git trimmed downthe dm_om_data.R
This commit is contained in:
parent
fae846395d
commit
05ab89ec09
5 changed files with 168 additions and 351 deletions
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@ -343,20 +343,45 @@ combining_dfs_plotting <- function( my_df_u
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, "\nNo. of rows merged_df3: ", nrow(merged_df3))
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quit()
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}
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#---------------------------------------------
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# add columns that are needed to generate plots with revised colnames and strings
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#----------------------------------------------
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merged_df3['sensitivity'] = ifelse(merged_df3['dst_mode'] == 1, "R", "S")
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merged_df3['mutation_info_labels'] = ifelse(merged_df3['mutation_info_labels'] == "DM", "R", "S")
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#=========================================
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# NEW: add consurf outcome
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#=========================================
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consurf_colOld = "consurf_colour_rev"
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consurf_colNew = "consurf_outcome"
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merged_df3[[consurf_colNew]] = merged_df3[[consurf_colOld]]
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merged_df3[[consurf_colNew]] = as.factor(merged_df3[[consurf_colNew]])
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merged_df3[[consurf_colNew]]
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#levels(merged_df3$consurf_outcome) = c("nsd", 1, 2, 3, 4, 5, 6, 7, 8, 9)
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merged_df2['sensitivity'] = ifelse(merged_df2['dst_mode'] == 1, "R", "S")
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merged_df2['mutation_info_labels'] = ifelse(merged_df2['mutation_info_labels'] == "DM", "R", "S")
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merged_df2[[consurf_colNew]] = merged_df2[[consurf_colOld]]
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merged_df2[[consurf_colNew]] = as.factor(merged_df2[[consurf_colNew]])
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merged_df2[[consurf_colNew]]
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#check1 = all(table(merged_df3["mutation_info_labels"]) == table(merged_df3['sensitivity']))
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#check2 = all(table(merged_df2["mutation_info_labels"]) == table(merged_df2['sensitivity']))
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#=========================================
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# NEW: fixed case for SNAP2 labels
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#=========================================
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snap2_colname = "snap2_outcome"
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merged_df3[[snap2_colname]] <- str_replace(merged_df3[[snap2_colname]], "effect", "Effect")
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merged_df3[[snap2_colname]] <- str_replace(merged_df3[[snap2_colname]], "neutral", "Neutral")
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merged_df2[[snap2_colname]] <- str_replace(merged_df2[[snap2_colname]], "effect", "Effect")
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merged_df2[[snap2_colname]] <- str_replace(merged_df2[[snap2_colname]], "neutral", "Neutral")
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#---------------------------------------------
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# NEW: add columns that are needed to generate
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# plots with revised colnames and strings
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#----------------------------------------------
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merged_df3$sensitivity = ifelse(merged_df3$dst_mode == 1, "R", "S")
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merged_df3$mutation_info_labels = ifelse(merged_df3$mutation_info_labels == "DM", "R", "S")
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check1 = all(merged_df3["mutation_info_labels"] == merged_df3['sensitivity'])
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check2 = all(merged_df2["mutation_info_labels"] == merged_df2['sensitivity'])
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merged_df2$sensitivity = ifelse(merged_df2$dst_mode == 1, "R", "S")
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merged_df2$mutation_info_labels = ifelse(merged_df2$mutation_info_labels == "DM", "R", "S")
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# for epistasis: fill na where dst: No equivalent in merged_df3
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merged_df2$dst2 = ifelse(is.na(merged_df2$dst), merged_df2$dst_mode, merged_df2$dst)
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check1 = all(merged_df3$mutation_info_labels == merged_df3$sensitivity)
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check2 = all(merged_df2$mutation_info_labels == merged_df2$sensitivity)
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if(check1 && check2){
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cat("PASS: merged_df3 and merged_df2 have mutation info labels as R and S"
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@ -5,47 +5,16 @@
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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:
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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) df to choose (merged_df3 or merged_df2)
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#2)
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##################################################################
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DistCutOff = 10
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#LigDist_colname # = "ligand_distance" # from globals
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ppi2Dist_colname = "interface_dist"
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naDist_colname = "TBC"
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# from plotting_globals.R
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# DistCutOff, LigDist_colname, ppi2Dist_colname, naDist_colname
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dm_om_wf_lf_data <- function(df
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, gene_name = gene # from globals
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, colnames_to_extract
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, ligand_dist_colname = LigDist_colname # from globals
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#, ppi2Dist_colname #from globals used
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#, naDist_colname #from globals used
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, dr_muts = dr_muts_col # from globals
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, other_muts = other_muts_col # from globals
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, snp_colname = "mutationinformation"
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@ -53,28 +22,19 @@ dm_om_wf_lf_data <- function(df
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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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#, dr_other_muts_labels = c("DM", "OM") # only used if ^^ = ""
