moved corr_data.R to redundant/
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1035547309
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cd772e9df1
2 changed files with 149 additions and 412 deletions
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#!/usr/bin/env Rscript
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#########################################################
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# TASK: Script to format data for corr plots
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#########################################################
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#library(dplyr)
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#=================================================
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# Data for Corrplots
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#=================================================
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cat("\n=========================================="
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, "\nCORR PLOTS data: ALL params"
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, "\n=========================================")
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# use data
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#merged_df2
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geneL_normal = c("pnca")
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geneL_na_dy = c("gid")
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geneL_na = c("rpob")
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geneL_ppi2 = c("alr", "embb", "katg", "rpob")
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#----------------------------
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# columns for corr plots:PS
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#----------------------------
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# NOTE: you can add mcsm_ppi column as well, and it will only select what it can find!
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big_df_colnames = data.frame(names(merged_df2))
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core_cols = c("mutationinformation", drug, "mutation_info_labels"
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, "duet_stability_change", "ligand_affinity_change", "ddg_foldx", "asa", "rsa"
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, "rd_values", "kd_values", "log10_or_mychisq", "neglog_pval_fisher","af"
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, "deepddg" , "ddg_dynamut2"
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, "consurf_score"
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#, "consurf_scaled"
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, "snap2_score"
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#, "snap2_scaled", "snap2_accuracy_pc"
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, "ligand_distance")
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if (tolower(gene)%in%geneL_normal){
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corr_cols_select = core_cols
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}
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if (tolower(gene)%in%geneL_na_dy){
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additional_cols = c("mcsm_na_affinity"
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, "ddg_dynamut"
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, "ddg_encom", "dds_encom"
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, "ddg_mcsm", "ddg_sdm"
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, "ddg_duet"
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#, "mcsm_na_scaled"
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#, "ddg_dynamut_scaled"
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#, "ddg_encom_scaled", "dds_encom_scaled"
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#, "ddg_mcsm_scaled", "ddg_sdm_scaled"
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#, "ddg_duet_scaled"
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)
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corr_cols_select = c(core_cols, additional_cols)
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}
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if (tolower(gene)%in%geneL_na){
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additional_cols = c("mcsm_na_affinity"
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#, "mcsm_na_scaled"
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)
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corr_cols_select = c(core_cols, additional_cols)
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}
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if (tolower(gene)%in%geneL_ppi2){
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additional_cols = c("mcsm_ppi2_affinity")
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corr_cols_select = c(core_cols, additional_cols)
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}
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# corr_cols_select <- c("mutationinformation", drug, "mutation_info_labels"
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# , "duet_stability_change", "ligand_affinity_change", "ddg_foldx", "asa", "rsa"
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# , "rd_values", "kd_values", "log10_or_mychisq", "neglog_pval_fisher","af"
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# , "deepddg", "ddg_dynamut", "ddg_dynamut2", "mcsm_na_affinity"
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# , "ddg_encom", "dds_encom", "ddg_mcsm", "ddg_sdm", "ddg_duet", "ligand_distance")
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#===========================
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# Corr data for plots: PS
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# big_df ps: ~ merged_df2
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#===========================
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corr_df_m2 = merged_df2[,colnames(merged_df2)%in%corr_cols_select]
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#-----------------------
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# formatting: some cols
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# Add pretty colnames
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#-----------------------
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corr_df_m2_f <- corr_df_m2 %>% dplyr::rename(
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'DUET' = duet_stability_change
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, 'mCSM-lig' = ligand_affinity_change
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, FoldX = ddg_foldx
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, DeepDDG = deepddg
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, ASA = asa
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, RSA = rsa
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, KD = kd_values
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, RD = rd_values
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, MAF = af
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, 'Log (OR)' = log10_or_mychisq
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, '-Log (P)' = neglog_pval_fisher
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, Dynamut = ddg_dynamut
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, 'ENCoM-DDG'= ddg_encom
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, mCSM = ddg_mcsm
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, SDM = ddg_sdm
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, 'DUET-d' = ddg_duet
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, 'ENCoM-DDS'= dds_encom
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, Dynamut2 = ddg_dynamut2
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, 'mCSM-NA' = mcsm_na_affinity )
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#===========================
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# Corr data for plots: PS
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# short_df ps: ~merged_df3
