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added fd corrected p-values for ks stats

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This commit is contained in:
Tanushree Tunstall 2022-08-13 14:54:51 +01:00
parent f5f1e388c3
commit 365c322953
3 changed files with 333 additions and 176 deletions
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8
config/embb.R
View file

@ -109,6 +109,10 @@ cat("\n==================================================="
)
##############################################################
# var for position customisation for plots
aa_pos_lig1 = aa_pos_ca
# aa_pos_lig1 = aa_pos_ca
# aa_pos_lig2 = aa_pos_cdl
# aa_pos_lig3 = aa_pos_dsl
aa_pos_lig1 = aa_pos_dsl
aa_pos_lig2 = aa_pos_cdl
aa_pos_lig3 = aa_pos_dsl
aa_pos_lig3 = aa_pos_ca

245
scripts/ks_test_all_PS.R
View file

@ -18,38 +18,26 @@ source("~/git/LSHTM_analysis/scripts/plotting/plotting_colnames.R")
# Output
#=============
outdir_images = paste0("~/git/Writing/thesis/images/results/", tolower(gene), "/")
outdir_stats = "~/git/LSHTM_analysis/scripts/plotting/plotting_thesis/stats/"
outdir_stats = paste0(outdir_images,"stats/")
# ks test by lineage
#ks_lineage = paste0(outdir, "/KS_lineage_all_muts.csv")
###########################
# Data for stats
# you need merged_df2 or merged_df2_comp
# you need df2 or df2_comp
# since this is one-many relationship
# i.e the same SNP can belong to multiple lineages
# using the _comp dataset means
# we lose some muts and at this level, we should use
# as much info as available, hence use df with NA
###########################
# REASSIGNMENT
my_df = merged_df2
# delete variables not required
rm(merged_df2, merged_df2_comp, merged_df3, merged_df3_comp)
rm(merged_df2_lig, merged_df2_comp_lig, merged_df3_lig, merged_df3_comp_lig, my_df_u, my_df_u_lig)
df2 = merged_df2[, colnames(merged_df2)%in%plotting_cols]
# quick checks
colnames(my_df)
str(my_df)
colnames(df2)
str(df2)
# Ensure correct data type in columns to plot: need to be factor
#is.factor(my_df$lineage)
#my_df$lineage = as.factor(my_df$lineage)
#is.factor(my_df$lineage)
########################################################################
table(my_df$lineage); str(my_df$lineage)
table(df2$lineage); str(df2$lineage)
# subset only lineages1-4
sel_lineages = c("L1"
@ -58,29 +46,22 @@ sel_lineages = c("L1"
, "L4")
# subset selected lineages
df_lin = subset(my_df, subset = lineage %in% sel_lineages)
df_lin = subset(df2, subset = lineage %in% sel_lineages)
table(df_lin$lineage)
table(df_lin$sensitivity)
table(df_lin$lineage, df_lin$sensitivity)
#==============
# dr_muts_col
#==============
#table(df_lin$mutation_info); str(df_lin$mutation_info)
#ensure lineage is a factor
#str(df_lin$lineage); str(df_lin$lineage_labels)
#df_lin$lineage = as.factor(df_lin$lineage)
#df_lin$lineage_labels = as.factor(df_lin$lineage)
table(df_lin$lineage); table(df_lin$lineage_labels)]
# subset df with dr muts only
#df_lin_dr = subset(df_lin, mutation_info == dr_muts_col)
#table(df_lin_dr$mutation_info)
#==============
# other_muts_col
#==============
#df_lin_other = subset(df_lin, mutation_info == other_muts_col)
#table(df_lin_other$mutation_info)
#=======================================================================
#==============================
# Stats for average stability
#=============================
# individual: CHECKS
lin1 = df_lin[df_lin$lineage == "L1",]$avg_stability_scaled
lin2 = df_lin[df_lin$lineage == "L2",]$avg_stability_scaled
@ -89,14 +70,14 @@ lin4 = df_lin[df_lin$lineage == "L4",]$avg_stability_scaled
ks.test(lin1, lin4)
ks.test(df_lin$avg_stability_scaled[df_lin$lineage == "L2"]
, df_lin$avg_stability_scaled[df_lin$lineage == "L3"])
ks.test(df_lin$avg_stability_scaled[df_lin$lineage == "L1"]
, df_lin$avg_stability_scaled[df_lin$lineage == "L4"])
#=======================================================================
my_lineages = levels(factor(df_lin$lineage)); my_lineages
#=======================================================================
# Loop
#0 : < 2.2e-16
#=====================
# Lineage 1 comparisons
#=====================
@ -131,7 +112,7 @@ for (i in my_lineages_comp_l1){
, df_lin$avg_stability_scaled[df_lin$lineage == i])$p.value
# print(c(lineage_comp, ks_method, ks_statistic[[1]], ks_pval))
l1_df$comparison = lineage_comp
l1_df$comparison = lineage_comp
l1_df$method = ks_method
l1_df$ks_statistic = ks_statistic[[1]]
l1_df$ks_pvalue = ks_pvalue
@ -142,6 +123,33 @@ for (i in my_lineages_comp_l1){
ks_df_l1 = rbind(ks_df_l1,l1_df)
}
ks_df_l1
# adjusted p-value: bonferroni
ks_df_l1$p_adj_bonferroni = p.adjust(ks_df_l1$ks_pvalue, method = "bonferroni")
ks_df_l1$signif_bon = ks_df_l1$p_adj_bonferroni
ks_df_l1 = dplyr::mutate(ks_df_l1
, signif_bon = case_when(signif_bon == 0.05 ~ "."
