aadded colnames to plot as names
This commit is contained in:
parent
4315adc556
commit
2f7558a883
3 changed files with 314 additions and 168 deletions
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@ -327,7 +327,6 @@ combining_dfs_plotting <- function( my_df_u
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stop("Cannot generate merged_df3")
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stop("Cannot generate merged_df3")
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}
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}
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##################################################################
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##################################################################
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head(merged_df3$position); tail(merged_df3$position) # should be sorted
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head(merged_df3$position); tail(merged_df3$position) # should be sorted
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# sanity check
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# sanity check
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@ -391,6 +390,301 @@ combining_dfs_plotting <- function( my_df_u
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stop("Abort: merged_df3 or merged_df2 can't be created because of lable mismatch")
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stop("Abort: merged_df3 or merged_df2 can't be created because of lable mismatch")
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}
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}
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##########################################################################
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# MERGED_df2: average cols #
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# Average stability + lig-affinity columns #
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##########################################################################
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#=====================================
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# merged_df2: Stability values: average
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#====================================
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#------------------------------
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# foldx sign reverse
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# for consistency with other tools
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#----------------------------------
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head(merged_df2$ddg_foldx)
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# foldx values: reverse signs
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#merged_df2['ddg_foldxC'] = abs(merged_df2$ddg_foldx)
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#head(merged_df2[, c("ddg_foldx", "ddg_foldxC")])
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# foldx scaled: reverse signs fs
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merged_df2['foldx_scaled_signC'] = abs(merged_df2$foldx_scaled)
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head(merged_df2[, c("foldx_scaled", "foldx_scaled_signC")])
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# find which stability cols to average: should contain revised foldx
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scaled_cols_stab = c("duet_scaled"
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, "deepddg_scaled"
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, "ddg_dynamut2_scaled"
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, "foldx_scaled_signC" # needed to get avg stability
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)
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#-----------------------------------------------
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# merged_df2: ADD col: average across predictors: stability
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#-----------------------------------------------
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if (all((scaled_cols_stab%in%colnames(merged_df2)))){
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cat("\nPASS: finding stability cols to average")
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cols2avg_stab = scaled_cols_stab
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cat("\nAveraging", length(cols2avg_stab), "stability columns:"
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, "\nThese are:", cols2avg_stab)
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merged_df2['avg_stability'] = rowMeans(merged_df2[, cols2avg_stab])
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}else{
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stop("\nAbort: Foldx column has opposing sign. Can't proceed to avergae.")
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}
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head(merged_df2[, c("mutationinformation"
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, "position"
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, "foldx_scaled"
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, scaled_cols_stab
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, "avg_stability")])
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#--------------------------------------
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# merged_df2: ADD col: average stability outcome
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#--------------------------------------
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merged_df2["avg_stability_outcome"] = ifelse(merged_df2["avg_stability"] < 0, "Destabilising", "Stabilising")
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head(merged_df2[, c("mutationinformation"
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, "position"
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, "avg_stability"
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, "avg_stability_outcome")])
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table(merged_df2["avg_stability_outcome"] )
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#--------------------------------------
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# merged_df2: ADD col: average stability scaled
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#--------------------------------------
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merged_df2["avg_stability_scaled"] = lapply(merged_df2["avg_stability"]
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, function(x) {
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scales::rescale_mid(x
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, to = c(-1,1)
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, from = c( min(merged_df2["avg_stability"])
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, max(merged_df2["avg_stability"]))
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, mid = 0)
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})
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if ( all(table(merged_df2["avg_stability"]<0) == table(merged_df2["avg_stability_scaled"]<0)) ){
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cat("\nPASS: Avergae stability column successfully averaged, scaled and categorised")
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}else{
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cat("\nAbort:Avergae stability column could not be processed")
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}
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head(merged_df2["avg_stability_scaled"])
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##########################################################################################
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#=====================================
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# merged_df2: Affinity values: average
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#======================================
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common_scaled_cols_affinity = c("affinity_scaled"
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, "mmcsm_lig_scaled")
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#------------------------------------------------------
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# merged_df2: ADD col: ensemble average across predictors: affinity
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#------------------------------------------------------
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if (all((common_scaled_cols_affinity%in%colnames(merged_df2)))){
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cat("\nPASS: finding affinity cols to average")
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cols2avg_aff = common_scaled_cols_affinity
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merged_df2['avg_lig_affinity'] = rowMeans(merged_df2[, cols2avg_aff])
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}else{
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stop("\nAbort: cols to average not found.")
