more plot files
This commit is contained in:
parent
04253b961f
commit
3d817fde0c
29 changed files with 3252 additions and 760 deletions
223
scripts/plotting/mcsm_affinity_data_only.R
Normal file
223
scripts/plotting/mcsm_affinity_data_only.R
Normal file
|
|
@ -0,0 +1,223 @@
|
|||
#source("~/git/LSHTM_analysis/config/pnca.R")
|
||||
#source("~/git/LSHTM_analysis/config/alr.R")
|
||||
#source("~/git/LSHTM_analysis/config/gid.R")
|
||||
#source("~/git/LSHTM_analysis/config/embb.R")
|
||||
#source("~/git/LSHTM_analysis/config/katg.R")
|
||||
source("~/git/LSHTM_analysis/config/rpob.R")
|
||||
|
||||
source("/home/tanu/git/LSHTM_analysis/my_header.R")
|
||||
#########################################################
|
||||
# TASK: Generate averaged affinity values
|
||||
# across all affinity tools for a given structure
|
||||
# as applicable...
|
||||
#########################################################
|
||||
|
||||
#=======
|
||||
# output
|
||||
#=======
|
||||
outdir_images = paste0("~/git/Writing/thesis/images/results/", tolower(gene))
|
||||
|
||||
#OutFile1
|
||||
outfile_mean_aff = paste0(outdir_images, "/", tolower(gene)
|
||||
, "_mean_affinity_all.csv")
|
||||
print(paste0("Output file:", outfile_mean_aff))
|
||||
|
||||
#OutFile2
|
||||
outfile_mean_aff_priorty = paste0(outdir_images, "/", tolower(gene)
|
||||
, "_mean_affinity_priority.csv")
|
||||
print(paste0("Output file:", outfile_mean_aff_priorty))
|
||||
|
||||
#%%===============================================================
|
||||
|
||||
#=============
|
||||
# Input
|
||||
#=============
|
||||
df3_filename = paste0("/home/tanu/git/Data/", drug, "/output/", tolower(gene), "_merged_df3.csv")
|
||||
df3 = read.csv(df3_filename)
|
||||
length(df3$mutationinformation)
|
||||
|
||||
# mut_info checks
|
||||
table(df3$mutation_info)
|
||||
table(df3$mutation_info_orig)
|
||||
table(df3$mutation_info_labels_orig)
|
||||
|
||||
# used in plots and analyses
|
||||
table(df3$mutation_info_labels) # different, and matches dst_mode
|
||||
table(df3$dst_mode)
|
||||
|
||||
# create column based on dst mode with different colname
|
||||
table(is.na(df3$dst))
|
||||
table(is.na(df3$dst_mode))
|
||||
|
||||
#===============
|
||||
# Create column: sensitivity mapped to dst_mode
|
||||
#===============
|
||||
df3$sensitivity = ifelse(df3$dst_mode == 1, "R", "S")
|
||||
table(df3$sensitivity)
|
||||
|
||||
length(unique((df3$mutationinformation)))
|
||||
all_colnames = as.data.frame(colnames(df3))
|
||||
common_cols = c("mutationinformation"
|
||||
, "X5uhc_position"
|
||||
, "X5uhc_offset"
|
||||
, "position"
|
||||
, "dst_mode"
|
||||
, "mutation_info_labels"
|
||||
, "sensitivity"
|
||||
, "ligand_distance"
|
||||
, "interface_dist")
|
||||
|
||||
all_colnames$`colnames(df3)`[grep("scaled", all_colnames$`colnames(df3)`)]
|
||||
all_colnames$`colnames(df3)`[grep("outcome", all_colnames$`colnames(df3)`)]
|
||||
|
||||
#===================
|
||||
# stability cols
|
||||
#===================
|
||||
raw_cols_stability = c("duet_stability_change"
|
||||
, "deepddg"
|
||||
, "ddg_dynamut2"
|
||||
, "ddg_foldx")
|
||||
|
||||
scaled_cols_stability = c("duet_scaled"
|
||||
, "deepddg_scaled"
|
||||
, "ddg_dynamut2_scaled"
|
||||
, "foldx_scaled")
|
||||
|
||||
outcome_cols_stability = c("duet_outcome"
|
||||
, "deepddg_outcome"
|
||||
, "ddg_dynamut2_outcome"
|
||||
, "foldx_outcome")
|
||||
|
||||
#===================
|
||||
# affinity cols
|
||||
#===================
|
||||
raw_cols_affinity = c("ligand_affinity_change"
|
||||
, "mmcsm_lig"
|
||||
, "mcsm_ppi2_affinity"
|
||||
, "mcsm_na_affinity")
|
||||
|
||||
scaled_cols_affinity = c("affinity_scaled"
|
||||
, "mmcsm_lig_scaled"
|
||||
, "mcsm_ppi2_scaled"
|
||||
, "mcsm_na_scaled" )
|
||||
|
||||
outcome_cols_affinity = c( "ligand_outcome"
|
||||
, "mmcsm_lig_outcome"
|
||||
, "mcsm_ppi2_outcome"
|
||||
, "mcsm_na_outcome")
|
||||
|
||||
#===================
|
||||
# conservation cols
|
||||
#===================
|
||||
# raw_cols_conservation = c("consurf_score"
|
