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renamed 2 to _v2

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This commit is contained in:
Tanushree Tunstall 2022-08-22 11:41:42 +01:00
parent c9d7ea9fad
commit 802d6f8495
18 changed files with 761 additions and 976 deletions
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45
config/alr.R
View file

@ -27,6 +27,7 @@ aa_plip_dcs_other = aa_plip_dcs[!aa_plip_dcs%in%aa_plip_dcs_hbond]
c2 = length(aa_plip_dcs_other) == length(aa_plip_dcs) - length(aa_plip_dcs_hbond)
#==========
# Arpeggio
#===========
@ -40,6 +41,18 @@ aa_arpeg_dcs_other = aa_arpeg_dcs[!aa_arpeg_dcs%in%c(aa_ligplus_dcs_other
c3 = length(aa_arpeg_dcs_other) == length(aa_arpeg_dcs) - ( length(aa_ligplus_dcs_other) + length(aa_plip_dcs_other) )
#######################################################################
#NEW AFTER ADDING PLP to structure! huh
# ADDED: 18 Aug 2022
# PLIP server for co factor PLP (CONFUSING!)
#and 2019 lit:lys42, M319, and Y364 : OFFSET is 24
#K42: K66, Y271:Y295, M319:M343, W89: W113, W203: W227, H209:H233, Q321:Q345
aa_pos_paper = sort(unique(c(66,70,112,113,164,196,227,233,237,252,254,255,295,342,343,344,345,388)))
plp_pos_paper = sort(unique(c(66, 70, 112, 196, 227, 237, 252, 254, 255, 388)))
#active_aa_pos = sort(unique(c(aa_pos_paper, active_aa_pos)))
aa_pos_plp = sort(unique(c(plp_pos_paper, 66, 70, 112, 237, 252, 254, 255, 196)))
#######################################################################
#===============
@ -47,7 +60,9 @@ c3 = length(aa_arpeg_dcs_other) == length(aa_arpeg_dcs) - ( length(aa_ligplus_dc
#===============
active_aa_pos = sort(unique(c(aa_ligplus_dcs
, aa_plip_dcs
, aa_arpeg_dcs)))
, aa_arpeg_dcs
, aa_pos_paper
, aa_pos_plp)))
#=================
# Drug binding aa
#=================
@ -56,6 +71,12 @@ aa_pos_dcs = sort(unique(c(aa_ligplus_dcs
, aa_arpeg_dcs)))
aa_pos_drug = aa_pos_dcs
#===============
# Co-factor: PLP aa
#===============
aa_pos_plp = aa_pos_plp
#===============
# Hbond aa
#===============
@ -102,11 +123,25 @@ if ( all(c1, c2, c3) ) {
, "\n\nNO other co-factors or ligands present\n")
}
#######################################################################
######################################################################
#NEW
# PLIP server for co factor PLP (CONFUSING!)
#and 2019 lit:lys42, M319, and Y364 : OFFSET is 24
#K42: K66, Y271:Y295, M319:M343, W89: W113, W203: W227, H209:H233, Q321:Q345
aa_pos_paper = sort(unique(c(66,70,112,113,164,196,227,233,237,252,254,255,295,342,343,344,345,388)))
plp_pos_paper = sort(unique(c(66, 70, 112, 196, 227, 237, 252, 254, 255, 388)))
active_aa_pos = sort(unique(c(aa_pos_paper, active_aa_pos)))
aa_pos_plp = sort(unique(c(plp_pos_paper, 66, 70, 112, 237, 252, 254, 255, 196)))
# add two key residues
#aa_pos_drug = sort(unique(c(319, 364, aa_pos_drug)))
#active_aa_pos = sort(unique(c(319, 364, active_aa_pos, aa_pos_plp)))
# FIXME: these should be populated!
aa_pos_lig1 = NULL
aa_pos_lig1 = aa_pos_plp
aa_pos_lig2 = NULL
aa_pos_lig3 = NULL
tile_map=data.frame(tile=c("ALR","DPA","CDL","Ca"),
tile_colour=c("green","darkslategrey","navyblue","purple"))
tile_map=data.frame(tile=c("ALR","PLP"),
tile_colour=c("green","darkslategrey"))

