generated simple affinity plots for embb
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1 changed files with 204 additions and 233 deletions
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@ -1,9 +1,9 @@
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#source("~/git/LSHTM_analysis/config/pnca.R")
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#source("~/git/LSHTM_analysis/config/alr.R")
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#source("~/git/LSHTM_analysis/config/gid.R")
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#source("~/git/LSHTM_analysis/config/embb.R")
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source("~/git/LSHTM_analysis/config/embb.R")
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#source("~/git/LSHTM_analysis/config/katg.R")
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source("~/git/LSHTM_analysis/config/rpob.R")
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#source("~/git/LSHTM_analysis/config/rpob.R")
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source("/home/tanu/git/LSHTM_analysis/my_header.R")
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#########################################################
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@ -11,65 +11,27 @@ source("/home/tanu/git/LSHTM_analysis/my_header.R")
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# across all affinity tools for a given structure
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# as applicable...
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#########################################################
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#=======
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# output
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#=======
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outdir_images = paste0("~/git/Writing/thesis/images/results/", tolower(gene))
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#OutFile1
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outfile_mean_aff = paste0(outdir_images, "/", tolower(gene)
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, "_mean_affinity_all.csv")
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print(paste0("Output file:", outfile_mean_aff))
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#OutFile2
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outfile_mean_aff_priorty = paste0(outdir_images, "/", tolower(gene)
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, "_mean_affinity_priority.csv")
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print(paste0("Output file:", outfile_mean_aff_priorty))
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#%%===============================================================
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#=============
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# Input
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#=============
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df3_filename = paste0("/home/tanu/git/Data/", drug, "/output/", tolower(gene), "_merged_df3.csv")
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df3 = read.csv(df3_filename)
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length(df3$mutationinformation)
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# mut_info checks
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table(df3$mutation_info)
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table(df3$mutation_info_orig)
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table(df3$mutation_info_labels_orig)
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# used in plots and analyses
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table(df3$mutation_info_labels) # different, and matches dst_mode
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table(df3$dst_mode)
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# create column based on dst mode with different colname
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table(is.na(df3$dst))
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table(is.na(df3$dst_mode))
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#===============
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# Create column: sensitivity mapped to dst_mode
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#===============
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df3$sensitivity = ifelse(df3$dst_mode == 1, "R", "S")
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table(df3$sensitivity)
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length(unique((df3$mutationinformation)))
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all_colnames = as.data.frame(colnames(df3))
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all_colnames= colnames(df3)
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#%%===============================================================
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# FIXME: ADD distance to NA when SP replies
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dist_columns = c("ligand_distance", "interface_dist")
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DistCutOff = 10
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common_cols = c("mutationinformation"
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, "position"
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, "X5uhc_position"
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, "X5uhc_offset"
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, "position"
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, "dst_mode"
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, "mutation_info_labels"
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, "sensitivity"
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, "sensitivity", dist_columns )
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, "ligand_distance"
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, "interface_dist")
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all_colnames$`colnames(df3)`[grep("scaled", all_colnames$`colnames(df3)`)]
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all_colnames$`colnames(df3)`[grep("outcome", all_colnames$`colnames(df3)`)]
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all_colnames[grep("scaled" , all_colnames)]
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all_colnames[grep("outcome" , all_colnames)]
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#===================
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# stability cols
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@ -106,7 +68,6 @@ outcome_cols_affinity = c( "ligand_outcome"
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, "mmcsm_lig_outcome"
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, "mcsm_ppi2_outcome"
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, "mcsm_na_outcome")
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#===================
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# conservation cols
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#===================
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@ -123,223 +84,233 @@ outcome_cols_affinity = c( "ligand_outcome"
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# , "snap2_outcome"
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# #consurf outcome doesn't exist
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# )
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all_cols= c(common_cols
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,raw_cols_stability, scaled_cols_stability, outcome_cols_stability
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, raw_cols_affinity, scaled_cols_affinity, outcome_cols_affinity)