, signif_bon <=0.0001 ~ '****'
, signif_bon <=0.001 ~ '***'
, signif_bon <=0.01 ~ '**'
, signif_bon <0.05 ~ '*'
, TRUE ~ 'ns'))
# adjusted p-value:fdr
ks_df_l1$p_adj_fdr = p.adjust(ks_df_l1$ks_pvalue, method = "fdr")
ks_df_l1$signif_fdr = ks_df_l1$p_adj_fdr
ks_df_l1 = dplyr::mutate(ks_df_l1
, signif_fdr = case_when(signif_fdr == 0.05 ~ "."
, signif_fdr <=0.0001 ~ '****'
, signif_fdr <=0.001 ~ '***'
, signif_fdr <=0.01 ~ '**'
, signif_fdr <0.05 ~ '*'
, TRUE ~ 'ns'))
ks_df_l1
#####################################################################
#=====================
@ -190,6 +198,31 @@ for (i in my_lineages_comp_l2){
}
# adjusted p-value: bonferroni
ks_df_l2$p_adj_bonferroni = p.adjust(ks_df_l2$ks_pvalue, method = "bonferroni")
ks_df_l2$signif_bon = ks_df_l2$p_adj_bonferroni
ks_df_l2 = dplyr::mutate(ks_df_l2
, signif_bon = case_when(signif_bon == 0.05 ~ "."
, signif_bon <=0.0001 ~ '****'
, signif_bon <=0.001 ~ '***'
, signif_bon <=0.01 ~ '**'
, signif_bon <0.05 ~ '*'
, TRUE ~ 'ns'))
# adjusted p-value:fdr
ks_df_l2$p_adj_fdr = p.adjust(ks_df_l2$ks_pvalue, method = "fdr")
ks_df_l2$signif_fdr = ks_df_l2$p_adj_fdr
ks_df_l2 = dplyr::mutate(ks_df_l2
, signif_fdr = case_when(signif_fdr == 0.05 ~ "."
, signif_fdr <=0.0001 ~ '****'
, signif_fdr <=0.001 ~ '***'
, signif_fdr <=0.01 ~ '**'
, signif_fdr <0.05 ~ '*'
, TRUE ~ 'ns'))
ks_df_l2
#=====================
# Lineage 3 comparisons
#=====================
@ -234,11 +267,33 @@ for (i in my_lineages_comp_l3){
ks_df_l3 = rbind(ks_df_l3, l3_df)
}
######################################################################################
# adjusted p-value: bonferroni
ks_df_l3$p_adj_bonferroni = p.adjust(ks_df_l3$ks_pvalue, method = "bonferroni")
ks_df_l3$signif_bon = ks_df_l3$p_adj_bonferroni
ks_df_l3 = dplyr::mutate(ks_df_l3
, signif_bon = case_when(signif_bon == 0.05 ~ "."
, signif_bon <=0.0001 ~ '****'
, signif_bon <=0.001 ~ '***'
, signif_bon <=0.01 ~ '**'
, signif_bon <0.05 ~ '*'
, TRUE ~ 'ns'))
# adjusted p-value:fdr
ks_df_l3$p_adj_fdr = p.adjust(ks_df_l3$ks_pvalue, method = "fdr")
ks_df_l3$signif_fdr = ks_df_l3$p_adj_fdr
ks_df_l3 = dplyr::mutate(ks_df_l3
, signif_fdr = case_when(signif_fdr == 0.05 ~ "."
, signif_fdr <=0.0001 ~ '****'
, signif_fdr <=0.001 ~ '***'
, signif_fdr <=0.01 ~ '**'
, signif_fdr <0.05 ~ '*'
, TRUE ~ 'ns'))
ks_df_l3
####################################################################
# combine all 4 ks_dfs
# combine all 3 ks_dfs
n_dfs = 3
if ( all.equal(nrow(ks_df_l1), nrow(ks_df_l2), nrow(ks_df_l3)) &&
all.equal(ncol(ks_df_l1), ncol(ks_df_l2), ncol(ks_df_l3)) ){
@ -263,12 +318,9 @@ if ( all.equal(nrow(ks_df_l1), nrow(ks_df_l2), nrow(ks_df_l3)) &&
, "\nCheck hardcoded value of n_dfs")
}
#--------------
# formatting
#--------------
# add total_n number
#ks_df_combined$total_samples_analysed = nrow(df_lin)
#----------------------------
# ADD extra cols: formatting
#----------------------------
# adding pvalue_signif
ks_df_combined$pvalue_signif = ks_df_combined$ks_pvalue
str(ks_df_combined$pvalue_signif)
@ -322,9 +374,31 @@ overall_RS_df$ks_pvalue = ks_pvalue
overall_RS_df$n_samples = n_samples_all
overall_RS_df$n_samples_total= n_samples_total
#--------------
# formatting
#--------------
#----------------------------
# ADD extra cols
#----------------------------
# adjusted p-value: bonferroni
overall_RS_df$p_adj_bonferroni = p.adjust(overall_RS_df$ks_pvalue, method = "bonferroni")
overall_RS_df$signif_bon = overall_RS_df$p_adj_bonferroni
overall_RS_df = dplyr::mutate(overall_RS_df
, signif_bon = case_when(signif_bon == 0.05 ~ "."
, signif_bon <=0.0001 ~ '****'
, signif_bon <=0.001 ~ '***'
, signif_bon <=0.01 ~ '**'
, signif_bon <0.05 ~ '*'
, TRUE ~ 'ns'))
# adjusted p-value:fdr
overall_RS_df$p_adj_fdr = p.adjust(overall_RS_df$ks_pvalue, method = "fdr")
overall_RS_df$signif_fdr = overall_RS_df$p_adj_fdr
overall_RS_df = dplyr::mutate(overall_RS_df
, signif_fdr = case_when(signif_fdr == 0.05 ~ "."
, signif_fdr <=0.0001 ~ '****'
, signif_fdr <=0.001 ~ '***'
, signif_fdr <=0.01 ~ '**'
, signif_fdr <0.05 ~ '*'
, TRUE ~ 'ns'))
# unadjusted p-values
overall_RS_df$pvalue_signif = overall_RS_df$ks_pvalue
overall_RS_df = dplyr::mutate(overall_RS_df
, pvalue_signif = case_when(pvalue_signif == 0.05 ~ "."
@ -333,8 +407,6 @@ overall_RS_df = dplyr::mutate(overall_RS_df
, pvalue_signif <=0.01 ~ '**'
, pvalue_signif <0.05 ~ '*'
, TRUE ~ 'ns'))
overall_RS_df
#####################################################################
if (all(colnames(ks_df_combined_f) == colnames(overall_RS_df))){
@ -349,20 +421,12 @@ ks_df_combined_f2 = rbind(ks_df_combined_f, overall_RS_df)
ks_df_combined_f2$ks_comp_type = "between_lineages"
ks_df_combined_f2$gene_name = tolower(gene)
# #==============================
# # write output file: KS test
# #===============================
# Out_lineage_bwL = paste0(outdir_stats
# , tolower(gene)
# , "_ks_lineage_bw.csv")
#
# cat("Output of KS test bt lineage:", Out_lineage_bwL)
# write.csv(ks_df_combined_f2, Out_lineage_bwL, row.names = FALSE)
ks_df_combined_f2
###########################################################################
#=======================
#=================================
# Within lineage R vs S: MANUAL
#=======================
#=================================
lin1 = df_lin[df_lin$lineage == my_lin1,]
ks.test(lin1$avg_stability_scaled[lin1$sensitivity == "R"]
, lin1$avg_stability_scaled[lin1$sensitivity == "S"])
@ -426,35 +490,49 @@ for (i in c(my_lin1
}
all_within_lin_df
all_within_lin_df$pvalue_signif = all_within_lin_df$ks_pvalue
str(all_within_lin_df$pvalue_signif)
#----------------------------
# ADD extra cols: formatting
#----------------------------
# adjusted p-value: bonferroni
all_within_lin_df$p_adj_bonferroni = p.adjust(all_within_lin_df$ks_pvalue, method = "bonferroni")
all_within_lin_df$signif_bon = all_within_lin_df$p_adj_bonferroni
all_within_lin_df = dplyr::mutate(all_within_lin_df
, pvalue_signif = case_when(pvalue_signif == 0.05 ~ "."