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, categ_cols_to_factor){
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df = as.data.frame(df)
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df['sensitivity'] = ifelse(df['dst_mode'] == 1, "R", "S")
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table(df['sensitivity'])
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df[[mut_info_label_colname]] = ifelse(df[[mut_info_label_colname]] == "DM", "R", "S")
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table(df[[mut_info_label_colname]])
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# Initialise the required dfs based on gene name
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geneL_normal = c("pnca")
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#geneL_na_dy = c("gid")
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geneL_na = c("gid", "rpob")
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geneL_dy = c("gid")
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geneL_ppi2 = c("alr", "embb", "katg", "rpob")
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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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@ -110,24 +70,6 @@ dm_om_wf_lf_data <- function(df
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)
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wf_lf_dataL = c(common_dfsL, additional_dfL)
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}
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if (tolower(gene_name)%in%geneL_dy){
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additional_dfL = list(
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wf_mcsm_na = data.frame()
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, lf_mcsm_na = data.frame()
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, wf_dynamut = data.frame()
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, lf_dynamut = data.frame()
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, wf_encomddg = data.frame()
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, lf_encomddg = data.frame()
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, wf_encomdds = data.frame()
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, lf_encomdds = data.frame()
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, wf_sdm = data.frame()
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, lf_sdm = data.frame()
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, wf_mcsm = data.frame()
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, lf_mcsm = data.frame()
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)
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wf_lf_dataL = c(common_dfsL, additional_dfL)
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}
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cat("\nInitializing an empty list of length:"
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, length(wf_lf_dataL))
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@ -137,26 +79,21 @@ dm_om_wf_lf_data <- function(df
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colnames_to_extract = c(snp_colname
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, mut_colname, mut_info_colname, mut_info_label_colname
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, aa_pos_colname
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, LigDist_colname
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, ppi2Dist_colname, naDist_colname
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, LigDist_colname # from globals
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, ppi2Dist_colname # from globals
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, naDist_colname # from globals
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, "duet_stability_change" , "duet_scaled" , "duet_outcome"
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, "ligand_affinity_change", "affinity_scaled" , "ligand_outcome"
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, "ddg_foldx" , "foldx_scaled" , "foldx_outcome"
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, "deepddg" , "deepddg_scaled" , "deepddg_outcome"
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, "asa" , "rsa"
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, "rd_values" , "kd_values"
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, "log10_or_mychisq" , "neglog_pval_fisher" , "af"
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, "ddg_dynamut2" , "ddg_dynamut2_scaled", "ddg_dynamut2_outcome"
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, "log10_or_mychisq" , "neglog_pval_fisher" , "maf" #"af"
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, "ddg_dynamut2" , "ddg_dynamut2_scaled", "ddg_dynamut2_outcome"
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, "mcsm_ppi2_affinity" , "mcsm_ppi2_scaled" , "mcsm_ppi2_outcome"
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, "consurf_score" , "consurf_scaled" #, "consurf_outcome"
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, "consurf_score" , "consurf_scaled" , "consurf_outcome" # exists now
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, "snap2_score" , "snap2_scaled" , "snap2_outcome"
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, "mcsm_na_affinity" , "mcsm_na_scaled" , "mcsm_na_outcome"
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, "ddg_dynamut" , "ddg_dynamut_scaled" , "ddg_dynamut_outcome"
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, "ddg_encom" , "ddg_encom_scaled" , "ddg_encom_outcome"
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, "dds_encom" , "dds_encom_scaled" , "dds_encom_outcome"
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, "ddg_mcsm" , "ddg_mcsm_scaled" , "ddg_mcsm_outcome"
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, "ddg_sdm" , "ddg_sdm_scaled" , "ddg_sdm_outcome"
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, "ddg_duet" , "ddg_duet_scaled" , "ddg_duet_outcome")
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, "mcsm_na_affinity" , "mcsm_na_scaled" , "mcsm_na_outcome")
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}else{
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colnames_to_extract = c(mut_colname, mut_info_colname, mut_info_label_colname
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, aa_pos_colname, LigDist_colname
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@ -186,47 +123,29 @@ dm_om_wf_lf_data <- function(df
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#=======================================================================
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table(comb_df_s[[mut_info_colname]])
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# further checks to make sure dr and other muts are indeed unique
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dr_muts = comb_df_s[comb_df_s[[mut_info_colname]] == dr_muts,]
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dr_muts_names = unique(dr_muts$mutation)
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other_muts = comb_df_s[comb_df_s[[mut_info_colname]] == other_muts,]
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other_muts_names = unique(other_muts$mutation)
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if ( table(dr_muts_names%in%other_muts_names)[[1]] == length(dr_muts_names) &&
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table(other_muts_names%in%dr_muts_names)[[1]] == length(other_muts_names) ){
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cat("PASS: dr and other muts are indeed unique")
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}else{
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cat("FAIL: dr and others muts are NOT unique!")