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#=======
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# output
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#=======
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outdir_images = paste0("~/git/Writing/thesis/images/results/", tolower(gene))
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######################################################################
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cols_to_consider = colnames(df3)[colnames(df3)%in%c(common_cols
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, raw_cols_affinity
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, scaled_cols_affinity
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, outcome_cols_affinity
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, raw_cols_stability
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, scaled_cols_stability
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, outcome_cols_stability
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)]
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#OutFile1
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outfile_mean_aff = paste0(outdir_images, "/", tolower(gene)
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, "_mean_ligand.csv")
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print(paste0("Output file:", outfile_mean_aff))
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cols_to_extract = cols_to_consider[cols_to_consider%in%c(common_cols
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#OutFile2
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outfile_ppi2 = paste0(outdir_images, "/", tolower(gene)
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, "_mean_ppi2.csv")
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print(paste0("Output file:", outfile_ppi2))
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#OutFile4
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#outfile_mean_aff_priorty = paste0(outdir_images, "/", tolower(gene)
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# , "_mean_affinity_priority.csv")
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#print(paste0("Output file:", outfile_mean_aff_priorty))
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#################################################################
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#################################################################
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# mut positions
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length(unique(df3$position))
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# mut_info checks
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table(df3$mutation_info)
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table(df3$mutation_info_orig)
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table(df3$mutation_info_labels_orig)
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# used in plots and analyses
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table(df3$mutation_info_labels) # different, and matches dst_mode
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table(df3$dst_mode)
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# create column based on dst mode with different colname
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table(is.na(df3$dst))
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table(is.na(df3$dst_mode))
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#===============
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# Create column: sensitivity mapped to dst_mode
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#===============
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df3$sensitivity = ifelse(df3$dst_mode == 1, "R", "S")
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table(df3$sensitivity)
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length(unique((df3$mutationinformation)))
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all_colnames = as.data.frame(colnames(df3))
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#===============
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# select columns specific to gene
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#===============
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gene_aff_cols = colnames(df3)[colnames(df3)%in%c(outcome_cols_affinity
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, scaled_cols_affinity)]
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df3_plot = df3[, cols_to_extract]
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##############################################################
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# FIXME: ADD distance to NA when SP replies
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gene_common_cols = colnames(df3)[colnames(df3)%in%common_cols]
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#####################
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# Ensemble affinity: affinity_cols
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# mcsm_lig, mmcsm_lig and mcsm_na
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#####################
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cols_to_extract = c(gene_common_cols
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, gene_aff_cols)
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cat("\nExtracting", length(cols_to_extract), "columns")
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df3_plot = df3[, cols_to_extract]
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table(df3_plot$mmcsm_lig_outcome)
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table(df3_plot$ligand_outcome)
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##############################################################
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# mCSM-lig, mCSM-NA, mCSM-ppi2, mmCSM-lig
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#########################################
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cols_to_numeric = c("ligand_outcome"
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, "mcsm_na_outcome"
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, "mcsm_ppi2_outcome"
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, "mmcsm_lig_outcome")
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#=====================================
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# mCSM-lig: Filter ligand distance <10
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#DistCutOff = 10
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#LigDist_colname = "ligand_distance"
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# extract outcome cols and map numeric values to the categories
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# Destabilising == 0, and stabilising == 1 so rescaling can let -1 be destabilising
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#########################################
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#=====================================
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# Affintiy (2 cols): average the scores
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# across predictors ==> average by
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# position ==> scale b/w -1 and 1
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# column to average: ens_affinity
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#=====================================
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cols_mcsm_lig = c("mutationinformation"
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df3_lig = df3[, c("mutationinformation"
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, "position"