||||
# , "snap2_score"
|
||||
# , "provean_score")
|
||||
#
|
||||
# scaled_cols_conservation = c("consurf_scaled"
|
||||
# , "snap2_scaled"
|
||||
# , "provean_scaled")
|
||||
#
|
||||
# # CANNOT strictly be used, as categories are not identical with conssurf missing altogether
|
||||
# outcome_cols_conservation = c("provean_outcome"
|
||||
# , "snap2_outcome"
|
||||
# #consurf outcome doesn't exist
|
||||
# )
|
||||
|
||||
######################################################################
|
||||
cols_to_consider = colnames(df3)[colnames(df3)%in%c(common_cols
|
||||
, raw_cols_affinity
|
||||
, scaled_cols_affinity
|
||||
, outcome_cols_affinity
|
||||
, raw_cols_stability
|
||||
, scaled_cols_stability
|
||||
, outcome_cols_stability
|
||||
)]
|
||||
|
||||
cols_to_extract = cols_to_consider[cols_to_consider%in%c(common_cols
|
||||
#, raw_cols_affinity
|
||||
, scaled_cols_stability
|
||||
, scaled_cols_affinity)]
|
||||
|
||||
df3_plot = df3[, cols_to_extract]
|
||||
|
||||
# FIXME: ADD distance to NA when SP replies
|
||||
DistCutOff_colnames = c("ligand_distance", "interface_dist")
|
||||
DistCutOff = 10
|
||||
|
||||
#df3_affinity_filtered = df3_plot[ (df3_plot$ligand_distance<10 | df3_plot$interface_dist <10),]
|
||||
df3_affinity_filtered = df3_plot[ (df3_plot$ligand_distance<10 & df3_plot$interface_dist <10),]
|
||||
|
||||
c0u = unique(df3_affinity_filtered$position)
|
||||
length(c0u)
|
||||
|
||||
foo = df3_affinity_filtered[df3_affinity_filtered$ligand_distance<10,]
|
||||
bar = df3_affinity_filtered[df3_affinity_filtered$interface_dist<10,]
|
||||
|
||||
wilcox.test(foo$mmcsm_lig_scaled~foo$sensitivity)
|
||||
wilcox.test(foo$mmcsm_lig~foo$sensitivity)
|
||||
|
||||
wilcox.test(foo$affinity_scaled~foo$sensitivity)
|
||||
wilcox.test(foo$ligand_affinity_change~foo$sensitivity)
|
||||
|
||||
wilcox.test(bar$mcsm_na_scaled~bar$sensitivity)
|
||||
wilcox.test(bar$mcsm_na_affinity~bar$sensitivity)
|
||||
|
||||
wilcox.test(bar$mcsm_ppi2_scaled~bar$sensitivity)
|
||||
wilcox.test(bar$mcsm_ppi2_affinity~bar$sensitivity)
|
||||
|
||||
##############################################################
|
||||
df = df3_affinity_filtered
|
||||
sum(is.na(df))
|
||||
df2 = na.omit(df) # Apply na.omit function
|
||||
|
||||
gene_dist_cols = colnames(df2)[colnames(df2)%in%DistCutOff_colnames]
|
||||
gene_aff_cols = colnames(df2)[colnames(df2)%in%scaled_cols_affinity]
|
||||
gene_stab_cols = colnames(df2)[colnames(df2)%in%scaled_cols_stability]
|
||||
|
||||
sel_cols = c("mutationinformation", "position", gene_stab_cols, gene_dist_cols, gene_aff_cols)
|
||||
df3 = df2[, sel_cols]
|
||||
|
||||
give_col=function(x,y,df=df3){
|
||||
df[df$position==x,y]
|
||||
}
|
||||
|
||||
for (i in unique(df3$position) ){
|
||||
#print(i)
|
||||
biggest = max(abs(give_col(i,gene_aff_cols)))
|
||||
|
||||
df3[df3$position==i,'abs_max_effect'] = biggest
|
||||
df3[df3$position==i,'effect_type']= names(
|
||||
give_col(i,gene_aff_cols)[which(
|
||||
abs(
|
||||
give_col(i,gene_aff_cols)
|
||||
) == biggest, arr.ind=T
|
||||
)[, "col"]])
|
||||
|
||||
effect_name = unique(df3[df3$position==i,'effect_type'])
|
||||
|
||||
# get index/rowname for value of max effect, and then use it to get the original sign
|
||||
# here
|
||||
#df3[df3$position==i,c(effect_name)]
|
||||
#which(abs(df3[df3$position==i,c('position',effect_name)][effect_name])==biggest, arr.ind=T)
|
||||
ind = rownames(which(abs(df3[df3$position==i
|
||||
,c('position', effect_name)][effect_name])== biggest
|
||||
, arr.ind=T))
|
||||
df3[df3$position==i,'effect_sign'] = sign(df3[effect_name][ind,])
|
||||
}
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
#%%============================================================
|
||||
# output
|
||||
write.csv(combined_df, outfile_mean_ens_st_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))
|
||||
|
||||
# end of script
|
||||
#===============================================================
|
||||
Loading…
Add table
Add a link
Reference in a new issue