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}
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head(merged_df2[, c("mutationinformation"
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, "position"
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, cols2avg_aff
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, "avg_lig_affinity")])
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table(merged_df2$affinity_scaled<0 )
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table(merged_df2$mmcsm_lig_scaled<0 )
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#--------------------------------------
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# merged_df2: ADD col: average affinity outcome
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#--------------------------------------
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merged_df2["avg_lig_affinity_outcome"] = ifelse(merged_df2["avg_lig_affinity"] < 0, "Destabilising", "Stabilising")
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head(merged_df2[, c("mutationinformation"
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, "position"
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, "avg_lig_affinity"
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, "avg_lig_affinity_outcome")])
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table(merged_df2["avg_lig_affinity_outcome"] )
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min( merged_df2['avg_lig_affinity']); max( merged_df2['avg_lig_affinity'])
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#--------------------------------------
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# merged_df2: ADD col: average affinity scaled
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#--------------------------------------
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merged_df2["avg_lig_affinity_scaled"] = lapply(merged_df2["avg_lig_affinity"]
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, function(x) {
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scales::rescale_mid(x
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, to = c(-1,1)
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, from = c( min(merged_df2["avg_lig_affinity"])
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, max(merged_df2["avg_lig_affinity"]))
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, mid = 0)
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})
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if ( all(table(merged_df2["avg_lig_affinity"]<0) == table(merged_df2["avg_lig_affinity_scaled"]<0)) ){
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cat("\nPASS: Avergae affinity column successfully averaged, scaled and categorised")
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}else{
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cat("\nAbort:Avergae affinity column could not be processed")
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}
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min( merged_df2['avg_lig_affinity_scaled']); max( merged_df2['avg_lig_affinity_scaled'])
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######################################################################################
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##########################################################################
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# MERGED_d3: average cols #
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# Average stability + lig-affinity columns #
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##########################################################################
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#==========================================
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# merged_df3: Stability values: average
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#==========================================
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#-------------------
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# foldx sign reverse
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# for consistency with other tools
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#-------------------
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head(merged_df3$ddg_foldx)
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# foldx values: reverse signs
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#merged_df3['ddg_foldxC'] = abs(merged_df3$ddg_foldx)
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#head(merged_df3[, c("ddg_foldx", "ddg_foldxC")])
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# foldx scaled: reverse signs fs
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merged_df3['foldx_scaled_signC'] = abs(merged_df3$foldx_scaled)
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head(merged_df3[, c("foldx_scaled", "foldx_scaled_signC")])
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# find which stability cols to average: should contain revised foldx
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scaled_cols_stab = c("duet_scaled"
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, "deepddg_scaled"
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, "ddg_dynamut2_scaled"
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#, "foldx_scaled"
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, "foldx_scaled_signC" # needed to get avg stability
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)
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#--------------------------------------------------------
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# merged_df3: ADD col: ensemble average across predictors: stability
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#---------------------------------------------------------
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if (all((scaled_cols_stab%in%colnames(merged_df3)))){
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cat("\nPASS: finding stability cols to average")
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cols2avg_stab = scaled_cols_stab
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cat("\nAveraging", length(cols2avg_stab), "stability columns:"
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, "\nThese are:", cols2avg_stab)
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merged_df3['avg_stability'] = rowMeans(merged_df3[, cols2avg_stab])
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}else{
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stop("\nAbort: Foldx column has opposing sign. Can't proceed to avergae.")