21
config/embb.R
View file

@ -70,29 +70,29 @@ cat("\nNo. of active site residues for gene"
##############################################################
aa_pos_emb = sort(unique(c( aa_ligplus_emb
, aa_plip_emb
, aa_arpeg_emb)))
, aa_plip_emb
, aa_arpeg_emb)))
aa_pos_drug = aa_pos_emb
aa_pos_emb_hbond = sort(unique(c( aa_ligplus_emb_hbond
, aa_plip_emb_hbond)))
, aa_plip_emb_hbond)))
aa_pos_ca = sort(unique(c( aa_ligplus_ca
, aa_plip_ca
, aa_arpeg_ca)))
, aa_plip_ca
, aa_arpeg_ca)))
aa_pos_cdl = sort(unique(c( aa_ligplus_cdl
, aa_plip_cdl
, aa_arpeg_cdl )))
, aa_plip_cdl
, aa_arpeg_cdl )))
aa_pos_cdl_hbond = sort(unique(c( aa_ligplus_cdl_hbond )))
aa_pos_dsl = sort(unique(c( aa_ligplus_dsl
, aa_plip_dsl
, aa_arpeg_dsl)))
, aa_plip_dsl
, aa_arpeg_dsl)))
aa_pos_dsl_hbond = sort(unique(c( aa_ligplus_dsl_hbond
, aa_plip_dsl_hbond)))
, aa_plip_dsl_hbond)))
cat("\n==================================================="
@ -120,3 +120,4 @@ aa_pos_lig3 = aa_pos_ca #purple
tile_map=data.frame(tile=c("EMB","DPA","CDL","Ca"),
tile_colour=c("green","darkslategrey","navyblue","purple"))
drug_main_res = c(299 , 302, 303 , 306 , 327 , 592 , 594, 988, 1028)

72
config/gid.R
View file

@ -53,19 +53,19 @@ aa_arpeg_amp = c(123, 125, 213)
# Active site
#=============
active_aa_pos = sort(unique(c(
#rna_bind_aa_pos
#, binding_aa_pos
aa_ligplus_sry
, aa_ligplus_sam
, aa_ligplus_amp
, aa_ligplus_rna
, aa_plip_sry
, aa_plip_sam
, aa_plip_amp
, aa_plip_rna
, aa_arpeg_sry
, aa_arpeg_sam
, aa_arpeg_amp
#rna_bind_aa_pos
#, binding_aa_pos
aa_ligplus_sry
, aa_ligplus_sam
, aa_ligplus_amp
, aa_ligplus_rna
, aa_plip_sry
, aa_plip_sam
, aa_plip_amp
, aa_plip_rna
, aa_arpeg_sry
, aa_arpeg_sam
, aa_arpeg_amp
)))
##############################################################
@ -77,42 +77,42 @@ cat("\nNo. of active site residues for gene"
##############################################################
aa_pos_sry = sort(unique(c(
aa_ligplus_sry
, aa_plip_sry
, aa_arpeg_sry)))
aa_ligplus_sry
, aa_plip_sry
, aa_arpeg_sry)))
aa_pos_drug = aa_pos_sry
aa_pos_sry_hbond = sort(unique(c(
aa_ligplus_sry_hbond
, aa_plip_sry_hbond)))
aa_ligplus_sry_hbond
, aa_plip_sry_hbond)))
aa_pos_rna = sort(unique(c(
aa_ligplus_rna
, aa_plip_rna)))
aa_ligplus_rna
, aa_plip_rna)))
aa_pos_rna_hbond = sort(unique(c(
aa_ligplus_rna_hbond
, aa_plip_rna_hbond)))
aa_ligplus_rna_hbond
, aa_plip_rna_hbond)))
aa_pos_sam = sort(unique(c(
aa_ligplus_sam
, aa_plip_sam
, aa_arpeg_sam)))
aa_ligplus_sam
, aa_plip_sam
, aa_arpeg_sam)))
aa_pos_sam_hbond = sort(unique(c(
aa_ligplus_sam_hbond
, aa_plip_sam_hbond)))
aa_ligplus_sam_hbond
, aa_plip_sam_hbond)))
aa_pos_amp = sort(unique(c(
aa_ligplus_amp
, aa_plip_amp
, aa_arpeg_amp)))
aa_ligplus_amp
, aa_plip_amp
, aa_arpeg_amp)))
aa_pos_amp_hbond = sort(unique(c(
aa_ligplus_amp_hbond
, aa_plip_amp_hbond)))
aa_ligplus_amp_hbond
, aa_plip_amp_hbond)))
cat("\n==================================================="
@ -129,9 +129,11 @@ cat("\n==================================================="
##############################################################
# var for position customisation for plots
aa_pos_lig1 = aa_pos_rna
aa_pos_lig2 = aa_pos_sam
aa_pos_drug
aa_pos_lig1 = aa_pos_sam
aa_pos_lig2 = aa_pos_rna
aa_pos_lig3 = aa_pos_amp
tile_map=data.frame(tile=c("GID","DPA","CDL","Ca"),
tile_map=data.frame(tile=c("SRY","SAM","RNA","AMP"),
tile_colour=c("green","darkslategrey","navyblue","purple"))