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quit()
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}
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# pretty display names i.e. labels to reduce major code duplication later
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foo_cnames = data.frame(colnames(comb_df_s))
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names(foo_cnames) <- "old_name"
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stability_suffix <- paste0(delta_symbol, delta_symbol, "G")
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flexibility_suffix <- paste0(delta_symbol, delta_symbol, "S")
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#flexibility_suffix <- paste0(delta_symbol, delta_symbol, "S")
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lig_dn = paste0("Ligand distance (", angstroms_symbol, ")"); lig_dn
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mcsm_lig_dn = paste0("Ligand affinity (log fold change)"); mcsm_lig_dn
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#lig_dn = paste0("Ligand distance (", angstroms_symbol, ")"); lig_dn
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#mcsm_lig_dn = paste0("Ligand affinity (log fold change)"); mcsm_lig_dn
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lig_dn = paste0("Lig Dist(", angstroms_symbol, ")"); lig_dn
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mcsm_lig_dn = paste0("mCSM-lig"); mcsm_lig_dn
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duet_dn = paste0("DUET ", stability_suffix); duet_dn
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foldx_dn = paste0("FoldX ", stability_suffix); foldx_dn
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deepddg_dn = paste0("Deepddg " , stability_suffix); deepddg_dn
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dynamut2_dn = paste0("Dynamut2 " , stability_suffix); dynamut2_dn
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mcsm_na_dn = paste0("mCSM-NA affinity ", stability_suffix); mcsm_na_dn
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mcsm_ppi2_dn = paste0("mCSM-PPI2 affinity ", stability_suffix); mcsm_ppi2_dn
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mcsm_na_dn = paste0("mCSM-NA ", stability_suffix); mcsm_na_dn
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mcsm_ppi2_dn = paste0("mCSM-PPI2 ", stability_suffix); mcsm_ppi2_dn
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consurf_dn = paste0("Consurf"); consurf_dn
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snap2_dn = paste0("SNAP2"); snap2_dn
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dynamut_dn = paste0("Dynamut ", stability_suffix); dynamut_dn
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encom_ddg_dn = paste0("EnCOM " , stability_suffix); encom_ddg_dn
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encom_dds_dn = paste0("EnCOM " , flexibility_suffix ); encom_dds_dn
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sdm_dn = paste0("SDM " , stability_suffix); sdm_dn
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mcsm_dn = paste0("mCSM " , stability_suffix ); mcsm_dn
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# change column names: plyr
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new_colnames = c(asa = "ASA"
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@ -235,7 +154,8 @@ new_colnames = c(asa = "ASA"
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, kd_values = "KD"
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, log10_or_mychisq = "Log10 (OR)"
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, neglog_pval_fisher = "-Log (P)"
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, af = "MAF"
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#, af = "MAF"
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, maf = "MAF"
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#, ligand_dist_colname = lig_dn # cannot handle variable name 'ligand_dist_colname'
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, affinity_scaled = mcsm_lig_dn
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, duet_scaled = duet_dn
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@ -245,12 +165,7 @@ new_colnames = c(asa = "ASA"
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, mcsm_na_scaled = mcsm_na_dn