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, "sensitivity"
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, "X5uhc_position"
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, "X5uhc_offset"
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, "ligand_distance"
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, "ligand_outcome"
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, "mmcsm_lig_outcome")
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cols_mcsm_lig
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df3_lig_ens = df3[, cols_mcsm_lig]
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, "ligand_affinity_change"
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, "affinity_scaled"
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, "ligand_outcome")]
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cols_to_numeric = c("ligand_outcome","mmcsm_lig_outcome")
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df3_lig_ens[, cols_to_numeric] <- sapply(df3_lig_ens[, cols_to_numeric]
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, function(x){ifelse(x == "Destabilising", 0, 1)})
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df3_lig = df3_lig[df3_lig["ligand_distance"]<DistCutOff,]
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cols_to_average_lig = which(colnames(df3_lig_ens)%in%cols_to_numeric)
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cols_to_average_lig
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expected_npos = sum(table(df3_lig["ligand_distance"]<DistCutOff))
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expected_npos
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# ensemble average across predictors
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df3_lig_ens$ens_lig = rowMeans(df3_lig_ens[,cols_to_average_lig])
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head(df3_lig_ens$position); head(df3_lig_ens$mutationinformation)
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head(df3_lig_ens$ens_lig)
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table(df3_lig_ens$ens_lig)
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#===============================
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# Filter ligand distance <10
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# from globals else uncomment
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#LigDist_cutoff = 10
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#LigDist_colname = "ligand_distance"
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#===============================
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table(df3_lig_ens[LigDist_colname]<LigDist_cutoff)
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expected_npos = table(df3_lig_ens$position[df3_lig_ens[LigDist_colname]<10])
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expected_npos = length(expected_npos)
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sum(table(df3_lig_ens$position[df3_lig_ens[LigDist_colname]<10]))
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df3_lig_ens_filtered = df3_lig_ens[df3_lig_ens[LigDist_colname]<10,]
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if ( nrow(df3_lig_ens_filtered) == sum(table(df3_lig_ens$position[df3_lig_ens[LigDist_colname]<10])) ){
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cat(paste0("\nPASS:", LigDist_colname, "filtered according to criteria:", LigDist_cutoff, angstroms_symbol ))
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if ( nrow(df3_lig) == expected_npos ){
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cat(paste0("\nPASS:", LigDist_colname, " filtered according to criteria:", LigDist_cutoff, angstroms_symbol ))
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}else{
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stop(paste0("\nAbort:", LigDist_colname, "could not be filtered according to criteria:", LigDist_cutoff, angstroms_symbol))
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stop(paste0("\nAbort:", LigDist_colname, " could not be filtered according to criteria:", LigDist_cutoff, angstroms_symbol))
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}
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# ensemble average of predictors by position
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mean_ens_lig_by_position <- df3_lig_ens_filtered %>%
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# group by position
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mean_lig_by_position <- df3_lig %>%
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dplyr::group_by(position) %>%
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dplyr::summarize(avg_ens_lig = mean(ens_lig))
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#dplyr::summarize(avg_lig = max(df3_lig_num))
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#dplyr::summarize(avg_lig = mean(ligand_outcome))
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#dplyr::summarize(avg_lig = mean(affinity_scaled, na.rm = T))
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dplyr::summarize(avg_lig = mean(ligand_affinity_change, na.rm = T))
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class(mean_ens_lig_by_position)
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class(mean_lig_by_position)
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# convert to a df
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mean_ens_lig_by_position = as.data.frame(mean_ens_lig_by_position)
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table(mean_ens_lig_by_position$avg_ens_lig)
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mean_lig_by_position = as.data.frame(mean_lig_by_position)
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table(mean_lig_by_position$avg_lig)
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# REscale b/w -1 and 1
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#en_aff_min = min(mean_ens_affinity_by_position['ens_affinity'])
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#en_aff_max = max(mean_ens_affinity_by_position['ens_affinity'])
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lig_min = min(mean_lig_by_position['avg_lig'])
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lig_max = max(mean_lig_by_position['avg_lig'])
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# scale the average affintiy value between -1 and 1
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# mean_ens_affinity_by_position['avg_ens_affinity_scaled'] = lapply(mean_ens_affinity_by_position['avg_ens_affinity']
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# , function(x) ifelse(x < 0, x/abs(en_aff_min), x/en_aff_max))
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mean_ens_lig_by_position['avg_ens_lig_scaled'] = lapply(mean_ens_lig_by_position['avg_ens_lig']
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mean_lig_by_position['avg_lig_scaled'] = lapply(mean_lig_by_position['avg_lig']
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, function(x) {
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scales::rescale(x, to = c(-1,1)
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#, from = c(en_aff_min,en_aff_max))
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, from = c(0,1))
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scales::rescale_mid(x
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, to = c(-1,1)
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, from = c(lig_min,lig_max)
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, mid = 0)
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#, from = c(0,1))
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})
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cat(paste0('Average (mcsm-lig+mmcsm-lig) scores:\n'