28
scripts/Header_TT.R
View file

@ -68,8 +68,8 @@ if (!require("ggridges")) {
#devtools::install_github("kassambara/ggcorrplot")
if (!require ("ggbeeswarm")){
install.packages("ggbeeswarm")
library(ggbeeswarm)
install.packages("ggbeeswarm")
library(ggbeeswarm)
}
if (!require("plotly")) {
@ -128,7 +128,7 @@ if(!require("stats4")) {
}
if(!require("data.table")) {
install.packages("data.table")
install.packages("data.table")
library(data.table)
}
@ -237,17 +237,17 @@ consurf_palette2 = c("0" = "yellow2"
# )
consurf_colours = c(
"0" = rgb(1.00,1.00,0.59)
, "1" = rgb(0.04,0.49,0.51)
, "2" = rgb(0.29,0.69,0.75)
, "3" = rgb(0.65,0.86,0.90)
, "4" = rgb(0.84,0.94,0.94)
, "5" = rgb(1.00,1.00,1.00)
, "6" = rgb(0.98,0.92,0.96)
, "7" = rgb(0.98,0.78,0.86)
, "8" = rgb(0.94,0.49,0.67)
, "9" = rgb(0.63,0.16,0.37)
)
"0" = rgb(1.00,1.00,0.59)
, "1" = rgb(0.04,0.49,0.51)
, "2" = rgb(0.29,0.69,0.75)
, "3" = rgb(0.65,0.86,0.90)
, "4" = rgb(0.84,0.94,0.94)
, "5" = rgb(1.00,1.00,1.00)
, "6" = rgb(0.98,0.92,0.96)
, "7" = rgb(0.98,0.78,0.86)
, "8" = rgb(0.94,0.49,0.67)
, "9" = rgb(0.63,0.16,0.37)
)
##################################################

31
scripts/functions/corr_plot_data.R
View file

@ -16,6 +16,7 @@ corr_data_extract <- function(df
if ( missing(colnames_to_extract) || missing(colnames_display_key) ){
# log10maf
df$maf2 = log10(df$maf) # can't see otherwise
sum(is.na(df$maf2))
@ -40,7 +41,7 @@ corr_data_extract <- function(df
, "consurf_score" , "snap2_score" , "provean_score"
, "ligand_affinity_change", "mmcsm_lig"
#, "ddg_dynamut", "ddg_encom", "dds_encom", "ddg_mcsm", "ddg_sdm", "ddg_duet"
)
)
display_common_colnames = c( drug, "dst_mode"
, "DUET" , "FoldX" , "DeepDDG", "Dynamut2"
@ -51,7 +52,7 @@ corr_data_extract <- function(df
, "ConSurf" , "SNAP2" , "PROVEAN"
, "mCSM-lig", "mmCSM-lig"
# , "Dynamut" , "ENCoM-DDG" , "mCSM" , "SDM" , "DUET-d" , "ENCoM-DDS"
)
)
if (tolower(gene)%in%geneL_normal){
colnames_to_extract = c(common_colnames)
@ -59,21 +60,21 @@ corr_data_extract <- function(df
corr_df = df[,colnames_to_extract]
colnames(corr_df) = display_colnames
}
}
if (tolower(gene)%in%geneL_ppi2){
colnames_to_extract = c(common_colnames ,"mcsm_ppi2_affinity", ppi2Dist_colname)
display_colnames = c(display_common_colnames,"mCSM-PPI2" , "PPI-Dist")
corr_df = df[,colnames_to_extract]
colnames(corr_df) = display_colnames
}
}
if (tolower(gene)%in%geneL_na){
colnames_to_extract = c(common_colnames,"mcsm_na_affinity", naDist_colname)
display_colnames = c(display_common_colnames, "mCSM-NA", "NA-Dist")
display_colnames = c(display_common_colnames, "mCSM-NA", "NCA-Dist")
corr_df = df[,colnames_to_extract]
colnames(corr_df) = display_colnames
}
}
# [optional] arg: extract_scaled_cols
if (extract_scaled_cols){
@ -98,18 +99,18 @@ corr_data_extract <- function(df
# colnames(corr_df)[colnames(corr_df)%in%colnames_to_extract] <- display_colnames
# colnames(corr_df)
cat("\nExtracted ncols:", ncol(corr_df)
,"\nRenaming successful")
cat("\nExtracted ncols:", ncol(corr_df)
,"\nRenaming successful")
cat("\nSneak peak...")
print(head(corr_df))
cat("\nSneak peak...")
print(head(corr_df))
# Move drug column to the end
last_col = colnames(corr_df[ncol(corr_df)])
#corr_df_f = corr_df %>% dplyr::relocate(all_of(drug), .after = last_col)
# Move drug column to the end
last_col = colnames(corr_df[ncol(corr_df)])
#corr_df_f = corr_df %>% dplyr::relocate(all_of(drug), .after = last_col)
#return(corr_df_f)
return(corr_df)
#return(corr_df_f)
return(corr_df)
}

4
scripts/functions/dm_om_data.R
View file

@ -222,12 +222,12 @@ dm_om_wf_lf_data <- function(df
}
if (tolower(gene)%in%geneL_na){
colnames_to_extract = c(common_colnames,"mcsm_na_affinity" , "mcsm_na_scaled" , "mcsm_na_outcome" , naDist_colname)
colnames_to_extract = c(common_colnames ,"mcsm_na_affinity" , "mcsm_na_scaled" , "mcsm_na_outcome" , naDist_colname)
display_colnames = c(display_common_colnames, "mcsm_na_affinity" , mcsm_na_dn , "mcsm_na_outcome" , na_dist_dn)
comb_df_sl = df[, colnames_to_extract]
# Rename cols: display names
colnames(comb_df) = display_colnames
colnames(comb_df_sl) = display_colnames
# Affinity filtered data: mcsm-na --> naDist_colname
comb_df_sl_na = comb_df_sl[comb_df_sl[[na_dist_dn]]<DistCutOff,]