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, mcsm_ppi2_affinity = mcsm_ppi2_dn
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, consurf_score = consurf_dn
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, snap2_score = snap2_dn
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, ddg_dynamut_scaled = dynamut_dn
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, ddg_encom_scaled = encom_ddg_dn
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, dds_encom_scaled = encom_dds_dn
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, ddg_sdm = sdm_dn
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, ddg_mcsm = mcsm_dn)
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, snap2_score = snap2_dn)
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comb_df_sl1 = plyr::rename(comb_df_s
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, replace = new_colnames
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@ -260,29 +175,26 @@ comb_df_sl1 = plyr::rename(comb_df_s
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# renaming colname using variable i.e ligand_dist_colname: dplyr
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comb_df_sl = comb_df_sl1 %>% dplyr::rename(!!lig_dn := all_of(ligand_dist_colname))
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names(comb_df_sl)
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#####################################################################
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if (mut_info_label_colname == "") {
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cat("\nAssigning labels:", dr_other_muts_labels, "--> to column:", mut_info_colname)
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table(comb_df_sl[[mut_info_colname]])
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# dr_muts
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levels(comb_df_sl[[mut_info_colname]])[levels(comb_df_sl[[mut_info_colname]])==dr_muts] <- dr_other_muts_labels[[1]]
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# other_muts
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levels(comb_df_sl[[mut_info_colname]])[levels(comb_df_sl[[mut_info_colname]])==other_muts] <- dr_other_muts_labels[[2]]
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table(comb_df_sl[[mut_info_colname]])
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static_cols1 = mut_info_colname
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}else{
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table(comb_df_sl[[mut_info_label_colname]])
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static_cols1 = mut_info_label_colname
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}
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#=======================
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# NEW: Affinity filtered data
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#========================
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# mcsm-lig --> LigDist_colname
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comb_df_sl_lig = comb_df_sl[comb_df_sl[[lig_dn]]<DistCutOff,]
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# mcsm-ppi2 --> ppi2Dist_colname
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comb_df_sl_ppi2 = comb_df_sl[comb_df_sl[[ppi2Dist_colname]]<DistCutOff,]
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# mcsm-na --> naDist_colname
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comb_df_sl_na = comb_df_sl[comb_df_sl[[naDist_colname]]<DistCutOff,]
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#####################################################################
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static_cols1 = mut_info_label_colname
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#######################################################################
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#======================
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# Selecting dfs
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# with appropriate cols
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#=======================
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static_cols_start = c(snp_colname
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, aa_pos_colname
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, mut_colname