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, head(mean_ens_lig_by_position['avg_ens_lig'])
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, head(mean_lig_by_position['avg_lig'])
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, '\n---------------------------------------------------------------'
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, '\nAverage (mcsm-lig+mmcsm-lig) scaled scores:\n'
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, head(mean_ens_lig_by_position['avg_ens_lig_scaled'])))
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, head(mean_lig_by_position['avg_lig_scaled'])))
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if ( nrow(mean_ens_lig_by_position) == expected_npos ){
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cat("\nPASS: Generated ensemble average values for ligand affinity" )
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if ( nrow(mean_lig_by_position) == length(unique(df3_lig$position)) ){
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cat("\nPASS: Generated average values for ligand affinity" )
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}else{
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stop(paste0("\nAbort: length mismatch for ligand affinity data"))
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}
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max(mean_lig_by_position$avg_lig); min(mean_lig_by_position$avg_lig)
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max(mean_lig_by_position$avg_lig_scaled); min(mean_lig_by_position$avg_lig_scaled)
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#################################################################
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#=====================================
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# Affintiy (mCSM-ppi2):
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#D1148G for rpob DOES NOT EXIST for 5UHC
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#=====================================
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cols_mcsm_ppi2 = c("mutationinformation"
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, "position"
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, "X5uhc_position"
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, "X5uhc_offset"
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, "sensitivity"
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, "interface_dist"
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#, "mcsm_ppi2_affinity"
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#, "mcsm_ppi2_scaled"
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, "mcsm_ppi2_outcome"
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)
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cols_mcsm_ppi2
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df3_ppi2_raw = df3[, c(cols_mcsm_ppi2, "mcsm_ppi2_affinity", "mcsm_ppi2_scaled") ]
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table(df3_ppi2_raw$mcsm_ppi2_outcome)
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df3_ppi2 = df3[, cols_mcsm_ppi2]
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cols_to_numeric_ppi2 = c("mcsm_ppi2_outcome")
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df3_ppi2[, cols_to_numeric_ppi2] <- sapply(df3_ppi2[, cols_to_numeric_ppi2]
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, function(x){ifelse(x == "Descreasing", 0, 1)})
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cols_to_average_ppi2 = which(colnames(df3_ppi2)%in%cols_to_numeric_ppi2)
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cols_to_average_ppi2
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#===============================
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# Filter interface <10
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Dist_cutoff = 10
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ppi2Dist_colname = "interface_dist"
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#===============================
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table(df3_ppi2[ppi2Dist_colname]<Dist_cutoff)
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expected_npos = table(df3_ppi2$position[df3_ppi2[ppi2Dist_colname]<10])
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expected_npos = length(expected_npos)
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sum(table(df3_ppi2$position[df3_ppi2[ppi2Dist_colname]<10]))
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df3_ppi2_filtered = df3_ppi2[df3_ppi2[ppi2Dist_colname]<10,]
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if (tolower(gene)== "rpob"){
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check = nrow(df3_ppi2_filtered) == ( sum(table(df3_ppi2$position[df3_ppi2[ppi2Dist_colname]<10])) + 1)
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}else{
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check = nrow(df3_ppi2_filtered) == sum(table(df3_ppi2$position[df3_ppi2[ppi2Dist_colname]<10]))
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}
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if (check ){
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cat(paste0("\nPASS:", ppi2Dist_colname
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,"filtered according to criteria:"
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, Dist_cutoff
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, angstroms_symbol ))
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}else{
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stop(paste0("\nAbort:", ppi2Dist_colname
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, "could not be filtered according to criteria:"
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, Dist_cutoff, angstroms_symbol))
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}
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######################################################################
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##################
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# merge: mean ensemble stability and affinity by_position
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####################
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# if ( class(mean_ens_stability_by_position) && class(mean_ens_affinity_by_position) != "data.frame"){
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# cat("Y")
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# }
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common_cols = intersect(colnames(mean_ens_stability_by_position), colnames(mean_ens_affinity_by_position))
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if (dim(mean_ens_stability_by_position) && dim(mean_ens_affinity_by_position)){
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print(paste0("PASS: dim's match, mering dfs by column :", common_cols))
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#combined = as.data.frame(cbind(mean_duet_by_position, mean_affinity_by_position ))
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combined_df = as.data.frame(merge(mean_ens_stability_by_position
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, mean_ens_affinity_by_position
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, by = common_cols
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, all = T))
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cat(paste0("\nnrows combined_df:", nrow(combined_df)
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, "\nnrows combined_df:", ncol(combined_df)))
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}else{
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cat(paste0("FAIL: dim's mismatch, aborting cbind!"