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}
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head(merged_df3[, c("mutationinformation"
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, "position"
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, "foldx_scaled"
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, scaled_cols_stab
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, "avg_stability")])
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#--------------------------------------
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# merged_df3: ADD col: average stability outcome
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#--------------------------------------
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merged_df3["avg_stability_outcome"] = ifelse(merged_df3["avg_stability"] < 0, "Destabilising", "Stabilising")
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head(merged_df3[, c("mutationinformation"
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, "position"
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, "avg_stability"
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, "avg_stability_outcome")])
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table(merged_df3["avg_stability_outcome"] )
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#--------------------------------------
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# merged_df3: ADD col: average stability scaled
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#--------------------------------------
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merged_df3["avg_stability_scaled"] = lapply(merged_df3["avg_stability"]
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, function(x) {
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scales::rescale_mid(x
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, to = c(-1,1)
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, from = c( min(merged_df3["avg_stability"])
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, max(merged_df3["avg_stability"]))
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, mid = 0)
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})
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if ( all(table(merged_df3["avg_stability"]<0) == table(merged_df3["avg_stability_scaled"]<0)) ){
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cat("\nPASS: Avergae stability column successfully averaged, scaled and categorised")
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}else{
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cat("\nAbort:Avergae stability column could not be processed")
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}
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head(merged_df3["avg_stability_scaled"])
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##########################################################################################
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#=====================================
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# merged_df3: Affinity values: average
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#======================================
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common_scaled_cols_affinity = c("affinity_scaled"
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, "mmcsm_lig_scaled")
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#------------------------------------------------------
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# merged_df3: ADD col: ensemble average across predictors: affinity
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#------------------------------------------------------
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if (all((common_scaled_cols_affinity%in%colnames(merged_df3)))){
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cat("\nPASS: finding affinity cols to average")
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cols2avg_aff = common_scaled_cols_affinity
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merged_df3['avg_lig_affinity'] = rowMeans(merged_df3[, cols2avg_aff])
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}else{
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stop("\nAbort: cols to average not found.")
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}
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head(merged_df3[, c("mutationinformation"
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, "position"
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, cols2avg_aff
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, "avg_lig_affinity")])
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table(merged_df3$affinity_scaled<0 )
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table(merged_df3$mmcsm_lig_scaled<0 )
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#--------------------------------------
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# merged_df3: ADD col: average affinity outcome
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#--------------------------------------