2
scripts/functions/plotting_globals.R
View file

@ -41,7 +41,7 @@ LigDist_cutoff <<- 10
DistCutOff <<- 10
ppi2Dist_colname <<- "interface_dist"
naDist_colname <<- "TBC"
naDist_colname <<- "nca_distance" # added it
#==================
# Angstroms symbol

220
scripts/plotting/get_plotting_dfs.R
View file

@ -6,7 +6,7 @@
# working dir and loading libraries
getwd()
source("~/git/LSHTM_analysis/scripts/Header_TT.R")
source("~/git/LSHTM_analysis/scripts/plotting/plotting_colnames.R")
# cmd args passed
# in from other scripts
# to call this
@ -43,7 +43,7 @@ import_dirs(drug, gene)
# call: plotting_data()
#---------------------------
if (!exists("infile_params") && exists("gene")){
#if (!is.character(infile_params) && exists("gene")){ # when running as cmd
#if (!is.character(infile_params) && exists("gene")){ # when running as cmd
in_filename_params = paste0(tolower(gene), "_all_params.csv")
infile_params = paste0(outdir, "/", in_filename_params)
cat("\nInput file for mcsm comb data not specified, assuming filename: ", infile_params, "\n")
@ -70,7 +70,7 @@ cat("\nLigand distance colname:", LigDist_colname
# call: combining_dfs_plotting()
#--------------------------------
if (!exists("infile_metadata") && exists("gene")){
#if (!is.character(infile_metadata) && exists("gene")){ # when running as cmd
#if (!is.character(infile_metadata) && exists("gene")){ # when running as cmd
in_filename_metadata = paste0(tolower(gene), "_metadata.csv") # part combined for gid
infile_metadata = paste0(outdir, "/", in_filename_metadata)
cat("\nInput file for gene metadata not specified, assuming filename: ", infile_metadata, "\n")
@ -122,52 +122,52 @@ merged_df3 = all_plot_dfs[[2]]
# ####################################################################
#
# #source(paste0(plot_script_path, "dm_om_data.R")) # calling the function directly instead
# geneL_normal = c("pnca")
# geneL_na = c("gid", "rpob")
# geneL_ppi2 = c("alr", "embb", "katg", "rpob")
#
# all_dm_om_df = dm_om_wf_lf_data(df = merged_df3, gene = gene)
#
# wf_duet = all_dm_om_df[['wf_duet']]
# lf_duet = all_dm_om_df[['lf_duet']]
#
# wf_mcsm_lig = all_dm_om_df[['wf_mcsm_lig']]
# lf_mcsm_lig = all_dm_om_df[['lf_mcsm_lig']]
#
# wf_foldx = all_dm_om_df[['wf_foldx']]
# lf_foldx = all_dm_om_df[['lf_foldx']]
#
# wf_deepddg = all_dm_om_df[['wf_deepddg']]
# lf_deepddg = all_dm_om_df[['lf_deepddg']]
#
# wf_dynamut2 = all_dm_om_df[['wf_dynamut2']]
# lf_dynamut2 = all_dm_om_df[['lf_dynamut2']]
#
# wf_consurf = all_dm_om_df[['wf_consurf']]
# lf_consurf = all_dm_om_df[['lf_consurf']]
#
# wf_snap2 = all_dm_om_df[['wf_snap2']]
# lf_snap2 = all_dm_om_df[['lf_snap2']]
#
# wf_provean = all_dm_om_df[['wf_provean']]
# lf_provean = all_dm_om_df[['lf_provean']]
#
# # NEW
# wf_dist_gen = all_dm_om_df[['wf_dist_gen']]
# lf_dist_gen = all_dm_om_df[['lf_dist_gen']]
#
# if (tolower(gene)%in%geneL_na){
# wf_mcsm_na = all_dm_om_df[['wf_mcsm_na']]
# lf_mcsm_na = all_dm_om_df[['lf_mcsm_na']]
# }
#
# if (tolower(gene)%in%geneL_ppi2){
# wf_mcsm_ppi2 = all_dm_om_df[['wf_mcsm_ppi2']]
# lf_mcsm_ppi2 = all_dm_om_df[['lf_mcsm_ppi2']]
# }
#
# s2 = c("\nSuccessfully sourced other_plots_data.R")
# cat(s2)
geneL_normal = c("pnca")
geneL_na = c("gid", "rpob")
geneL_ppi2 = c("alr", "embb", "katg", "rpob")
all_dm_om_df = dm_om_wf_lf_data(df = merged_df3, gene = gene)
wf_duet = all_dm_om_df[['wf_duet']]
lf_duet = all_dm_om_df[['lf_duet']]
wf_mcsm_lig = all_dm_om_df[['wf_mcsm_lig']]
lf_mcsm_lig = all_dm_om_df[['lf_mcsm_lig']]
wf_foldx = all_dm_om_df[['wf_foldx']]
lf_foldx = all_dm_om_df[['lf_foldx']]
wf_deepddg = all_dm_om_df[['wf_deepddg']]
lf_deepddg = all_dm_om_df[['lf_deepddg']]
wf_dynamut2 = all_dm_om_df[['wf_dynamut2']]
lf_dynamut2 = all_dm_om_df[['lf_dynamut2']]
wf_consurf = all_dm_om_df[['wf_consurf']]
lf_consurf = all_dm_om_df[['lf_consurf']]
wf_snap2 = all_dm_om_df[['wf_snap2']]
lf_snap2 = all_dm_om_df[['lf_snap2']]
wf_provean = all_dm_om_df[['wf_provean']]
lf_provean = all_dm_om_df[['lf_provean']]
# NEW
wf_dist_gen = all_dm_om_df[['wf_dist_gen']]
lf_dist_gen = all_dm_om_df[['lf_dist_gen']]
if (tolower(gene)%in%geneL_na){
wf_mcsm_na = all_dm_om_df[['wf_mcsm_na']]
lf_mcsm_na = all_dm_om_df[['lf_mcsm_na']]
}
if (tolower(gene)%in%geneL_ppi2){
wf_mcsm_ppi2 = all_dm_om_df[['wf_mcsm_ppi2']]
lf_mcsm_ppi2 = all_dm_om_df[['lf_mcsm_ppi2']]
}
s2 = c("\nSuccessfully sourced other_plots_data.R")
cat(s2)
#
# ####################################################################
# # Data for Lineage barplots: WF and LF dfs
@ -175,71 +175,71 @@ merged_df3 = all_plot_dfs[[2]]