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, "\nnrows df1:", nrow(mean_duet_by_position)
|
||||
, "\nnrows df2:", nrow(mean_affinity_by_position)))
|
||||
quit()
|
||||
}
|
||||
#%%============================================================
|
||||
# output
|
||||
write.csv(combined_df, outfile_mean_ens_st_aff
|
||||
write.csv(mean_lig_by_position, outfile_mean_aff
|
||||
, row.names = F)
|
||||
cat("Finished writing file:\n"
|
||||
, outfile_mean_ens_st_aff
|
||||
, "\nNo. of rows:", nrow(combined_df)
|
||||
, "\nNo. of cols:", ncol(combined_df))
|
||||
, outfile_mean_aff
|
||||
, "\nNo. of rows:", nrow(mean_lig_by_position)
|
||||
, "\nNo. of cols:", ncol(mean_lig_by_position))
|
||||
##################################################################
|
||||
##################################################################
|
||||
#=====================================
|
||||
# mCSM-ppi2: Filter interface_dist <10
|
||||
#DistCutOff = 10
|
||||
|
||||
#=====================================
|
||||
df3_ppi2 = df3[, c("mutationinformation"
|
||||
, "position"
|
||||
, "interface_dist"
|
||||
, "mcsm_ppi2_affinity"
|
||||
, "mcsm_ppi2_scaled"
|
||||
, "mcsm_ppi2_outcome")]
|
||||
|
||||
df3_ppi2 = df3_ppi2[df3_ppi2["interface_dist"]<DistCutOff,]
|
||||
|
||||
expected_npos = sum(table(df3_ppi2["interface_dist"]<DistCutOff))
|
||||
expected_npos
|
||||
|
||||
if ( nrow(df3_ppi2) == expected_npos ){
|
||||
cat(paste0("\nPASS:", "interface_dist", " filtered according to criteria:", LigDist_cutoff, angstroms_symbol ))
|
||||
}else{
|
||||
stop(paste0("\nAbort:", "interface_dist", " could not be filtered according to criteria:", LigDist_cutoff, angstroms_symbol))
|
||||
}
|
||||
|
||||
# group by position
|
||||
mean_ppi2_by_position <- df3_ppi2 %>%
|
||||
dplyr::group_by(position) %>%
|
||||
#dplyr::summarize(avg_ppi2 = max(df3_ppi2_num))
|
||||
#dplyr::summarize(avg_ppi2 = mean(mcsm_ppi2_outcome))
|
||||
#dplyr::summarize(avg_ppi2 = mean(mcsm_ppi2_scaled, na.rm = T))
|
||||
dplyr::summarize(avg_ppi2 = mean(mcsm_ppi2_affinity, na.rm = T))
|
||||
|
||||
class(mean_ppi2_by_position)
|
||||
|
||||
# convert to a df
|
||||
mean_ppi2_by_position = as.data.frame(mean_ppi2_by_position)
|
||||
table(mean_ppi2_by_position$avg_ppi2)
|
||||
|
||||
# REscale b/w -1 and 1
|
||||
lig_min = min(mean_ppi2_by_position['avg_ppi2'])
|
||||
lig_max = max(mean_ppi2_by_position['avg_ppi2'])
|
||||
|
||||
mean_ppi2_by_position['avg_ppi2_scaled'] = lapply(mean_ppi2_by_position['avg_ppi2']
|
||||
, function(x) {
|
||||
scales::rescale_mid(x
|
||||
, to = c(-1,1)
|
||||
, from = c(lig_min,lig_max)
|
||||
, mid = 0)
|
||||
#, from = c(0,1))
|
||||
})
|
||||
|
||||
cat(paste0('Average ppi2 scores:\n'
|
||||
, head(mean_ppi2_by_position['avg_ppi2'])
|
||||
, '\n---------------------------------------------------------------'
|
||||
, '\nAverage ppi2 scaled scores:\n'
|
||||
, head(mean_ppi2_by_position['avg_ppi2_scaled'])))
|
||||
|
||||
if ( nrow(mean_ppi2_by_position) == length(unique(df3_ppi2$position)) ){
|
||||
cat("\nPASS: Generated average values for ppi2" )
|
||||
}else{
|
||||
stop(paste0("\nAbort: length mismatch for ppi2 data"))
|
||||
}
|
||||
|
||||
max(mean_ppi2_by_position$avg_ppi2); min(mean_ppi2_by_position$avg_ppi2)
|
||||
max(mean_ppi2_by_position$avg_ppi2_scaled); min(mean_ppi2_by_position$avg_ppi2_scaled)
|
||||
|
||||
|
||||
write.csv(mean_ppi2_by_position, outfile_ppi2
|
||||
, row.names = F)
|
||||
cat("Finished writing file:\n"
|
||||
, outfile_ppi2
|
||||
, "\nNo. of rows:", nrow(mean_ppi2_by_position)
|
||||
, "\nNo. of cols:", ncol(mean_ppi2_by_position))
|
||||
|
||||
|
||||
# end of script
|
||||
#===============================================================
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue