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added function to extract data for correlation plots and corresponding test script

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Tanushree Tunstall 2022-01-29 17:31:02 +00:00
parent a4a4890634
commit 1035547309
2 changed files with 115 additions and 0 deletions
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scripts/functions/corr_plot_df.R Normal file
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#colnames_to_extract = c("mutationinformation"
# , "duet_affinity_change")
#display_colnames_key = c(mutationinformation = "MUT"
# , duet_affinity_change = "DUET")
##################################################################
corr_data_extract <- function(corr_plot_df
, gene_name = gene
, drug_name = drug
, 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=========================================")
cat("\nExtracting default columns for"
, "\nGene name:", gene
, "\nDrug name:", drug)
colnames_to_extract = c(drug
#, "mutationinformation"
, "mutation_info_labels"
, "duet_stability_change"
, "ligand_affinity_change"
, "ligand_distance"
, "ddg_foldx"
, "deepddg"
, "asa"
, "rsa"
, "kd_values"
, "rd_values"
, "af"
, "log10_or_mychisq"
, "neglog_pval_fisher"
, "ddg_dynamut2"
, "consurf_score"
, "snap2_score"
, "ddg_dynamut", "ddg_encom", "dds_encom", "ddg_mcsm", "ddg_sdm", "ddg_duet"
, "mcsm_na_affinity"
, "mcsm_ppi2_affinity"
)
# [optional] arg: extract_scaled_cols
if (extract_scaled_cols){
cat("\nExtracting scaled columns as well...\n")
all_scaled_cols = colnames(merged_df3)[grep(".*scaled", colnames(merged_df3))]
colnames_to_extract = c(colnames_to_extract, all_scaled_cols)
}else{
colnames_to_extract = colnames_to_extract
}
corr_df = corr_plot_df[, colnames(corr_plot_df)%in%colnames_to_extract]
# arg: colnames_display_key
colnames_display_key = c(duet_stability_change = "DUET"
, ligand_affinity_change = "mCSM-lig"
, ligand_distance = "ligand_distance"
, ddg_foldx = "FoldX"
, deepddg = "DeepDDG"
, asa = "ASA"
, rsa = "RSA"
, kd_values = "KD"
, rd_values = "RD"
, af = "MAF"
, log10_or_mychisq = "Log (OR)"
, neglog_pval_fisher = "-Log (P)"
, ddg_dynamut2 = "Dynamut2"
, consurf_score = "Consurf"
, snap2_score = "SNAP2"
, ddg_dynamut = "Dynamut"
, ddg_encom = "ENCoM-DDG"
, ddg_mcsm = "mCSM"
, ddg_sdm = "SDM"
, ddg_duet = "DUET-d"
, dds_encom = "ENCoM-DDS"
, mcsm_na_affinity = "mCSM-NA"
, mcsm_ppi2_affinity = "mCSM-PPI2")
# COMMENT: This only works when all the columns are in the namekey vector.
# If one is missing, there is no error, but it also renamed as "NA.
#names(corr_df) <- colnames_display_key[names(corr_df)]
# Solution: to use plyr::rename()
# Consider using requireNamespace() instead of library() so its function names doesn't collide with dplyr's.
corr_df = plyr::rename(corr_df
, replace = colnames_display_key
, warn_missing = T
, warn_duplicated = T)
cat("\nExtracted ncols:", ncol(corr_df)
,"\nRenaming successful")
cat("\nSneak peak...")
#print(head(corr_df))
# Move drug column to the end
last_col = colnames(corr_df[ncol(corr_df)])
corr_df_f = corr_df %>% dplyr::relocate(all_of(drug), .after = last_col)
return(corr_df_f)
}
}