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merged_df3["avg_lig_affinity_outcome"] = ifelse(merged_df3["avg_lig_affinity"] < 0, "Destabilising", "Stabilising")
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head(merged_df3[, c("mutationinformation"
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, "position"
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, "avg_lig_affinity"
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, "avg_lig_affinity_outcome")])
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table(merged_df3["avg_lig_affinity_outcome"] )
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min( merged_df3['avg_lig_affinity']); max( merged_df3['avg_lig_affinity'])
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#--------------------------------------
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# merged_df3: ADD col: average affinity scaled
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#--------------------------------------
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merged_df3["avg_lig_affinity_scaled"] = lapply(merged_df3["avg_lig_affinity"]
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, function(x) {
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scales::rescale_mid(x
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, to = c(-1,1)
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, from = c( min(merged_df3["avg_lig_affinity"])
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, max(merged_df3["avg_lig_affinity"]))
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, mid = 0)
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})
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if ( all(table(merged_df3["avg_lig_affinity"]<0) == table(merged_df3["avg_lig_affinity_scaled"]<0)) ){
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cat("\nPASS: Avergae affinity column successfully averaged, scaled and categorised")
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}else{
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cat("\nAbort:Avergae affinity column could not be processed")
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}
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min( merged_df3['avg_lig_affinity_scaled']); max( merged_df3['avg_lig_affinity_scaled'])
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####################################################################
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#TODO
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# Choose few columns to return as plot_df
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####################################################################
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return(list( merged_df2
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return(list( merged_df2
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, merged_df3
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, merged_df3
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))
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))
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@ -150,11 +150,11 @@ linP_dm_om
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# , "mmcsm_lig_scaled"
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# , "mmcsm_lig_scaled"
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# , "mcsm_ppi2_scaled"
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# , "mcsm_ppi2_scaled"
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# , "mcsm_na_scaled"
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# , "mcsm_na_scaled"
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# , "avg_affinity_scaled")
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# , "avg_lig_affinity_scaled")
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# lineage_distP(merged_df2
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# lineage_distP(merged_df2
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# , with_facet = F
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# , with_facet = F
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# , x_axis = "avg_affinity_scaled"
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# , x_axis = "avg_lig_affinity_scaled"
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# , y_axis = "lineage_labels"
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# , y_axis = "lineage_labels"
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# , x_lab = my_xlabel
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# , x_lab = my_xlabel
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# , use_lineages = c("L1", "L2", "L3", "L4")
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# , use_lineages = c("L1", "L2", "L3", "L4")
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@ -33,17 +33,21 @@ common_cols = c("mutationinformation"
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raw_cols_stability = c("duet_stability_change"
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raw_cols_stability = c("duet_stability_change"
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, "deepddg"
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, "deepddg"
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, "ddg_dynamut2"