, pvalue_signif <=0.0001 ~ '****'
, pvalue_signif <=0.001 ~ '***'
, pvalue_signif <=0.01 ~ '**'
, pvalue_signif <0.05 ~ '*'
, signif_bon = case_when(signif_bon == 0.05 ~ "."
, signif_bon <=0.0001 ~ '****'
, signif_bon <=0.001 ~ '***'
, signif_bon <=0.01 ~ '**'
, signif_bon <0.05 ~ '*'
, TRUE ~ 'ns'))
all_within_lin_df
# adjusted p-value:fdr
all_within_lin_df$p_adj_fdr = p.adjust(all_within_lin_df$ks_pvalue, method = "fdr")
all_within_lin_df$signif_fdr = all_within_lin_df$p_adj_fdr
all_within_lin_df = dplyr::mutate(all_within_lin_df
, signif_fdr = case_when(signif_fdr == 0.05 ~ "."
, signif_fdr <=0.0001 ~ '****'
, signif_fdr <=0.001 ~ '***'
, signif_fdr <=0.01 ~ '**'
, signif_fdr <0.05 ~ '*'
, TRUE ~ 'ns'))
# ADD extra cols
# unadjusted p-value
all_within_lin_df$pvalue_signif = all_within_lin_df$ks_pvalue
str(all_within_lin_df$pvalue_signif)
all_within_lin_df = dplyr::mutate(all_within_lin_df
, pvalue_signif = case_when(pvalue_signif == 0.05 ~ "."
, pvalue_signif <=0.0001 ~ '****'
, pvalue_signif <=0.001 ~ '***'
, pvalue_signif <=0.01 ~ '**'
, pvalue_signif <0.05 ~ '*'
, TRUE ~ 'ns'))
# ADD info cols
all_within_lin_df$ks_comp_type = "within_lineages"
all_within_lin_df$gene_name = tolower(gene)
# #--------------------
# # write output file: KS test within grpup
# #----------------------
# Out_ks_withinL = paste0(outdir_stats
# , tolower(gene)
# , "_ks_lineage_within.csv")
# cat("Output of KS test within lineage:",Out_ks_withinL )
# write.csv(all_within_lin_df, Out_ks_withinL, row.names = FALSE)
##################################################################
if (all(colnames(ks_df_combined_f2) == colnames(Out_ks_withinL))){
if (all(colnames(ks_df_combined_f2) == colnames(all_within_lin_df))){
cat("\nPASS:combining KS test results")
}else{
@ -469,5 +547,6 @@ ks_df_combined_all = rbind(ks_df_combined_f2, all_within_lin_df)
Out_ks_all = paste0(outdir_stats
, tolower(gene)
, "_ks_lineage_all_comp.csv")
cat("Output of KS test all comparisons:", Out_ks_all )
write.csv(ks_df_combined_all, Out_ks_all, row.names = FALSE)
write.csv(ks_df_combined_all, Out_ks_all, row.names = FALSE)

256
scripts/plotting/plotting_thesis/preformatting.R
View file

@ -21,116 +21,190 @@ geneL_normal = c("pnca")
geneL_na = c("gid", "rpob")
geneL_ppi2 = c("alr", "embb", "katg", "rpob")
# LigDist_colname # from globals used
# ppi2Dist_colname #from globals used
# naDist_colname #from globals used
common_cols = c("mutationinformation"
, "X5uhc_position"
, "X5uhc_offset"
, "position"
, "dst_mode"
, "mutation_info_labels"
, "sensitivity", dist_columns )
# counting
foo = merged_df3[, c("mutationinformation"
, "wild_pos"
, "position"
, "sensitivity"
, "avg_lig_affinity"
, "avg_lig_affinity_scaled"
, "avg_lig_affinity_outcome"
, "ligand_distance"
, "ligand_affinity_change"
, "affinity_scaled"
, "ligand_outcome"
, "consurf_colour_rev"
, "consurf_outcome")]
#===================
# stability cols
#===================
raw_cols_stability = c("duet_stability_change"
, "deepddg"
, "ddg_dynamut2"
, "ddg_foldx"
, "avg_stability")
table(foo$consurf_outcome)
scaled_cols_stability = c("duet_scaled"
, "deepddg_scaled"
, "ddg_dynamut2_scaled"
, "foldx_scaled"
, "foldx_scaled_signC" # needed to get avg stability
, "avg_stability_scaled")
foo2 = foo[foo$ligand_distance<10,]
outcome_cols_stability = c("duet_outcome"
, "deepddg_outcome"
, "ddg_dynamut2_outcome"
, "foldx_outcome"
, "avg_stability_outcome")
table(foo2$ligand_outcome)
all_stability_cols = c(raw_cols_stability
, scaled_cols_stability
, outcome_cols_stability)
#===================
# affinity cols
#===================
raw_cols_affinity = c("ligand_affinity_change"
, "mmcsm_lig"
, "mcsm_ppi2_affinity"
, "mcsm_na_affinity"
, "avg_lig_affinity")
#############################
# wide plots SNP
# DRUG
length(aa_pos_drug); aa_pos_drug
drug = foo[foo$position%in%aa_pos_drug,]
drug$wild_pos
scaled_cols_affinity = c("affinity_scaled"
, "mmcsm_lig_scaled"
, "mcsm_ppi2_scaled"
, "mcsm_na_scaled"
, "avg_lig_affinity_scaled")
length(unique(drug$position)); unique(drug$position)
table(drug$position)
outcome_cols_affinity = c( "ligand_outcome"
, "mmcsm_lig_outcome"
, "mcsm_ppi2_outcome"
, "mcsm_na_outcome"
, "avg_lig_affinity_outcome")
drug$mutationinformation[drug$position==306]
drug$mutationinformation[drug$position==303]
all_affinity_cols = c(raw_cols_affinity
, scaled_cols_affinity
, outcome_cols_affinity)
#===================
# conservation cols
#===================
raw_cols_conservation = c("consurf_score"
, "snap2_score"
, "provean_score")
#CA
length(aa_pos_ca); aa_pos_ca
ca = foo[foo$position%in%aa_pos_ca,]
ca$position; length(unique(ca$position))
table(ca$position)
scaled_cols_conservation = c("consurf_scaled"
, "snap2_scaled"
, "provean_scaled")
# DSL
length(aa_pos_dsl); aa_pos_dsl
dsl= foo[foo$position%in%aa_pos_dsl,]
dsl$position; length(unique(dsl$position))
table(dsl$position)
outcome_cols_conservation = c("provean_outcome"
, "snap2_outcome"
, "consurf_colour_rev"
, "consurf_outcome")
all_conserv_cols = c(raw_cols_conservation
, scaled_cols_conservation
, outcome_cols_conservation)
dsl$mutationinformation[dsl$position==330]
dsl$mutationinformation[dsl$position==438]
dsl$mutationinformation[dsl$position==439]
dsl$mutationinformation[dsl$position==510]
########################################
categ_cols_to_factor = grep( "_outcome|_info", colnames(merged_df3) )
fact_cols = colnames(merged_df3)[categ_cols_to_factor]
# CDL
length(aa_pos_cdl); aa_pos_cdl
cdl= foo[foo$position%in%aa_pos_cdl,]
length(unique(cdl$position)); cdl$position;
table(cdl$position)
if (any(lapply(merged_df3[, fact_cols], class) == "character")){
cat("\nChanging", length(categ_cols_to_factor), "cols to factor")
merged_df3[, fact_cols] <- lapply(merged_df3[, fact_cols], as.factor)
if (all(lapply(merged_df3[, fact_cols], class) == "factor")){
cat("\nSuccessful: cols changed to factor")
}
cdl$mutationinformation[cdl$position==456]
cdl$mutationinformation[cdl$position==521]
cdl$mutationinformation[cdl$position==554]
cdl$mutationinformation[cdl$position==568]
cdl$mutationinformation[cdl$position==575]
cdl$mutationinformation[cdl$position==580]
cdl$mutationinformation[cdl$position==658]
cdl$mutationinformation[cdl$position==665]
###############################################
# OR plot
bar = merged_df3[, c("mutationinformation"
, "wild_pos"
, "position"
, "sensitivity"
, affinity_dist_colnames
, "or_mychisq"
, "pval_fisher"
#, "pval_chisq"
, "neglog_pval_fisher"
, "log10_or_mychisq")]
# bar$p_adj_bonferroni = p.adjust(bar$pval_fisher, method = "bonferroni")
# bar$signif_bon = bar$p_adj_bonferroni
# bar = dplyr::mutate(bar
# , signif_bon = case_when(signif_bon == 0.05 ~ "."
# , signif_bon <=0.0001 ~ '****'
# , signif_bon <=0.001 ~ '***'
# , signif_bon <=0.01 ~ '**'
# , signif_bon <0.05 ~ '*'
# , TRUE ~ 'ns'))
bar$p_adj_fdr = p.adjust(bar$pval_fisher, method = "BH")
bar$signif_fdr = bar$p_adj_fdr
bar = dplyr::mutate(bar
, signif_fdr = case_when(signif_fdr == 0.05 ~ "."
, signif_fdr <=0.0001 ~ '****'
, signif_fdr <=0.001 ~ '***'
, signif_fdr <=0.01 ~ '**'
, signif_bon <0.05 ~ '*'
, TRUE ~ 'ns'))
# sort df
bar = bar[order(bar$or_mychisq, decreasing = T), ]
bar = bar[, c("mutationinformation"
, "wild_pos"
, "position"
, "sensitivity"
, affinity_dist_colnames
, "or_mychisq"
#, "pval_fisher"
#, "pval_chisq"
#, "neglog_pval_fisher"
#, "log10_or_mychisq"
#, "signif_bon"
, "p_adj_fdr"
, "signif_fdr")]
table(bar$sensitivity)
table(bar$or_mychisq>1&bar$signif_fdr) # sen and res ~ OR
str(bar)
sen = bar[bar$or_mychisq<1,]
sen = na.omit(sen)
res = bar[bar$or_mychisq>1,]
res = na.omit(res)
# comp
bar_or = bar[!is.na(bar$or_mychisq),]
table(bar_or$sensitivity)
sen1 = bar_or[bar_or$or_mychisq<1,] # sen and res ~OR
res1 = bar_or[bar_or$or_mychisq>1,] # sen and res ~OR
# sanity check
if (nrow(bar_or) == nrow(sen1) + nrow(res1) ){
cat("\nPASS: df with or successfully sourced"
, "\nCalculating % of muts with OR>1")
}else{
cat("\nRequested cols aready factors")
stop("Abort: df with or numbers mimatch")
}
cat("\ncols changed to factor are:\n", colnames(merged_df3)[categ_cols_to_factor] )
# percent for OR muts
pc_orR = nrow(res1)/(nrow(sen1) + nrow(res1)); pc_orR
cat("\nPercentage of muts with OR>1 i.e resistant:"
, pc_orR *100 )
####################################
# merged_df3: NECESSARY pre-processing
###################################
#df3 = merged_df3
plot_cols = c("mutationinformation", "mutation_info_labels", "position", "dst_mode"
, all_cols)
# muts with highest OR
head(bar_or$mutationinformation, 10)
# sort df
bar_or = bar_or[order(bar_or$or_mychisq
, bar_or$ligand_distance
, bar_or$interface_dist
, decreasing = T), ]
bar_or$drug_site = ifelse(bar_or$position%in%aa_pos_drug, "drug", "no")
table(bar_or$drug_site)
bar_or$dsl_site = ifelse(bar_or$position%in%aa_pos_dsl, "dsl", "no")
table(bar_or$dsl_site)
bar_or$ca_site = ifelse(bar_or$position%in%aa_pos_ca, "ca", "no")
table(bar_or$ca_site)
bar_or$cdl_site = ifelse(bar_or$position%in%aa_pos_cdl, "cdl", "no")
table(bar_or$cdl_site)
top10_or = bar_or[1:10,]
# are these active sites
top10_or$position[top10_or$position%in%active_aa_pos]
all_cols = c(common_cols
, all_stability_cols
, all_affinity_cols
, all_conserv_cols)
# clostest most sig
bar_or_lig = bar_or[bar_or$ligand_distance<10,]
bar_or_lig = bar_or_lig[order(bar_or_lig$ligand_distance, -bar_or_lig$or_mychisq), ]
table(bar_or_lig$signif_fdr)
bar_or_ppi = bar_or[bar_or$interface_dist<10,]
bar_or_ppi = bar_or_ppi[order(bar_or_ppi$interface_dist, -bar_or_ppi$or_mychisq), ]
table(bar_or_ppi$signif_fdr)