updated docs for dm_om_data.R
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4 changed files with 54 additions and 179 deletions
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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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# corr_plot_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"
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# , "duet_affinity_change")
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#display_colnames_key = c(mutationinformation = "MUT"
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# , 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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# No formatting applied to these cols i.e display name
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# TO DO: SHINY
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#1) Corr type?
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#2)
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##################################################################
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corr_data_extract <- function(corr_plot_df
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#, gene_name = gene
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, drug_name = 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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cat("\nExtracting default columns for"
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#, "\nGene name:", gene
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, "\nDrug name:", drug)
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colnames_to_extract = c(drug
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#, "mutationinformation"
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, "mutation_info_labels"
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, "duet_stability_change"
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, "ligand_affinity_change"
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#, "ligand_distance"
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, ligand_dist_colname
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, "ddg_foldx"
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, "deepddg"
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, "asa"
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, "rsa"
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, "kd_values"
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, "rd_values"
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, "af"
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, "log10_or_mychisq"
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, "neglog_pval_fisher"
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, "ddg_dynamut2"
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, "consurf_score"
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, "snap2_score"
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, "ddg_dynamut", "ddg_encom", "dds_encom", "ddg_mcsm", "ddg_sdm", "ddg_duet"
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, "mcsm_na_affinity"
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, "mcsm_ppi2_affinity"
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)
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# [optional] arg: extract_scaled_cols
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if (extract_scaled_cols){
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cat("\nExtracting scaled columns as well...\n")
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all_scaled_cols = colnames(merged_df3)[grep(".*scaled", colnames(merged_df3))]
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colnames_to_extract = c(colnames_to_extract, all_scaled_cols)
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}else{
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colnames_to_extract = colnames_to_extract
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}
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corr_df = corr_plot_df[, colnames(corr_plot_df)%in%colnames_to_extract]
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# arg: colnames_display_key
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colnames_display_key = c(duet_stability_change = "DUET"
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, ligand_affinity_change = "mCSM-lig"
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#, ligand_distance = "ligand_distance"
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#, ligand_dist_colname = "ligand_distance"
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, ddg_foldx = "FoldX"
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, deepddg = "DeepDDG"
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, asa = "ASA"
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, rsa = "RSA"
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, kd_values = "KD"
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, rd_values = "RD"
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, af = "MAF"
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, log10_or_mychisq = "Log (OR)"
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, neglog_pval_fisher = "-Log (P)"
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, ddg_dynamut2 = "Dynamut2"
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, consurf_score = "Consurf"
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, snap2_score = "SNAP2"
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, ddg_dynamut = "Dynamut"
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, ddg_encom = "ENCoM-DDG"
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, ddg_mcsm = "mCSM"
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, ddg_sdm = "SDM"
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, ddg_duet = "DUET-d"
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, dds_encom = "ENCoM-DDS"
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, mcsm_na_affinity = "mCSM-NA"
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, mcsm_ppi2_affinity = "mCSM-PPI2")
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# COMMENT: This only works when all the columns are in the namekey vector.
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# If one is missing, there is no error, but it also renamed as "NA.
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#names(corr_df) <- colnames_display_key[names(corr_df)]
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# Solution: to use plyr::rename()
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# Consider using requireNamespace() instead of library() so its function names doesn't collide with dplyr's.
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corr_df = plyr::rename(corr_df
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, replace = colnames_display_key
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, warn_missing = T
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, warn_duplicated = T)
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cat("\nExtracted ncols:", ncol(corr_df)
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,"\nRenaming successful")
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cat("\nSneak peak...")
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print(head(corr_df))
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# Move drug column to the end
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last_col = colnames(corr_df[ncol(corr_df)])
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corr_df_f = corr_df %>% dplyr::relocate(all_of(drug), .after = last_col)
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return(corr_df_f)
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}
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}
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@ -1,28 +1,40 @@
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#!/usr/bin/env Rscript
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#########################################################
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# TASK: Script to format data for dm om plots:
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# generating WF and LF data for each of the parameters
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# duet, mcsm-lig, foldx, deepddg, dynamut2, mcsm-na, mcsm-ppi2, encom, dynamut..etc
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# generating WF and LF data for each of the parameters:
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# duet, mcsm-lig, foldx, deepddg, dynamut2, mcsm-na, mcsm-ppi2, encom, dynamut..etc
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# Called by get_plotting_dfs.R
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# dm_om_wf_lf_data()
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# Input: data with all parameters (merged_df3, my_use case)
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# gene: [conditional generation of dfs like mcsm-NA, mcsm-ppi2 as not all genes have all these values]
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# colnames_to_extract = c("mutationinformation"
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# , "duet_affinity_change...")
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# ligand_dist_colname = LigDist_colname # from globals
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# dr_muts = dr_muts_col # from globals ...dr_mutations_<drug>
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# other_muts = other_muts_col # from globals ...other_mutations_<drug>
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# snp_colname = "mutationinformation"
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# aa_pos_colname = "position" # to sort df by
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# mut_colname = "mutation"
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# mut_info_colname = "mutation_info"
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# mut_info_label_colname = "mutation_info_labels" # if empty, below used
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# dr_other_muts_labels = c("DM", "OM") # only used if ^^ = ""
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# categ_cols_to_factor: converts the cols with '_outcome'and 'info' to factor
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# INPUT:
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# df: merged_df3 (data with all parameters)
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# NOTE*: merged_df2 will not be appropriate as it brings up most params as significant!?,atleast for gid
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# gene: [conditional generation of dfs like mcsm-NA, mcsm-ppi2 as not all genes have all these values]
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# colnames_to_extract : columns to extract, either user-specified.