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@ -296,7 +208,8 @@ static_cols_end = c(lig_dn
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, "KD"
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, "MAF"
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, "Log10 (OR)"
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, "-Log (P)")
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#, "-Log (P)"
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)
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#########################################################################
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#==============
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@ -312,7 +225,7 @@ expected_rows_lf = nrow(wf_duet) * (length(wf_duet) - length(pivot_cols_duet))
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expected_rows_lf
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# LF data: duet
|
||||
lf_duet = gather(wf_duet
|
||||
lf_duet = tidyr::gather(wf_duet
|
||||
, key = param_type
|
||||
, value = param_value
|
||||
, all_of(duet_dn):tail(static_cols_end,1)
|
||||
|
|
@ -329,35 +242,6 @@ if (nrow(lf_duet) == expected_rows_lf){
|
|||
wf_lf_dataL[['wf_duet']] = wf_duet
|
||||
wf_lf_dataL[['lf_duet']] = lf_duet
|
||||
|
||||
############################################################################
|
||||
#==============
|
||||
# mCSM-lig
|
||||
#==============
|
||||
# WF data: mcsm_lig
|
||||
cols_to_select_mcsm_lig = c(static_cols_start, c("ligand_outcome", mcsm_lig_dn), static_cols_end)
|
||||
wf_mcsm_lig = comb_df_sl[, cols_to_select_mcsm_lig]
|
||||
|
||||
pivot_cols_mcsm_lig = cols_to_select_mcsm_lig[1: (length(static_cols_start) + 1)]; pivot_cols_mcsm_lig
|
||||
expected_rows_lf = nrow(wf_mcsm_lig) * (length(wf_mcsm_lig) - length(pivot_cols_mcsm_lig))
|
||||
expected_rows_lf
|
||||
|
||||
# LF data: mcsm_lig
|
||||
lf_mcsm_lig = gather(wf_mcsm_lig
|
||||
, key = param_type
|
||||
, value = param_value
|
||||
, all_of(mcsm_lig_dn):tail(static_cols_end,1)
|
||||
, factor_key = TRUE)
|
||||
|
||||
if (nrow(lf_mcsm_lig) == expected_rows_lf){
|
||||
cat("\nPASS: long format data created for ", mcsm_lig_dn)
|
||||
}else{
|
||||
cat("\nFAIL: long format data could not be created for mcsm_lig")
|
||||
quit()
|
||||
}
|
||||
|
||||
# Assign them to the output list
|
||||
wf_lf_dataL[['wf_mcsm_lig']] = wf_mcsm_lig
|
||||
wf_lf_dataL[['lf_mcsm_lig']] = lf_mcsm_lig
|
||||
############################################################################
|
||||
#==============
|
||||
# FoldX
|
||||
|
|
@ -446,7 +330,9 @@ if (nrow(lf_dynamut2) == expected_rows_lf){
|
|||
# Assign them to the output list
|
||||
wf_lf_dataL[['wf_dynamut2']] = wf_dynamut2
|
||||
wf_lf_dataL[['lf_dynamut2']] = lf_dynamut2
|
||||
############################################################################
|
||||
|
||||
|
||||
######################################################################################
|
||||
#==================
|
||||
# Consurf: LF
|
||||
#https://consurf.tau.ac.il/overview.php
|
||||
|
|
@ -459,9 +345,9 @@ wf_lf_dataL[['lf_dynamut2']] = lf_dynamut2
|
|||
#5-->"", 6-->"", 7-->"", 8-->"", 9-->"most_conserved"
|
||||
#====================
|
||||
# FIXME: if you add category column to consurf
|
||||
#cols_to_select_consurf = c(static_cols_start, c("consurf_outcome", consurf_dn), static_cols_end)
|
||||
#wf_consurf = comb_df_sl[, cols_to_select_consurf]
|
||||
#pivot_cols_consurf = cols_to_select_consurf[1: (length(static_cols_start) + 1)]; pivot_cols_consurf
|
||||
cols_to_select_consurf = c(static_cols_start, c("consurf_outcome", consurf_dn), static_cols_end)
|
||||
wf_consurf = comb_df_sl[, cols_to_select_consurf]
|
||||
pivot_cols_consurf = cols_to_select_consurf[1: (length(static_cols_start) + 1)]; pivot_cols_consurf
|
||||
|
||||
# WF data: consurf
|
||||
cols_to_select_consurf = c(static_cols_start, c(consurf_dn), static_cols_end)
|
||||
|
|
@ -517,15 +403,54 @@ if (nrow(lf_snap2) == expected_rows_lf){
|
|||
# Assign them to the output list
|
||||
wf_lf_dataL[['wf_snap2']] = wf_snap2
|
||||
wf_lf_dataL[['lf_snap2']] = lf_snap2
|
||||
###########################################################################
|
||||
# AFFINITY cols
|
||||
###########################################################################
|
||||