# # location: scripts/functions/lineage_plot_data.R
# ####################################################################
#
# #source(paste0(plot_script_path, "lineage_data.R"))
source(paste0(plot_script_path, "lineage_data.R"))
# # converted to a function. Moved lineage_data.R to redundant/
# lineage_dfL = lineage_plot_data(merged_df2
# , lineage_column_name = "lineage"
# , remove_empty_lineage = F
# , lineage_label_col_name = "lineage_labels"
# , id_colname = "id"
# , snp_colname = "mutationinformation"
# )
#
# lin_wf = lineage_dfL[['lin_wf']]
# lin_lf = lineage_dfL[['lin_lf']]
#
# s3 = c("\nSuccessfully sourced lineage_data.R")
# cat(s3)
#
# ####################################################################
# # Data for corr plots:
# # My function: corr_data_extract()
# # location: scripts/functions/corr_plot_data.R
# ####################################################################
# # make sure the above script works because merged_df2_combined is needed
# merged_df3 = as.data.frame(merged_df3)
#
# corr_df_m3_f = corr_data_extract(merged_df3
lineage_dfL = lineage_plot_data(merged_df2
, lineage_column_name = "lineage"
, remove_empty_lineage = F
, lineage_label_col_name = "lineage_labels"
, id_colname = "id"
, snp_colname = "mutationinformation"
)
lin_wf = lineage_dfL[['lin_wf']]
lin_lf = lineage_dfL[['lin_lf']]
s3 = c("\nSuccessfully sourced lineage_data.R")
cat(s3)
####################################################################
# Data for corr plots:
# My function: corr_data_extract()
# location: scripts/functions/corr_plot_data.R
####################################################################
# make sure the above script works because merged_df2_combined is needed
merged_df3 = as.data.frame(merged_df3)
corr_df_m3_f = corr_data_extract(merged_df3
, gene = gene
, drug = drug
, extract_scaled_cols = F)
head(corr_df_m3_f)
# corr_df_m2_f = corr_data_extract(merged_df2
# , gene = gene
# , drug = drug
# , extract_scaled_cols = F)
# head(corr_df_m3_f)
#
# # corr_df_m2_f = corr_data_extract(merged_df2
# # , gene = gene
# # , drug = drug
# # , extract_scaled_cols = F)
# # head(corr_df_m2_f)
#
# s4 = c("\nSuccessfully sourced Corr_data.R")
# cat(s4)
#
# ########################################################################
# # End of script
# ########################################################################
# head(corr_df_m2_f)
s4 = c("\nSuccessfully sourced Corr_data.R")
cat(s4)
########################################################################
# End of script
########################################################################
# if ( all( length(s1), length(s2), length(s3), length(s4) ) > 0 ){
# cat(
# "\n##################################################"
# , "\nSuccessful: get_plotting_dfs.R worked!"
# , "\n###################################################\n")
# } else {
# cat(
# "\n#################################################"
# , "\nFAIL: get_plotting_dfs.R didn't complete fully!Please check"
# , "\n###################################################\n" )
# cat(
# "\n##################################################"
# , "\nSuccessful: get_plotting_dfs.R worked!"
# , "\n###################################################\n")
# } else {
# cat(
# "\n#################################################"
# , "\nFAIL: get_plotting_dfs.R didn't complete fully!Please check"
# , "\n###################################################\n" )
# }
#
# ########################################################################
# # clear excess variables: from the global enviornment
#
# vars0 = ls(envir = .GlobalEnv)[grepl("curr_*", ls(envir = .GlobalEnv))]
# vars1 = ls(envir = .GlobalEnv)[grepl("^cols_to*", ls(envir = .GlobalEnv))]
# vars2 = ls(envir = .GlobalEnv)[grepl("pivot_cols_*", ls(envir = .GlobalEnv))]
# vars3 = ls(envir = .GlobalEnv)[grepl("expected_*", ls(envir = .GlobalEnv))]
#
# rm( infile_metadata
# , infile_params
# , vars0
# , vars1
# , vars2
# , vars3)
########################################################################
# clear excess variables: from the global enviornment
vars0 = ls(envir = .GlobalEnv)[grepl("curr_*", ls(envir = .GlobalEnv))]
vars1 = ls(envir = .GlobalEnv)[grepl("^cols_to*", ls(envir = .GlobalEnv))]
vars2 = ls(envir = .GlobalEnv)[grepl("pivot_cols_*", ls(envir = .GlobalEnv))]
vars3 = ls(envir = .GlobalEnv)[grepl("expected_*", ls(envir = .GlobalEnv))]
rm( infile_metadata
, infile_params
, vars0
, vars1
, vars2
, vars3)

