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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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175
scripts/plotting/plotting_thesis/corr_plots_thesis.R
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@ -127,48 +127,50 @@ dev.off()
#================
# ppi2 affinity
#================
corr_ppi2_colnames = c("mCSM-PPI2"
, "MAF"
, "Log(OR)"
, "-Log(P)"
, "PPI-Dist" # "interface_dist"
, "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$`Log(OR)`))
cat("\nComplete muts for ppi2 affinity for", gene, ":", complete_obs_ppi2)
color_coln = which(colnames(corr_df_ppi2) == "dst_mode")
end = which(colnames(corr_df_ppi2) == drug)
ncol_omit = 3 #omit dist col
corr_end = end-ncol_omit
#------------------------
# Output: ppi2 corrP
#------------------------
corr_ppi2P = paste0(outdir_images
,tolower(gene)
,"_corr_ppi2.svg" )
cat("Corr plot ppi2 with coloured dots:", corr_ppi2P)
svg(corr_ppi2P, width = 10, height = 10)
my_corr_pairs(corr_data_all = corr_df_ppi2
, corr_cols = colnames(corr_df_ppi2[1:corr_end])
, corr_method = "spearman"
, colour_categ_col = colnames(corr_df_ppi2[color_coln]) #"dst_mode"
, categ_colour = c("red", "blue")
, density_show = F
, hist_col = "coral4"
, dot_size = 2
, ats = 1.5
, corr_lab_size = 3
, corr_value_size = 1)
dev.off()
if (tolower(gene)%in%geneL_ppi2){
corr_ppi2_colnames = c("mCSM-PPI2"
, "MAF"
, "Log(OR)"
, "-Log(P)"
, "PPI-Dist" # "interface_dist"
, "dst_mode"
, drug)
corr_ppi2_colnames%in%colnames(corr_plotdf)
corr_df_ppi2 = corr_plotdf[, corr_ppi2_colnames]
corr_df_ppi2 = corr_df_ppi2[corr_df_ppi2["PPI-Dist"]<DistCutOff,]
complete_obs_ppi2 = nrow(corr_df_ppi2) - sum(is.na(corr_df_ppi2$`Log(OR)`))
cat("\nComplete muts for ppi2 affinity for", gene, ":", complete_obs_ppi2)
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
,tolower(gene)
,"_corr_ppi2.svg" )
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
, corr_cols = colnames(corr_df_ppi2[1:corr_end])
, corr_method = "spearman"
, colour_categ_col = colnames(corr_df_ppi2[color_coln]) #"dst_mode"
, categ_colour = c("red", "blue")
, density_show = F
, hist_col = "coral4"
, dot_size = 2
, ats = 1.5
, corr_lab_size = 3
, corr_value_size = 1)
dev.off()
}
# FIXME: ADD distance
#==================
@ -177,48 +179,52 @@ dev.off()
#================
# NA affinity
#================
corr_na_colnames = c("mCSM-NA"
, "MAF"
, "Log(OR)"
, "-Log(P)"
, "NA-Dist" # "NA_dist"
, "dst_mode"
, drug)
if (tolower(gene)%in%geneL_na){
corr_na_colnames = c("mCSM-NA"
, "MAF"
, "Log(OR)"
, "-Log(P)"
, "NA-Dist" # "NA_dist"
, "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$`Log(OR)`))
cat("\nComplete muts for NA affinity for", gene, ":", complete_obs_na)
color_coln = which(colnames(corr_df_na) == "dst_mode")
end = which(colnames(corr_df_na) == drug)
ncol_omit = 3 #omit dist col
corr_end = end-ncol_omit
#------------------------
# Output: mCSM-NA corrP
#------------------------
corr_naP = paste0(outdir_images
,tolower(gene)
,"_corr_na.svg" )
cat("Corr plot mCSM-NA with coloured dots:", corr_naP)
svg(corr_naP, width = 10, height = 10)
my_corr_pairs(corr_data_all = corr_df_na
, corr_cols = colnames(corr_df_na[1:corr_end])
, corr_method = "spearman"
, colour_categ_col = colnames(corr_df_na[color_coln]) #"dst_mode"
, categ_colour = c("red", "blue")
, density_show = F
, hist_col = "coral4"
, dot_size = 2
, ats = 1.5
, corr_lab_size = 3
, corr_value_size = 1)
dev.off()
}
corr_na_colnames%in%colnames(corr_plotdf)
corr_df_na = corr_plotdf[, corr_na_colnames]
corr_df_na = corr_df_na[corr_df_na["NA-Dist"]<DistCutOff,]
complete_obs_na = nrow(corr_df_na) - sum(is.na(corr_df_na$`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
#------------------------
# 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)
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"
, categ_colour = c("red", "blue")
, density_show = F
, hist_col = "coral4"
, dot_size = 2
, ats = 1.5
, corr_lab_size = 3
, corr_value_size = 1)
dev.off()
####################################################
# CONSERVATION
@ -265,3 +271,6 @@ my_corr_pairs(corr_data_all = corr_df_cons
, corr_value_size = 1)
dev.off()
#