#=========================
|
||||
# mCSM-lig:
|
||||
# data filtered by cut off
|
||||
#=========================
|
||||
#---------------------
|
||||
# mCSM-lig: WF and lF
|
||||
#----------------------
|
||||
# WF data: mcsm_lig
|
||||
cols_to_select_mcsm_lig = c(static_cols_start, c("ligand_outcome", mcsm_lig_dn), static_cols_end)
|
||||
wf_mcsm_lig = comb_df_sl_lig[, cols_to_select_mcsm_lig] # filtered df
|
||||
|
||||
############################################################################
|
||||
pivot_cols_mcsm_lig = cols_to_select_mcsm_lig[1: (length(static_cols_start) + 1)]; pivot_cols_mcsm_lig
|
||||
expected_rows_lf = nrow(wf_mcsm_lig) * (length(wf_mcsm_lig) - length(pivot_cols_mcsm_lig))
|
||||
expected_rows_lf
|
||||
|
||||
# LF data: mcsm_lig
|
||||
lf_mcsm_lig = gather(wf_mcsm_lig
|
||||
, key = param_type
|
||||
, value = param_value
|
||||
, all_of(mcsm_lig_dn):tail(static_cols_end,1)
|
||||
, factor_key = TRUE)
|
||||
|
||||
if (nrow(lf_mcsm_lig) == expected_rows_lf){
|
||||
cat("\nPASS: long format data created for ", mcsm_lig_dn)
|
||||
}else{
|
||||
cat("\nFAIL: long format data could not be created for mcsm_lig")
|
||||
quit()
|
||||
}
|
||||
|
||||
# Assign them to the output list
|
||||
wf_lf_dataL[['wf_mcsm_lig']] = wf_mcsm_lig
|
||||
wf_lf_dataL[['lf_mcsm_lig']] = lf_mcsm_lig
|
||||
|
||||
#====================
|
||||
# mcsm-NA affinity
|
||||
# data filtered by cut off
|
||||
#====================
|
||||
if (tolower(gene_name)%in%geneL_na){
|
||||
#==============
|
||||
# mCSM-NA: LF
|
||||
#==============
|
||||
#---------------
|
||||
# mCSM-NA: WF and lF
|
||||
#-----------------
|
||||
# WF data: mcsm-na
|
||||
cols_to_select_mcsm_na = c(static_cols_start, c("mcsm_na_outcome", mcsm_na_dn), static_cols_end)
|
||||
wf_mcsm_na = comb_df_sl[, cols_to_select_mcsm_na]
|
||||
#wf_mcsm_na = comb_df_sl[, cols_to_select_mcsm_na]
|
||||
wf_mcsm_na = comb_df_sl_na[, cols_to_select_mcsm_na]
|
||||
|
||||
pivot_cols_mcsm_na = cols_to_select_mcsm_na[1: (length(static_cols_start) + 1)]; pivot_cols_mcsm_na
|
||||
expected_rows_lf = nrow(wf_mcsm_na) * (length(wf_mcsm_na) - length(pivot_cols_mcsm_na))
|
||||
|
|
@ -550,14 +475,19 @@ if (tolower(gene_name)%in%geneL_na){
|
|||
wf_lf_dataL[['lf_mcsm_na']] = lf_mcsm_na
|
||||
|
||||
}
|
||||
#-------------------------------------------------------------------
|
||||
|
||||
#=========================
|
||||
# mcsm-ppi2 affinity
|
||||
# data filtered by cut off
|
||||
#========================
|
||||
if (tolower(gene_name)%in%geneL_ppi2){
|
||||
#==============
|
||||
# mCSM-PPI2: LF
|
||||
#==============
|
||||
#-----------------
|
||||
# mCSM-PPI2: WF and lF
|
||||
#-----------------
|
||||
# WF data: mcsm-ppi2
|
||||
cols_to_select_mcsm_ppi2 = c(static_cols_start, c("mcsm_ppi2_outcome", mcsm_ppi2_dn), static_cols_end)
|
||||
wf_mcsm_ppi2 = comb_df_sl[, cols_to_select_mcsm_ppi2]
|
||||
#wf_mcsm_ppi2 = comb_df_sl[, cols_to_select_mcsm_ppi2]
|
||||
wf_mcsm_ppi2 = comb_df_sl_ppi2[, cols_to_select_mcsm_ppi2]
|
||||
|
||||
pivot_cols_mcsm_ppi2 = cols_to_select_mcsm_ppi2[1: (length(static_cols_start) + 1)]; pivot_cols_mcsm_ppi2
|
||||
expected_rows_lf = nrow(wf_mcsm_ppi2) * (length(wf_mcsm_ppi2) - length(pivot_cols_mcsm_ppi2))
|
||||
|
|
@ -582,156 +512,7 @@ if (tolower(gene_name)%in%geneL_ppi2){
|
|||
wf_lf_dataL[['lf_mcsm_ppi2']] = lf_mcsm_ppi2
|
||||
|
||||
}
|
||||
#-------------------------------------------------------------------
|
||||
if (tolower(gene_name)%in%geneL_dy){
|
||||
#==============
|
||||
# Dynamut: LF
|
||||
#==============
|
||||
# WF data: dynamut
|
||||
cols_to_select_dynamut = c(static_cols_start, c("ddg_dynamut_outcome", dynamut_dn), static_cols_end)
|
||||
wf_dynamut = comb_df_sl[, cols_to_select_dynamut]
|
||||
|
||||
pivot_cols_dynamut = cols_to_select_dynamut[1: (length(static_cols_start) + 1)]; pivot_cols_dynamut
|
||||
expected_rows_lf = nrow(wf_dynamut) * (length(wf_dynamut) - length(pivot_cols_dynamut))
|
||||
expected_rows_lf
|
||||
|
||||
# LF data: dynamut
|
||||
lf_dynamut = gather(wf_dynamut
|
||||
, key = param_type
|
||||
, value = param_value
|
||||
, all_of(dynamut_dn):tail(static_cols_end,1)
|
||||
, factor_key = TRUE)
|
||||
|
||||
if (nrow(lf_dynamut) == expected_rows_lf){
|
||||
cat("\nPASS: long format data created for ", dynamut_dn)
|
||||
}else{
|
||||
cat("\nFAIL: long format data could not be created for duet")