89
scripts/plotting/plotting_thesis/appendix_tables.R
View file

@ -38,18 +38,18 @@ angstroms_symbol
df3 = merged_df3
cols_to_output = c("mutationinformation"
, "position"
, affinity_dist_colnames[1]
, "ligand_affinity_change"
, "ligand_outcome"
, "mmcsm_lig"
, "mmcsm_lig_outcome"
, affinity_dist_colnames[2]
, "mcsm_ppi2_affinity"
, "mcsm_ppi2_outcome"
, "maf"
, "or_mychisq"
, "pval_fisher")
, "position"
, affinity_dist_colnames[1]
, "ligand_affinity_change"
, "ligand_outcome"
, "mmcsm_lig"
, "mmcsm_lig_outcome"
, affinity_dist_colnames[2]
, "mcsm_ppi2_affinity"
, "mcsm_ppi2_outcome"
, "maf"
, "or_mychisq"
, "pval_fisher")
cols_to_output
df3_output = df3[, cols_to_output]
@ -65,12 +65,12 @@ colnames(df3_output)
df3_output$p_adj_fdr = p.adjust(df3_output$pval_fisher, method = "fdr")
df3_output$signif_fdr = df3_output$p_adj_fdr
df3_output = dplyr::mutate(df3_output
, 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'))
, 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'))
# rounding
df3_output$or_mychisq = round(df3_output$or_mychisq,2)
df3_output$p_adj_fdr = round(df3_output$p_adj_fdr,2)
@ -101,17 +101,17 @@ head(df3_output)
df_lig = df3_output[df3_output[[LigDist_colname]]<DistCutOff,]
cols_to_output_lig = c("mutationinformation"
, "position"
, LigDist_colname
, "ligand_affinity_change"
, "ligand_outcome"
, "mmcsm_lig"
, "mmcsm_lig_outcome"
, "maf_percent"
, "or_mychisq"
, "pval_fisher"
, "p_adj_fdr"
, "signif_fdr")
, "position"
, LigDist_colname
, "ligand_affinity_change"
, "ligand_outcome"
, "mmcsm_lig"
, "mmcsm_lig_outcome"
, "maf_percent"
, "or_mychisq"
, "pval_fisher"
, "p_adj_fdr"
, "signif_fdr")
# select cols
Out_df_lig = df_lig[, cols_to_output_lig]
@ -123,17 +123,17 @@ Out_df_ligS = Out_df_lig[order(-Out_df_lig$or_mychisq, Out_df_lig$maf_percent),
head(Out_df_ligS); tail(Out_df_ligS)
colsNames_to_output_lig = c("Mutation"
, "position"
, paste0("Lig-Dist (", angstroms_symbol, ")")
, "mCSM-ligand affinity"
, "mCSM ligand_outcome"
, "mmCSM-ligand affinity"
, "mmCSM ligand_outcome"
, paste0("MAF ","(%)")
, "Odds Ratio"
, "P-value"
, "Adjusted P-value"
, "P-value significance")
, "position"
, paste0("Lig-Dist (", angstroms_symbol, ")")
, "mCSM-ligand affinity"
, "mCSM ligand_outcome"
, "mmCSM-ligand affinity"
, "mmCSM ligand_outcome"
, paste0("MAF ","(%)")
, "Odds Ratio"
, "P-value"
, "Adjusted P-value"
, "Adjusted P-value significance")
colnames(Out_df_ligS) = colsNames_to_output_lig
head(Out_df_ligS)
@ -142,8 +142,8 @@ head(Out_df_ligS)
# write output file: KS test within grpup
#----------------------
Out_ligT = paste0(outdir_stats
, tolower(gene)
, "_lig_muts.csv")
, tolower(gene)
, "_lig_muts.csv")
cat("Output of Ligand muts:", Out_ligT )
write.csv(Out_df_ligS, Out_ligT, row.names = FALSE)
@ -179,12 +179,13 @@ Out_df_ppi2S = Out_df_ppi2[order(-Out_df_ppi2$or_mychisq, Out_df_ppi2$maf_percen
colsNames_to_output_ppi2 = c("Mutation"
, "position"
, paste0("PPI2-Dist (", angstroms_symbol, ")")
, paste0("mCSM-PPI2 (", delta_symbol, ")")
, paste0("mCSM-PPI2 (", delta_symbol,delta_symbol,"G)")
, "mCSM-PPI2 outcome"
, paste0("MAF ","(%)")
, "Odds Ratio"
, "P-value"
, "Adjusted P-value"
, "P-value significance")
, "Adjusted P-value significance")
colnames(Out_df_ppi2S) = colsNames_to_output_ppi2
Out_df_ppi2S