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#===========================
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corr_df_m3 = corr_df_m2[!duplicated(corr_df_m2$mutationinformation),]
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na_or = sum(is.na(corr_df_m3$log10_or_mychisq))
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check1 = nrow(corr_df_m3) - na_or; check1
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if (nrow(corr_df_m3) == nrow(merged_df3) && nrow(merged_df3_comp) == check1) {
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cat( "\nPASS: No. of rows for corr_df_m3 match"
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, "\nPASS: No. of OR values checked: " , check1)
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} else {
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cat("\nFAIL: Numbers mismatch:"
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, "\nExpected nrows: ", nrow(merged_df3)
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, "\nGot: ", nrow(corr_df_m3)
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, "\nExpected OR values: ", nrow(merged_df3_comp)
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, "\nGot: ", check1)
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}
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#-----------------------
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# formatting: some cols
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# Add pretty colnames
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#-----------------------
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corr_df_m3_f <- corr_df_m3 %>%
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rename(
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DUET = duet_stability_change
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, 'mCSM-lig' = ligand_affinity_change
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, FoldX = ddg_foldx
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, DeepDDG = deepddg
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, ASA = asa
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, RSA = rsa
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, KD = kd_values
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, RD = rd_values
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, MAF = af
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, 'Log (OR)' = log10_or_mychisq
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, '-Log (P)' = neglog_pval_fisher
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, Dynamut = ddg_dynamut
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, 'ENCoM-DDG'= ddg_encom
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, mCSM = ddg_mcsm
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, SDM = ddg_sdm
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, 'DUET-d' = ddg_duet
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, 'ENCoM-DDS'= dds_encom
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, Dynamut2 = ddg_dynamut2
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, 'mCSM-NA' = mcsm_na_affinity )
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########################################################################
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cat("\nCorr Data created:"
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, "\n==================================="
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, "\ncorr_df_m2: created from merged_df2"
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, "\n==================================="
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, "\nnrows:", nrow(corr_df_m2)
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, "\nncols:", ncol(corr_df_m2)
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, "\n==================================="
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, "\ncorr_df_m3: created from merged_df3"
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, "\n==================================="
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, "\nnrows:", nrow(corr_df_m3)
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, "\nncols:", ncol(corr_df_m3)
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)
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#!/usr/bin/env Rscript
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#!/usr/bin/env Rscript
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#########################################################
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#########################################################
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# TASK: Prepare for correlation data
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# TASK: Script to format data for corr plots
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#########################################################
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#library(dplyr)
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#=======================================================================
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#=================================================
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# working dir and loading libraries
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# Data for Corrplots
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getwd()
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#=================================================
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setwd("~/git/LSHTM_analysis/scripts/plotting")
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cat("\n=========================================="
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getwd()
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, "\nCORR PLOTS data: ALL params"
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, "\n=========================================")
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#source("~/git/LSHTM_analysis/scripts/Header_TT.R")
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# use data
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source("../functions/my_pairs_panel.R") # with lower panel turned off
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#merged_df2
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source("../functions/plotting_globals.R")
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geneL_normal = c("pnca")
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source("../functions/plotting_data.R")
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geneL_na_dy = c("gid")
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source("../functions/combining_dfs_plotting.R")
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geneL_na = c("rpob")
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###########################################################
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geneL_ppi2 = c("alr", "embb", "katg", "rpob")
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#===========
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# input
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#===========
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#---------------------
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# call: import_dirs()
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#---------------------
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import_dirs(drug, gene)
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#---------------------------
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#----------------------------
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# call: plotting_data()
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# columns for corr plots:PS
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#---------------------------
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#----------------------------
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#if (!exists("infile_params") && exists("gene")){
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# NOTE: you can add mcsm_ppi column as well, and it will only select what it can find!
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if (!is.character(infile_params) && exists("gene")){ # when running as cmd
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big_df_colnames = data.frame(names(merged_df2))
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#in_filename_params = paste0(tolower(gene), "_all_params.csv")
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in_filename_params = paste0(tolower(gene), "_comb_afor.csv") # part combined for gid
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core_cols = c("mutationinformation", drug, "mutation_info_labels"
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infile_params = paste0(outdir, "/", in_filename_params)
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, "duet_stability_change", "ligand_affinity_change", "ddg_foldx", "asa", "rsa"
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cat("\nInput file for mcsm comb data not specified, assuming filename: ", infile_params, "\n")
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, "rd_values", "kd_values", "log10_or_mychisq", "neglog_pval_fisher","af"
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, "deepddg" , "ddg_dynamut2"
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, "consurf_score"
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#, "consurf_scaled"
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, "snap2_score"
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#, "snap2_scaled", "snap2_accuracy_pc"
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, "ligand_distance")
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if (tolower(gene)%in%geneL_normal){
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corr_cols_select = core_cols
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}
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if (tolower(gene)%in%geneL_na_dy){
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additional_cols = c("mcsm_na_affinity"
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, "ddg_dynamut"
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, "ddg_encom", "dds_encom"
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, "ddg_mcsm", "ddg_sdm"
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, "ddg_duet"
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#, "mcsm_na_scaled"
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#, "ddg_dynamut_scaled"
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#, "ddg_encom_scaled", "dds_encom_scaled"
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#, "ddg_mcsm_scaled", "ddg_sdm_scaled"
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#, "ddg_duet_scaled"
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)
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corr_cols_select = c(core_cols, additional_cols)
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}
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}
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# Input 1: read <gene>_comb_afor.csv
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if (tolower(gene)%in%geneL_na){
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cat("\nReading mcsm combined data file: ", infile_params)
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additional_cols = c("mcsm_na_affinity"
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mcsm_df = read.csv(infile_params, header = T)
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#, "mcsm_na_scaled"
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pd_df = plotting_data(mcsm_df)
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)
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my_df_u = pd_df[[1]] # this forms one of the input for combining_dfs_plotting()
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corr_cols_select = c(core_cols, additional_cols)
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#--------------------------------
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# call: combining_dfs_plotting()
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#--------------------------------
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#if (!exists("infile_metadata") && exists("gene")){
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if (!is.character(infile_metadata) && exists("gene")){ # when running as cmd
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in_filename_metadata = paste0(tolower(gene), "_metadata.csv") # part combined for gid
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infile_metadata = paste0(outdir, "/", in_filename_metadata)
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cat("\nInput file for gene metadata not specified, assuming filename: ", infile_metadata, "\n")
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}
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}
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# Input 2: read <gene>_meta data.csv