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, "ddg_dynamut2"
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, "ddg_foldx")
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, "ddg_foldx"
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, "avg_stability")
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scaled_cols_stability = c("duet_scaled"
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scaled_cols_stability = c("duet_scaled"
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, "deepddg_scaled"
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, "deepddg_scaled"
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, "ddg_dynamut2_scaled"
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, "ddg_dynamut2_scaled"
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, "foldx_scaled")
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, "foldx_scaled"
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, "foldx_scaled_signC" # needed to get avg stability
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, "avg_stability_scaled")
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outcome_cols_stability = c("duet_outcome"
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outcome_cols_stability = c("duet_outcome"
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, "deepddg_outcome"
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, "deepddg_outcome"
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, "ddg_dynamut2_outcome"
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, "ddg_dynamut2_outcome"
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, "foldx_outcome")
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, "foldx_outcome"
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, "avg_stability_outcome")
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all_stability_cols = c(raw_cols_stability
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all_stability_cols = c(raw_cols_stability
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, scaled_cols_stability
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, scaled_cols_stability
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@ -54,17 +58,20 @@ all_stability_cols = c(raw_cols_stability
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raw_cols_affinity = c("ligand_affinity_change"
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raw_cols_affinity = c("ligand_affinity_change"
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, "mmcsm_lig"
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, "mmcsm_lig"
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, "mcsm_ppi2_affinity"
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, "mcsm_ppi2_affinity"
|
||||||
, "mcsm_na_affinity")
|
, "mcsm_na_affinity"
|
||||||
|
, "avg_lig_affinity")
|
||||||
|
|
||||||
scaled_cols_affinity = c("affinity_scaled"
|
scaled_cols_affinity = c("affinity_scaled"
|
||||||
, "mmcsm_lig_scaled"
|
, "mmcsm_lig_scaled"
|
||||||
, "mcsm_ppi2_scaled"
|
, "mcsm_ppi2_scaled"
|
||||||
, "mcsm_na_scaled" )
|
, "mcsm_na_scaled"
|
||||||
|
, "avg_lig_affinity_scaled")
|
||||||
|
|
||||||
outcome_cols_affinity = c( "ligand_outcome"
|
outcome_cols_affinity = c( "ligand_outcome"
|
||||||
, "mmcsm_lig_outcome"
|
, "mmcsm_lig_outcome"
|
||||||
, "mcsm_ppi2_outcome"
|
, "mcsm_ppi2_outcome"
|
||||||
, "mcsm_na_outcome")
|
, "mcsm_na_outcome"
|
||||||
|
, "avg_lig_affinity_outcome")
|
||||||
|
|
||||||
all_affinity_cols = c(raw_cols_affinity
|
all_affinity_cols = c(raw_cols_affinity
|
||||||
, scaled_cols_affinity
|
, scaled_cols_affinity
|
||||||
|
|
@ -90,10 +97,6 @@ all_conserv_cols = c(raw_cols_conservation
|
||||||
, outcome_cols_conservation)
|
, outcome_cols_conservation)
|
||||||
|
|
||||||
|
|
||||||
all_cols = c(common_cols
|
|
||||||
, all_stability_cols
|
|
||||||
, all_affinity_cols
|
|
||||||
, all_conserv_cols)
|
|
||||||
|
|
||||||
########################################
|
########################################
|
||||||
categ_cols_to_factor = grep( "_outcome|_info", colnames(merged_df3) )
|
categ_cols_to_factor = grep( "_outcome|_info", colnames(merged_df3) )
|
||||||
|
|
@ -118,161 +121,10 @@ cat("\ncols changed to factor are:\n", colnames(merged_df3)[categ_cols_to_factor
|
||||||
plot_cols = c("mutationinformation", "mutation_info_labels", "position", "dst_mode"
|
plot_cols = c("mutationinformation", "mutation_info_labels", "position", "dst_mode"
|
||||||
, all_cols)
|
, all_cols)
|
||||||
|
|
||||||
df3 = merged_df3[, colnames(merged_df3)%in%plot_cols]
|
|
||||||
|
|
||||||
#=================
|
|
||||||
# PREFORMATTING: for consistency
|
|
||||||
#=================
|
|
||||||
# DONE: combining_dfs.R
|
|
||||||
# df3$sensitivity = ifelse(df3$dst_mode == 1, "R", "S")
|
|
||||||
# table(df3$sensitivity)
|
|
||||||
|
|
||||||
# ConSurf labels
|
|
||||||
#consurf_colOld = "consurf_colour_rev"
|
|
||||||
#consurf_colNew = "consurf_outcome"
|
|
||||||
#df3[[consurf_colNew]] = df3[[consurf_colOld]]
|
|
||||||
#df3[[consurf_colNew]] = as.factor(df3[[consurf_colNew]])
|
|
||||||
#df3[[consurf_colNew]]
|
|
||||||
# not this bit
|
|
||||||
#!!!!!!!!!!!!!1
|
|
||||||
#levels(df3$consurf_outcome) = c( "nsd", 1, 2, 3, 4, 5, 6, 7, 8, 9)