745
scripts/plotting/plotting_thesis/basic_barplots.R Executable file → Normal file
View file

@ -25,23 +25,28 @@
#=============
# Data: Input
#==============
#source("~/git/LSHTM_analysis/config/alr.R")
source("~/git/LSHTM_analysis/config/embb.R")
#source("~/git/LSHTM_analysis/config/katg.R")
#source("~/git/LSHTM_analysis/config/gid.R")
#source("~/git/LSHTM_analysis/config/pnca.R")
#source("~/git/LSHTM_analysis/config/embb.R")
#source("~/git/LSHTM_analysis/config/gid.R")
#source("~/git/LSHTM_analysis/config/alr.R")
source("~/git/LSHTM_analysis/config/katg.R")
#source("~/git/LSHTM_analysis/config/rpob.R")
source("~/git/LSHTM_analysis/scripts/plotting/get_plotting_dfs.R")
source("~/git/LSHTM_analysis/scripts/plotting/plotting_colnames.R")
#source("~/git/LSHTM_analysis/scripts/plotting/plotting_colnames.R") sourced by above
# sanity check
cat("\nSourced plotting cols as well:", length(plotting_cols))
####################################################
class(merged_df3)
merged_df3 = as.data.frame(merged_df3)
class(df3)
class(merged_df3)
head(merged_df3$pos_count)
nc_pc_CHANGE = which(colnames(merged_df3)== "pos_count")
nc_pc_CHANGE = which(colnames(merged_df3)== "pos_count"); nc_pc_CHANGE
colnames(merged_df3)[nc_pc_CHANGE] = "df2_pos_count_all"
head(merged_df3$pos_count)
head(merged_df3$df2_pos_count_all)
@ -52,8 +57,7 @@ merged_df3 = merged_df3[, !colnames(merged_df3)%in%c("pos_count")]
head(merged_df3$pos_count)
df3 = merged_df3[, colnames(merged_df3)%in%plotting_cols]
#"nca_distance"%in%colnames(df3)
#=======
# output
@ -62,192 +66,9 @@ outdir_images = paste0("~/git/Writing/thesis/images/results/", tolower(gene), "/
cat("plots will output to:", outdir_images)
###########################################################
# ConSurf labels
#------------------------------
# plot default sizes
#------------------------------
sts = 22
subtitle_colour = "black"
geom_ls = 10
##############################################################
#------------------------------
# stability barplots:
outcome_stability_cols
# label_categories should be = levels(as.factor(plot_df[[df_colname]]))
#-------------------------
# duetP
duetP = stability_count_bp(plotdf = df3
, df_colname = "duet_outcome"
, leg_title = "mCSM-DUET"
#, label_categories = labels_duet
, yaxis_title = "Number of nsSNPs"
, leg_position = "none"
, subtitle_text = "mCSM-DUET"
, geom_ls = geom_ls
, bar_fill_values = c("#F8766D", "#00BFC4")
, sts = sts
, subtitle_colour= subtitle_colour)
# foldx
foldxP = stability_count_bp(plotdf = df3
, df_colname = "foldx_outcome"
#, leg_title = "FoldX"
#, label_categories = labels_foldx
, yaxis_title = ""
, leg_position = "none"
, subtitle_text = "FoldX"
, geom_ls = geom_ls
, bar_fill_values = c("#F8766D", "#00BFC4")
, sts = sts
, subtitle_colour= subtitle_colour)
# deepddg
deepddgP = stability_count_bp(plotdf = df3
, df_colname = "deepddg_outcome"
#, leg_title = "DeepDDG"
#, label_categories = labels_deepddg
, yaxis_title = "Number of nsSNPs"
, leg_position = "none"
, subtitle_text = "DeepDDG"
, geom_ls = geom_ls
, bar_fill_values = c("#F8766D", "#00BFC4")
, sts = sts
, subtitle_colour= subtitle_colour)
# deepddg
dynamut2P = stability_count_bp(plotdf = df3
, df_colname = "ddg_dynamut2_outcome"
#, leg_title = "Dynamut2"
#, label_categories = labels_ddg_dynamut2_outcome
, yaxis_title = ""
, leg_position = "none"
, subtitle_text = "Dynamut2"
, geom_ls = geom_ls
, bar_fill_values = c("#F8766D", "#00BFC4")
, sts = sts
, subtitle_colour= subtitle_colour)
dynamut2P