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if (tolower(gene)%in%geneL_ppi2){
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cat("\nReading meta data file: ", infile_metadata)
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additional_cols = c("mcsm_ppi2_affinity")
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corr_cols_select = c(core_cols, additional_cols)
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}
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gene_metadata <- read.csv(infile_metadata
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# corr_cols_select <- c("mutationinformation", drug, "mutation_info_labels"
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, stringsAsFactors = F
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# , "duet_stability_change", "ligand_affinity_change", "ddg_foldx", "asa", "rsa"
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, header = T)
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# , "rd_values", "kd_values", "log10_or_mychisq", "neglog_pval_fisher","af"
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# , "deepddg", "ddg_dynamut", "ddg_dynamut2", "mcsm_na_affinity"
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# , "ddg_encom", "dds_encom", "ddg_mcsm", "ddg_sdm", "ddg_duet", "ligand_distance")
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all_plot_dfs = combining_dfs_plotting(my_df_u
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#===========================
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, gene_metadata
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# Corr data for plots: PS
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, lig_dist_colname = 'ligand_distance'
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# big_df ps: ~ merged_df2
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, lig_dist_cutoff = 10)
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#===========================
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cat(paste0("Directories imported:"
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corr_df_m2 = merged_df2[,colnames(merged_df2)%in%corr_cols_select]
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, "\ndatadir:", datadir
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, "\nindir:", indir
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, "\noutdir:", outdir
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, "\nplotdir:", plotdir))
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cat(paste0("Variables imported:"
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#-----------------------
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, "\ndrug:", drug
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# formatting: some cols
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, "\ngene:", gene
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# Add pretty colnames
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, "\ngene_match:", gene_match
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#-----------------------
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, "\nAngstrom symbol:", angstroms_symbol
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corr_df_m2_f <- corr_df_m2 %>% dplyr::rename(
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, "\nNo. of duplicated muts:", dup_muts_nu
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'DUET' = duet_stability_change
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, "\nNA count for ORs:", na_count
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, 'mCSM-lig' = ligand_affinity_change
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, "\nNA count in df2:", na_count_df2
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, FoldX = ddg_foldx
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, "\nNA count in df3:", na_count_df3))
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, DeepDDG = deepddg
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, ASA = asa
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, RSA = rsa
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, KD = kd_values
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, RD = rd_values
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, MAF = af
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, 'Log (OR)' = log10_or_mychisq
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, '-Log (P)' = neglog_pval_fisher
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, Dynamut = ddg_dynamut
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, 'ENCoM-DDG'= ddg_encom
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, mCSM = ddg_mcsm
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, SDM = ddg_sdm
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, 'DUET-d' = ddg_duet
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, 'ENCoM-DDS'= dds_encom
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, Dynamut2 = ddg_dynamut2
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, 'mCSM-NA' = mcsm_na_affinity )
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#=======
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#===========================
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# output
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# Corr data for plots: PS
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#=======
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# short_df ps: ~merged_df3
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# corr_ps_df2
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#===========================
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# corr_lig_df2
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####################################################################
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corr_df_m3 = corr_df_m2[!duplicated(corr_df_m2$mutationinformation),]
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# end of loading libraries and functions
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####################################################################
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#%%%%%%%%%%%%%%%%%%%%%%%%%
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na_or = sum(is.na(corr_df_m3$log10_or_mychisq))
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#df_ps = merged_df3
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check1 = nrow(corr_df_m3) - na_or; check1
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df_ps = merged_df2
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#df_lig = merged_df3_lig
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if (nrow(corr_df_m3) == nrow(merged_df3) && nrow(merged_df3_comp) == check1) {
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df_lig = merged_df2_lig
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cat( "\nPASS: No. of rows for corr_df_m3 match"
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, "\nPASS: No. of OR values checked: " , check1)
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#%%%%%%%%%%%%%%%%%%%%%%%%%
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} else {
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cat("\nFAIL: Numbers mismatch:"
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, "\nExpected nrows: ", nrow(merged_df3)
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, "\nGot: ", nrow(corr_df_m3)
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, "\nExpected OR values: ", nrow(merged_df3_comp)
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, "\nGot: ", check1)
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}
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|
|
||||||
|
#-----------------------
|
||||||
|
# formatting: some cols
|
||||||
|
# Add pretty colnames
|
||||||
|
#-----------------------
|
||||||
|
corr_df_m3_f <- corr_df_m3 %>%
|
||||||
|
rename(
|
||||||
|
DUET = duet_stability_change
|
||||||
|
, 'mCSM-lig' = ligand_affinity_change
|
||||||
|
, FoldX = ddg_foldx
|
||||||
|
, DeepDDG = deepddg
|
||||||
|
, ASA = asa
|
||||||
|
, RSA = rsa
|
||||||
|
, KD = kd_values
|
||||||
|
, RD = rd_values
|
||||||
|
, MAF = af
|
||||||
|
, 'Log (OR)' = log10_or_mychisq
|
||||||
|
, '-Log (P)' = neglog_pval_fisher
|
||||||
|
, Dynamut = ddg_dynamut
|
||||||
|
, 'ENCoM-DDG'= ddg_encom
|
||||||
|
, mCSM = ddg_mcsm
|
||||||
|
, SDM = ddg_sdm
|
||||||
|
, 'DUET-d' = ddg_duet
|
||||||
|
, 'ENCoM-DDS'= dds_encom
|
||||||
|
, Dynamut2 = ddg_dynamut2
|
||||||
|
, 'mCSM-NA' = mcsm_na_affinity )
|
||||||
|
|
||||||
########################################################################
|
########################################################################
|
||||||
# end of data extraction and cleaning for plots #
|
cat("\nCorr Data created:"
|
||||||
########################################################################
|
, "\n==================================="
|
||||||
|
, "\ncorr_df_m2: created from merged_df2"
|
||||||
#======================
|
, "\n==================================="
|
||||||
# adding log cols
|
, "\nnrows:", nrow(corr_df_m2)
|
||||||
#======================
|
, "\nncols:", ncol(corr_df_m2)
|
||||||
df_ps$log10_or_mychisq = log10(df_ps$or_mychisq)
|
, "\n==================================="
|
||||||
df_ps$neglog_pval_fisher = -log10(df_ps$pval_fisher)
|
, "\ncorr_df_m3: created from merged_df3"
|
||||||
|
, "\n==================================="
|
||||||
df_ps$log10_or_kin = log10(df_ps$or_kin)
|
, "\nnrows:", nrow(corr_df_m3)
|
||||||
df_ps$neglog_pwald_kin = -log10(df_ps$pwald_kin)
|
, "\nncols:", ncol(corr_df_m3)
|
||||||
|
)
|
||||||
#df_ps$mutation_info_labels = ifelse(df_ps$mutation_info == dr_muts_col, 1, 0)
|
|
||||||
|
|
||||||
#===============================
|
|
||||||
# Data for Correlation plots:PS
|
|
||||||
#===============================
|
|
||||||
# subset data to generate pairwise correlations
|
|
||||||
cols_to_select = c("mutationinformation"
|
|
||||||
, "duet_scaled"
|
|
||||||
, "foldx_scaled"
|
|
||||||
#, "mutation_info_labels"
|
|
||||||
, "asa"
|
|
||||||
, "rsa"
|
|
||||||
, "rd_values"
|
|
||||||
, "kd_values"
|
|
||||||
, "log10_or_mychisq"
|
|
||||||
, "neglog_pval_fisher"
|
|
||||||
, "or_kin"
|
|
||||||
, "neglog_pwald_kin"
|
|
||||||
, "af"
|
|
||||||
#, "af_kin"
|
|
||||||
, "duet_outcome"
|
|
||||||
, drug)
|
|
||||||
|
|
||||||
corr_data_ps = df_ps[cols_to_select]
|
|
||||||
|
|
||||||
dim(corr_data_ps)
|
|
||||||
|
|
||||||
# assign nice colnames (for display)
|
|
||||||
my_corr_colnames = c("Mutation"
|
|
||||||
, "DUET"
|
|
||||||
, "Foldx"
|
|
||||||
#, "Mutation class"
|
|
||||||
, "ASA"
|
|
||||||
, "RSA"
|
|
||||||
, "RD"
|
|
||||||
, "KD"
|
|
||||||
, "Log (OR)"
|
|
||||||
, "-Log (P)"
|
|
||||||
, "Adjusted (OR)"
|
|
||||||
, "-Log (P wald)"
|
|
||||||
, "AF"
|
|
||||||
, "AF_kin"
|
|
||||||
, "duet_outcome"
|
|
||||||
, drug)
|
|
||||||
|
|
||||||
length(my_corr_colnames)
|
|
||||||
|
|
||||||
colnames(corr_data_ps)
|
|
||||||
colnames(corr_data_ps) <- my_corr_colnames
|
|
||||||
colnames(corr_data_ps)
|
|
||||||
|
|
||||||
start = 1
|
|
||||||
end = which(colnames(corr_data_ps) == drug); end # should be the last column
|
|
||||||
offset = 1
|
|
||||||
|
|
||||||
#corr_ps_df2 = corr_data_ps[start:(end-offset)] # without drug
|
|
||||||
corr_ps_df2 = corr_data_ps[start:end]
|
|
||||||
head(corr_ps_df2)
|
|
||||||
|
|
||||||
#--------------------------
|
|
||||||
# short_df ps: merged_df3
|
|
||||||
#--------------------------
|
|
||||||
corr_ps_df3 = corr_ps_df2[!duplicated(corr_ps_df2$Mutation),]
|
|
||||||
|
|
||||||
na_or = sum(is.na(corr_ps_df3$`Log (OR)`))
|
|
||||||
check1 = nrow(corr_ps_df3) - na_or
|
|
||||||
|
|
||||||
na_adj_or = sum(is.na(corr_ps_df3$`adjusted (OR)`))
|
|
||||||
check2 = nrow(corr_ps_df3) - na_adj_or
|
|
||||||
|
|
||||||
#if ( nrow(corr_ps_df3) == nrow(merged_df3) ) {
|
|
||||||
# cat( "PASS: No. of rows for corr_ps_df3 match" )
|
|
||||||
#}if ( nrow(merged_df3_comp) == check1 ){
|
|
||||||
# cat( "PASS: No. of OR values checked" )
|
|
||||||
#}
|
|
||||||
|
|
||||||
################################################################################################
|
|
||||||
#=================================
|
|
||||||
# Data for Correlation plots: LIG
|
|
||||||
#=================================
|
|
||||||
table(df_lig$ligand_outcome)
|
|
||||||
|
|
||||||
df_lig$log10_or_mychisq = log10(df_lig$or_mychisq)
|
|
||||||
df_lig$neglog_pval_fisher = -log10(df_lig$pval_fisher)
|
|
||||||
|
|
||||||
df_lig$log10_or_kin = log10(df_lig$or_kin)
|
|
||||||
df_lig$neglog_pwald_kin = -log10(df_lig$pwald_kin)
|
|
||||||
|
|
||||||
# subset data to generate pairwise correlations
|
|
||||||
cols_to_select = c("mutationinformation"
|
|
||||||
, "affinity_scaled"
|
|
||||||
#, "mutation_info_labels"
|
|
||||||
, "asa"
|
|
||||||
, "rsa"
|
|
||||||
, "rd_values"
|
|
||||||
, "kd_values"
|
|
||||||
, "log10_or_mychisq"
|
|
||||||
, "neglog_pval_fisher"
|
|
||||||
, "or_kin"
|
|
||||||
, "neglog_pwald_kin"
|
|
||||||
, "af"
|
|
||||||
, "af_kin"
|
|
||||||
, "ligand_outcome"
|
|
||||||
, drug)
|
|
||||||
|
|
||||||
corr_data_lig = df_lig[, cols_to_select]
|
|
||||||
|
|
||||||
|
|
||||||
dim(corr_data_lig)
|
|
||||||
|
|
||||||
# assign nice colnames (for display)
|
|
||||||
my_corr_colnames = c("Mutation"
|
|
||||||
, "Ligand Affinity"
|
|
||||||
#, "Mutation class"
|
|
||||||
, "ASA"
|
|
||||||
, "RSA"
|
|
||||||
, "RD"
|
|
||||||
, "KD"
|
|
||||||
, "Log (OR)"
|
|
||||||
, "-Log (P)"
|
|
||||||
, "Adjusted (OR)"
|
|
||||||
, "-Log (P wald)"
|
|
||||||
, "AF"
|
|
||||||
, "AF_kin"
|
|
||||||
, "ligand_outcome"
|
|
||||||
, drug)
|
|
||||||
|
|
||||||
length(my_corr_colnames)
|
|
||||||
|
|
||||||
colnames(corr_data_lig)
|
|
||||||
colnames(corr_data_lig) <- my_corr_colnames
|
|
||||||
colnames(corr_data_lig)
|
|
||||||
|
|
||||||
start = 1
|
|
||||||
end = which(colnames(corr_data_lig) == drug); end # should be the last column
|
|
||||||
offset = 1
|
|
||||||
|
|
||||||
#corr_lig_df2 = corr_data_lig[start:(end-offset)] # without drug
|
|
||||||
corr_lig_df2 = corr_data_lig[start:end]
|
|
||||||
head(corr_lig_df2)
|
|
||||||
|
|
||||||
#-----------------
|
|
||||||
# short_df lig: merged_df3_lig
|
|
||||||
#-----------------
|
|
||||||
|
|
||||||
corr_lig_df3 = corr_lig_df2[!duplicated(corr_lig_df2$Mutation),]
|
|
||||||
|
|
||||||
#######################################################
|
|
||||||
rm(merged_df2, merged_df2_lig, merged_df3, merged_df3_lig
|
|
||||||
, merged_df2_comp , merged_df3_comp, merged_df2_comp_lig, merged_df3_comp_lig
|
|
||||||
, corr_data_ps, corr_data_lig)
|
|
||||||
|
|
|
||||||
Loading…
Add table
Add a link
Reference in a new issue