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#By default it is c("mutationinformation" , "duet_affinity_change...")
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# ligand_dist_colname : column name containing ligand distance. By deafult, it is LigDist_colname (imported from globals)
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# dr_muts : dr_muts_col (imported from globals; dr_mutations_<drug>)
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# other_muts : other_muts_col (imported from globals ...other_mutations_<drug>)
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# snp_colname : SNP column name. By default it is "mutationinformation"
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# aa_pos_colname : Column name containing the aa position. This is used to sort the df by.
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# mut_colname : Column name containing snp info in format "<abc_pXXdef>. By default, it is "mutation"
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# mut_info_colname : Column name containing mutation info whether it is DM or OM. By default, it is "mutation_info"
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# mut_info_label_colname : Column containing pre-formatted labels for mutation info.
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# For my use case, this is called "mutation_info_labels"
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# This column has short labels like DM and OM coresponding to dr_muts and other_muts.
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# NOTE*: if this is left empty, then the arg ('dr_other_muts_labels') will be used
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# dr_other_muts_labels : User specified labels, must correspond to dr_muts and other_muts.
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# NOTE*: Only used if the arg (mut_info_label_colname) is empty!
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# categ_cols_to_factor : Column names to convert to factors. These mainly correspond to the outcome columns associated with the
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# arg ('colnames_to_extract'). These have the suffix "_outcome" in their colnames. Additionally column 'mutation_info' is also
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# converted to factor. By default, it converts the cols with '_outcome'and 'info' to factor.
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# Users are able to provide a vector of their corresponding column names
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# RETURNS: List
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# WF nd LF data grouped by mutation_info i.e DM (drug mutations) and OM (other mutations)
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# TO DO: SHINY
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#1)
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#1) df to choose (merged_df3 or merged_df2)
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#2)
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##################################################################
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dm_om_wf_lf_data <- function(df
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@ -48,7 +60,7 @@ dm_om_wf_lf_data <- function(df
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# common_dfs
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common_dfsL = list(
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wf_duet = data.frame()
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wf_duet = data.frame()
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, lf_duet = data.frame()
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, wf_mcsm_lig = data.frame()
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, lf_mcsm_lig = data.frame()
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#!/usr/bin/env Rscript
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source("~/git/LSHTM_analysis/config/gid.R")
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source("~/git/LSHTM_analysis/scripts/plotting/get_plotting_dfs.R")
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m3 = corr_data_extract(merged_df3); head(m3)
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m2 = corr_data_extract(meregd_df2); head(m2)
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m3S = corr_data_extract(merged_df3, extract_scaled_cols = T); head(m3S)
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@ -88,13 +88,6 @@ merged_df3 = all_plot_dfs[[2]]
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merged_df2_comp = all_plot_dfs[[3]]
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merged_df3_comp = all_plot_dfs[[4]]
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#======================================================================
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####################################################################
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# Data for combining other dfs
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####################################################################
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#source("other_dfs_data.R")
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# Fixed this at source i.e python script
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# Moved: "other_dfs_data.R" to redundant/
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####################################################################
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# Data for subcols barplot (~heatmap)
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@ -109,35 +102,39 @@ merged_df3_comp = all_plot_dfs[[4]]
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# Data for logoplots
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####################################################################
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#source(paste0(plot_script_path, "logo_data.R"))
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#s1 = c("\nSuccessfully sourced logo_data.R")
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#source(paste0(plot_script_path, "logo_data_msa.R"))
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#s1 = c("\nSuccessfully sourced logo_data_msa.R")
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#cat(s1)
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# input data is merged_df3
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# so repurposed it into a function so params can be passed instead to generate
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# data required for plotting.
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# Moved "logo_data.R" to redundant/
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source(paste0(plot_script_path, "logo_data_msa.R"))
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s1 = c("\nSuccessfully sourced logo_data_msa.R")
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cat(s1)
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####################################################################
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# Data for DM OM Plots: WF and LF dfs
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# My function: dm_om_wf_lf_data()
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####################################################################
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# My function: dm_om_wf_lf_data()
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# location: scripts/functions/dm_om_data.R
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#source("other_plots_data.R")
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# converted to a function
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# moved old one to redundant.
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source(paste0(plot_script_path, "dm_om_data.R"))
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s2 = c("\nSuccessfully sourced other_plots_data.R")
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cat(s2)
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####################################################################
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#source(paste0(plot_script_path, "dm_om_data.R"))
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#s2 = c("\nSuccessfully sourced other_plots_data.R")
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#cat(s2)
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####################################################################
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# Data for Lineage barplots: WF and LF dfs
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# My function: lineage_plot_data()
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# location: scripts/functions/lineage_plot_data.R
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####################################################################
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source(paste0(plot_script_path, "lineage_data.R"))
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#source(paste0(plot_script_path, "lineage_data.R"))
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# converted to a function. Moved lineage_data.R to redundant/
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lineage_dfL = lineage_plot_data(df = merged_df2
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, lineage_column_name = "lineage"
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, remove_empty_lineage = F
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, lineage_label_col_name = "lineage_labels"
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, id_colname = "id"
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, snp_colname = "mutationinformation"
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)
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lin_wf = lineage_dfL[['lin_wf']]
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lin_lf = lineage_dfL[['lin_lf']]
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s3 = c("\nSuccessfully sourced lineage_data.R")
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cat(s3)
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@ -145,6 +142,7 @@ cat(s3)
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####################################################################
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# Data for corr plots:
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# My function: corr_data_extract()
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# location: scripts/functions/corr_plot_data.R
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####################################################################
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# make sure the above script works because merged_df2_combined is needed
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