|
|
||||||
|
|
||||||
#levels(df3$consurf_outcome)
|
|
||||||
|
|
||||||
# SNAP2 labels
|
|
||||||
#snap2_colname = "snap2_outcome"
|
|
||||||
#df3[[snap2_colname]] <- str_replace(df3[[snap2_colname]], "effect", "Effect")
|
|
||||||
#df3[[snap2_colname]] <- str_replace(df3[[snap2_colname]], "neutral", "Neutral")
|
|
||||||
|
|
||||||
# for ref: not needed perse as function already does this and assigns labels for barplots
|
|
||||||
# labels_duet = levels(as.factor(df3$duet_outcome))
|
|
||||||
# labels_foldx = levels(as.factor(df3$foldx_outcome))
|
|
||||||
# labels_deepddg = levels(as.factor(df3$deepddg_outcome))
|
|
||||||
# labels_ddg_dynamut2_outcome = levels(as.factor(df3$ddg_dynamut2_outcome))
|
|
||||||
#
|
|
||||||
# labels_lig = levels(as.factor(df3_lig$ligand_outcome))
|
|
||||||
# labels_mmlig = levels(as.factor(df3_lig$mmcsm_lig_outcome))
|
|
||||||
# labels_ppi2 = levels(as.factor(df3_ppi2$mcsm_ppi2_outcome))
|
|
||||||
#
|
|
||||||
# labels_provean = levels(as.factor(df3$provean_outcome))
|
|
||||||
# labels_snap2 = levels(as.factor(df3$snap2_outcome))
|
|
||||||
# labels_consurf = levels(as.factor(df3$consurf_colour_rev))
|
|
||||||
# df3$consurf_colour_rev = as.factor(df3$consurf_colour_rev )
|
|
||||||
##############################################################################
|
|
||||||
#######################################
|
|
||||||
# merged_df2: NECESSARY pre-processing
|
|
||||||
######################################
|
|
||||||
df2 = merged_df2
|
|
||||||
|
|
||||||
#=================
|
|
||||||
# PREFORMATTING: for consistency
|
|
||||||
#=================
|
|
||||||
# DONE: combining_dfs.R
|
|
||||||
# df2$sensitivity = ifelse(df2$dst_mode == 1, "R", "S")
|
|
||||||
# table(df2$sensitivity)
|
|
||||||
|
|
||||||
#----------------------------------------------------
|
|
||||||
# Create dst2: fill na in dst with value of dst_mode
|
|
||||||
# for epistasis
|
|
||||||
#----------------------------------------------------
|
|
||||||
# DONE: combining_dfs.R
|
|
||||||
# df2$dst2 = ifelse(is.na(df2$dst), df2$dst_mode, df2f$dst)
|
|
||||||
|
|
||||||
#----------------------------------------------------
|
|
||||||
# reverse signs for foldx scaled values for
|
|
||||||
# to allow average with other tools
|
|
||||||
#----------------------------------------------------
|
|
||||||
head(df2['ddg_foldx'])
|
|
||||||
df2['ddg_foldxC'] = abs(df2$ddg_foldx)
|
|
||||||
head(df2['ddg_foldxC'])
|
|
||||||
|
|
||||||
head(df2['foldx_scaled'])
|
|
||||||
df2['foldx_scaled_signC'] = abs(df2$foldx_scaled)
|
|
||||||
head(df2['foldx_scaled_signC'])
|
|
||||||
|
|
||||||
rm_foldx_cols = c("ddg_foldx","foldx_scaled")
|
|
||||||
raw_cols_stab_revised = raw_cols_stability[!raw_cols_stability%in%rm_foldx_cols]
|
|
||||||
raw_cols_stab_revised = c(raw_cols_stab_revised,"ddg_foldxC")
|
|
||||||
|
|
||||||
scaled_cols_stab_revised = scaled_cols_stability[!scaled_cols_stability%in%rm_foldx_cols]
|
|
||||||
scaled_cols_stab_revised = c(scaled_cols_stab_revised, "foldx_scaled_signC")
|
|
||||||
|
|
||||||
######################################################
|
|
||||||
# Affinity related variables
|
|
||||||
# DONE:in plotting_globals.R
|
|
||||||
# DistCutOff = 10
|
|
||||||
# LigDist_colname # = "ligand_distance" # from globals
|
|
||||||
# ppi2Dist_colname = "interface_dist"
|
|
||||||
# naDist_colname = "TBC"
|
|
||||||
|
|
||||||
######################################################
|
|
||||||
# corr colnames
|
|
||||||
# drug
|
|
||||||
# "dst_mode"
|
|
||||||
# "ligand_distance"
|
|
||||||
# "DUET"
|
|
||||||
# "mCSM-lig"
|
|
||||||
# "FoldX"
|
|
||||||
# "DeepDDG"
|
|
||||||
# "ASA"
|
|
||||||
# "RSA"
|
|
||||||
# "KD"
|
|
||||||
# "RD"
|
|
||||||
# "Consurf"
|
|
||||||
# "SNAP2"
|
|
||||||
# "MAF"
|
|
||||||
# "Log (OR)"
|
|
||||||
# "-Log (P)"
|
|
||||||
# "Dynamut2"
|
|
||||||
# "mCSM-PPI2"
|
|
||||||
# "interface_dist"
|
|
||||||
|
|
||||||
corr_ps_colnames = c("DUET"
|
|
||||||
, "FoldX"
|
|
||||||
, "DeepDDG"
|
|
||||||
, "Dynamut2"
|
|
||||||
|
|
||||||
, "MAF"
|
|
||||||
, "Log (OR)"
|
|
||||||
, "-Log (P)"
|
|
||||||
|
|
||||||
# , "ASA"
|
|
||||||
# , "RSA"
|
|
||||||
# , "KD"
|
|
||||||
# , "RD"
|
|
||||||
# , "Consurf"
|
|
||||||
# , "SNAP2"
|
|
||||||
|
|
||||||
#, "mCSM-lig"
|
|
||||||
#, "ligand_distance"
|
|
||||||
#, "mCSM-PPI2"
|
|
||||||
#, "interface_dist"
|
|
||||||
, "dst_mode"
|
|
||||||
, drug
|
|
||||||
)
|
|
||||||
|
|
||||||
corr_lig_colnames = c("mCSM-lig"
|
|
||||||
, "MAF"
|
|
||||||
, "Log (OR)"
|
|
||||||
, "-Log (P)"
|
|
||||||
, "ligand_distance"
|
|
||||||
, "dst_mode"
|
|
||||||
, drug)
|
|
||||||
|
|
||||||
corr_ppi2_colnames = c("mCSM-PPI2"
|
|
||||||
, "SNAP2"
|
|
||||||
, "Log (OR)"
|
|
||||||
, "-Log (P)"
|
|
||||||
, "interface_dist"
|
|
||||||
, "dst_mode"
|
|
||||||
, drug)
|
|
||||||
|
|
||||||
corr_conservation_cols = c("ConSurf"
|
|
||||||
, "SNAP2"
|
|
||||||
, "PROVEAN"
|
|
||||||
, "MAF"
|
|
||||||
, "Log (OR)"
|
|
||||||
, "-Log (P)"
|
|
||||||
, "dst_mode"
|
|
||||||
, drug)
|
|
||||||
|
|
||||||
|
all_cols = c(common_cols
|
||||||
|
, all_stability_cols
|
||||||
|
, all_affinity_cols
|
||||||
|
, all_conserv_cols)
|
||||||
|
|
|
||||||
Loading…
Add table
Add a link
Reference in a new issue