|
||||
quit()
|
||||
}
|
||||
|
||||
# Assign them to the output list
|
||||
wf_lf_dataL[['wf_dynamut']] = wf_dynamut
|
||||
wf_lf_dataL[['lf_dynamut']] = lf_dynamut
|
||||
|
||||
#-------------------------------------------------------------------------
|
||||
#==============
|
||||
# EnCOM ddg: LF
|
||||
#==============
|
||||
# WF data: encomddg
|
||||
cols_to_select_encomddg = c(static_cols_start, c("ddg_encom_outcome", encom_ddg_dn), static_cols_end)
|
||||
wf_encomddg = comb_df_sl[, cols_to_select_encomddg]
|
||||
|
||||
pivot_cols_encomddg = cols_to_select_encomddg[1: (length(static_cols_start) + 1)]; pivot_cols_encomddg
|
||||
expected_rows_lf = nrow(wf_encomddg ) * (length(wf_encomddg ) - length(pivot_cols_encomddg))
|
||||
expected_rows_lf
|
||||
|
||||
# LF data: encomddg
|
||||
lf_encomddg = gather(wf_encomddg
|
||||
, key = param_type
|
||||
, value = param_value
|
||||
, all_of(encom_ddg_dn):tail(static_cols_end,1)
|
||||
, factor_key = TRUE)
|
||||
|
||||
if (nrow(lf_encomddg) == expected_rows_lf){
|
||||
cat("\nPASS: long format data created for ", encom_ddg_dn)
|
||||
}else{
|
||||
cat("\nFAIL: long format data could not be created for duet")
|
||||
quit()
|
||||
}
|
||||
|
||||
# Assign them to the output list
|
||||
wf_lf_dataL[['wf_encomddg']] = wf_encomddg
|
||||
wf_lf_dataL[['lf_encomddg']] = lf_encomddg
|
||||
#-------------------------------------------------------------------------
|
||||
#==============
|
||||
# EnCOM dds: LF
|
||||
#==============
|
||||
# WF data: encomdds
|
||||
cols_to_select_encomdds = c(static_cols_start, c("dds_encom_outcome", encom_dds_dn), static_cols_end)
|
||||
wf_encomdds = comb_df_sl[, cols_to_select_encomdds]
|
||||
|
||||
pivot_cols_encomdds = cols_to_select_encomdds[1: (length(static_cols_start) + 1)]; pivot_cols_encomdds
|
||||
expected_rows_lf = nrow(wf_encomdds) * (length(wf_encomdds) - length(pivot_cols_encomdds))
|
||||
expected_rows_lf
|
||||
|
||||
# LF data: encomdds
|
||||
lf_encomdds = gather(wf_encomdds
|
||||
, key = param_type
|
||||
, value = param_value
|
||||
, all_of(encom_dds_dn):tail(static_cols_end,1)
|
||||
, factor_key = TRUE)
|
||||
|
||||
if (nrow(lf_encomdds) == expected_rows_lf){
|
||||
cat("\nPASS: long format data created for", encom_dds_dn)
|
||||
}else{
|
||||
cat("\nFAIL: long format data could not be created for duet")
|
||||
quit()
|
||||
}
|
||||
|
||||
# Assign them to the output list
|
||||
wf_lf_dataL[['wf_encomdds']] = wf_encomdds
|
||||
wf_lf_dataL[['lf_encomdds']] = lf_encomdds
|
||||
#-------------------------------------------------------------------------
|
||||
#==============
|
||||
# SDM: LF
|
||||
#==============
|
||||
# WF data: sdm
|
||||
cols_to_select_sdm = c(static_cols_start, c("ddg_sdm_outcome", sdm_dn), static_cols_end)
|
||||
wf_sdm = comb_df_sl[, cols_to_select_sdm]
|
||||
|
||||
pivot_cols_sdm = cols_to_select_sdm[1: (length(static_cols_start) + 1)]; pivot_cols_sdm
|
||||
expected_rows_lf = nrow(wf_sdm) * (length(wf_sdm) - length(pivot_cols_sdm))
|
||||
expected_rows_lf
|
||||
|
||||
# LF data: sdm
|
||||
lf_sdm = gather(wf_sdm
|
||||
, key = param_type
|
||||
, value = param_value
|
||||
, all_of(sdm_dn):tail(static_cols_end,1)
|
||||
, factor_key = TRUE)
|
||||
|
||||
if (nrow(lf_sdm) == expected_rows_lf){
|
||||
cat("\nPASS: long format data created for", sdm_dn)
|
||||
}else{
|
||||
cat("\nFAIL: long format data could not be created for duet")
|
||||
quit()
|
||||
}
|
||||
|
||||
# Assign them to the output list
|
||||
wf_lf_dataL[['wf_sdm']] = wf_sdm
|
||||
wf_lf_dataL[['lf_sdm']] = lf_sdm
|
||||
#-------------------------------------------------------------------------
|
||||
#==============
|
||||
# mCSM: LF
|
||||
#==============
|
||||
# WF data: mcsm
|
||||
cols_to_select_mcsm = c(static_cols_start, c("ddg_mcsm_outcome", mcsm_dn), static_cols_end)
|
||||
wf_mcsm = comb_df_sl[, cols_to_select_mcsm]
|
||||
|
||||
pivot_cols_mcsm = cols_to_select_mcsm[1: (length(static_cols_start) + 1)]; pivot_cols_mcsm
|
||||
expected_rows_lf = nrow(wf_mcsm) * (length(wf_mcsm) - length(pivot_cols_mcsm))
|
||||
expected_rows_lf
|
||||
|
||||
# LF data: mcsm
|
||||
lf_mcsm = gather(wf_mcsm
|
||||
, key = param_type
|
||||
, value = param_value
|
||||
, all_of(mcsm_dn):tail(static_cols_end,1)
|
||||
, factor_key = TRUE)
|
||||
|
||||
if (nrow(lf_mcsm) == expected_rows_lf){
|
||||
cat("\nPASS: long format data created for", mcsm_dn)
|
||||
}else{
|
||||
cat("\nFAIL: long format data could not be created for duet")
|
||||
quit()
|
||||
}
|
||||
|
||||
# Assign them to the output list
|
||||
wf_lf_dataL[['wf_mcsm']] = wf_mcsm
|
||||
wf_lf_dataL[['lf_mcsm']] = lf_mcsm
|
||||
|
||||
}
|
||||
#-------------------------------------------------------------------------
|
||||
return(wf_lf_dataL)
|
||||
}
|
||||
############################################################################
|
||||
|
|
|
|||
|
|
@ -39,6 +39,10 @@ resistance_col <<- "drtype"
|
|||
LigDist_colname <<- "ligand_distance"
|
||||
LigDist_cutoff <<- 10
|
||||
|
||||
DistCutOff = 10
|
||||
ppi2Dist_colname = "interface_dist"
|
||||
naDist_colname = "TBC"
|
||||
|
||||
#==================
|
||||
# Angstroms symbol
|
||||
#==================
|
||||
|
|
|
|||
|
|
@ -112,7 +112,7 @@ cat(s1)
|
|||
#source("other_plots_data.R")
|
||||
####################################################################
|
||||
|
||||
source(paste0(plot_script_path, "dm_om_data.R"))
|
||||
#source(paste0(plot_script_path, "dm_om_data.R"))
|
||||
s2 = c("\nSuccessfully sourced other_plots_data.R")
|
||||
cat(s2)
|
||||
|
||||
|
|
|
|||
|
|
@ -10,8 +10,10 @@ source("~/git/LSHTM_analysis/config/embb.R")
|
|||
source("~/git/LSHTM_analysis/scripts/plotting/get_plotting_dfs.R")
|
||||
###################################################################
|
||||
# FIXME: ADD distance to NA when SP replies
|
||||
# DONE: plotting_globals.R
|
||||
dist_columns = c("ligand_distance", "interface_dist")
|
||||
DistCutOff = 10
|
||||
|
||||
common_cols = c("mutationinformation"
|
||||
, "X5uhc_position"
|
||||
, "X5uhc_offset"
|
||||
|
|
@ -98,22 +100,24 @@ df3 = merged_df3
|
|||
#=================
|
||||
# PREFORMATTING: for consistency
|
||||
#=================
|
||||
df3$sensitivity = ifelse(df3$dst_mode == 1, "R", "S")
|
||||
table(df3$sensitivity)
|
||||
# DONE: combining_dfs.R
|
||||
# df3$sensitivity = ifelse(df3$dst_mode == 1, "R", "S")
|
||||
# table(df3$sensitivity)
|
||||
|
||||
# ConSurf labels
|
||||
consurf_colOld = "consurf_colour_rev"
|
||||
consurf_colNew = "consurf_outcome"
|
||||
df3[[consurf_colNew]] = df3[[consurf_colOld]]
|
||||
df3[[consurf_colNew]] = as.factor(df3[[consurf_colNew]])
|
||||
df3[[consurf_colNew]]
|
||||
#consurf_colOld = "consurf_colour_rev"
|
||||
#consurf_colNew = "consurf_outcome"
|
||||
#df3[[consurf_colNew]] = df3[[consurf_colOld]]
|
||||
#df3[[consurf_colNew]] = as.factor(df3[[consurf_colNew]])
|
||||
#df3[[consurf_colNew]]
|
||||
# not this bit
|
||||
levels(df3$consurf_outcome) = c( "nsd", 1, 2, 3, 4, 5, 6, 7, 8, 9)
|
||||
levels(df3$consurf_outcome)
|
||||
#levels(df3$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")
|
||||
#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$duet_outcome))
|
||||
|
|
@ -138,14 +142,16 @@ df2 = merged_df2
|
|||
#=================
|
||||
# PREFORMATTING: for consistency
|
||||
#=================
|
||||
df2$sensitivity = ifelse(df2$dst_mode == 1, "R", "S")
|
||||
table(df2$sensitivity)
|
||||
# DONE: combining_dfs.R
|
||||
# df2$sensitivity = ifelse(df2$dst_mode == 1, "R", "S")
|
||||
# table(df2$sensitivity)
|
||||
|
||||
#----------------------------------------------------
|
||||
# Create dst2: fill na in dst with value of dst_mode
|
||||
# for epistasis
|
||||
#----------------------------------------------------
|
||||
df2$dst2 = ifelse(is.na(df2$dst), df2$dst_mode, df2f$dst)
|
||||
# DONE: combining_dfs.R
|
||||
# df2$dst2 = ifelse(is.na(df2$dst), df2$dst_mode, df2f$dst)
|
||||
|
||||
#----------------------------------------------------
|
||||
# reverse signs for foldx scaled values for
|
||||
|
|
@ -168,10 +174,11 @@ scaled_cols_stab_revised = c(scaled_cols_stab_revised, "foldx_scaled_signC")
|
|||
|
||||
######################################################
|
||||
# Affinity related variables
|
||||
DistCutOff = 10
|
||||
LigDist_colname # = "ligand_distance" # from globals
|
||||
ppi2Dist_colname = "interface_dist"
|
||||
naDist_colname = "TBC"
|
||||
# DONE:in plotting_globals.R
|
||||
# DistCutOff = 10
|
||||
# LigDist_colname # = "ligand_distance" # from globals
|
||||
# ppi2Dist_colname = "interface_dist"
|
||||
# naDist_colname = "TBC"
|
||||
|
||||
######################################################
|
||||
# corr colnames
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue