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, dot_size = 2
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, ats = 1.5
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, corr_lab_size = 3
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, corr_value_size = 1)
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dev.off()
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}
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corr_na_colnames%in%colnames(corr_plotdf)
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corr_df_na = corr_plotdf[, corr_na_colnames]
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corr_df_na = corr_df_na[corr_df_na["NA-Dist"]<DistCutOff,]
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complete_obs_na = nrow(corr_df_na) - sum(is.na(corr_df_na$`Log(OR)`))
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cat("\nComplete muts for NA affinity for", gene, ":", complete_obs_na)
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color_coln = which(colnames(corr_df_na) == "dst_mode")
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end = which(colnames(corr_df_na) == drug)
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ncol_omit = 3 #omit dist col
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corr_end = end-ncol_omit
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#------------------------
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# Output: mCSM-NA corrP
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#------------------------
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corr_naP = paste0(outdir_images
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,tolower(gene)
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,"_corr_na.svg" )
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cat("Corr plot mCSM-NA with coloured dots:", corr_naP)
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svg(corr_naP, width = 10, height = 10)
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my_corr_pairs(corr_data_all = corr_df_na
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, corr_cols = colnames(corr_df_na[1:corr_end])
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, corr_method = "spearman"
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, colour_categ_col = colnames(corr_df_na[color_coln]) #"dst_mode"
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, categ_colour = c("red", "blue")
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, density_show = F
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, hist_col = "coral4"
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, dot_size = 2
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, ats = 1.5
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, corr_lab_size = 3
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, corr_value_size = 1)
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dev.off()
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####################################################
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# CONSERVATION
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@ -265,3 +271,6 @@ my_corr_pairs(corr_data_all = corr_df_cons
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, corr_value_size = 1)
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dev.off()
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#
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