# # extract common legend
# common_legend = get_legend(duetP +
# guides(color = guide_legend(nrow = 1)) +
# theme(legend.position = "top"))
#
# #==========================
# #output: STABILITY PLOTS
# #===========================
# bp_stability_CLP = paste0(outdir_images
# , tolower(gene)
# ,"_bp_stability_CL.svg")
#
# svg(bp_stability_CLP, width = 15, height = 12)
# print(paste0("plot filename:", bp_stability_CLP))
#
# cowplot::plot_grid(
# common_legend,
# cowplot::plot_grid(duetP, foldxP
# , deepddgP, dynamut2P
# , nrow = 2
# , ncol = 2
# #, labels = c("(a)", "(b)", "(c)", "(d)")
# , labels = "AUTO"
# , label_size = 25)
# , ncol = 1
# , nrow = 2
# , rel_heights = c(0.4/10,9/10))
#
# dev.off()
###########################################################
#=========================
# Conservation outcome
# check this var:
outcome_conservation_cols
all(df3$consurf_colour_rev == df3$consurf_outcome)
#df3["consurf_outcome"] = as.factor(df3["consurf_outcome"])
levels(df3[["consurf_outcome"]])
#==========================
table(df3$consurf_outcome)
ggplot(df3, aes_string(x = "consurf_outcome")) +
geom_bar(aes(fill = eval(parse(text = "consurf_outcome")))
, show.legend = TRUE) +
scale_fill_manual(name = ""
, values = consurf_colours
#, labels = levels(df3[["snap2_outcome"]])
)
# consurf# had to turn label categories off for consurf
consurfP = stability_count_bp(plotdf = df3
, df_colname = "consurf_outcome"
#, leg_title = "ConSurf"
#, label_categories = labels_consurf
, yaxis_title = "Number of nsSNPs"
, leg_position = "top"
, subtitle_text = "ConSurf"
, geom_ls = 5
, bar_fill_values = consurf_colours # from globals
, sts = sts
, subtitle_colour= subtitle_colour)
consurfP
# provean
proveanP = stability_count_bp(plotdf = df3
, df_colname = "provean_outcome"
#, leg_title = "PROVEAN"
#, label_categories = labels_provean
, yaxis_title = ""
, leg_position = "top"
, subtitle_text = "PROVEAN"
, geom_ls = geom_ls
, bar_fill_values = c("#D01C8B", "#F1B6DA") # light pink and deep
, sts = sts
, subtitle_colour= subtitle_colour)
# snap2
snap2P = stability_count_bp(plotdf = df3
, df_colname = "snap2_outcome"
#, leg_title = "SNAP2"
#, label_categories = labels_snap2
, yaxis_title = ""
, leg_position = "top"
, subtitle_text = "SNAP2"
, geom_ls = geom_ls
, bar_fill_values = c("#D01C8B", "#F1B6DA") # light pink and deep
, sts = sts
, subtitle_colour= subtitle_colour)
#============================
# output: CONSERVATION PLOTS
#============================
# bp_conservation_CLP = paste0(outdir_images
# ,tolower(gene)
# ,"_bp_conservation_CL.svg" )
#
# print(paste0("plot filename:", bp_conservation_CLP))
# svg(bp_conservation_CLP, width = 15, height = 6.5)
#
# cowplot::plot_grid(proveanP, snap2P, consurfP
# , nrow = 1
# , ncol = 3
# #, labels = c("(a)", "(b)", "(c)", "(d)")
# , labels = "AUTO"
# , label_size = 25
# #, rel_heights = c(0.4/10,9/10))
# , rel_widths = c(0.9, 0.9, 1.1))
#
#
# dev.off()
###########################################################
#=========================
# Affinity outcome
# check this var: outcome_cols_affinity
@ -272,17 +93,19 @@ common_bp_title = paste0("Sites <", DistCutOff, angstroms_symbol)
mLigP = stability_count_bp(plotdf = df3_lig
, df_colname = "ligand_outcome"
#, leg_title = "mCSM-lig"
#, label_categories = labels_lig
#, bp_plot_title = paste(common_bp_title, "ligand")
, yaxis_title = "Number of nsSNPs"
, leg_position = "none"
, subtitle_text = "mCSM-lig"
, geom_ls = geom_ls
, bar_fill_values = c("#F8766D", "#00BFC4")
, sts = sts
, subtitle_colour= subtitle_colour
#, bp_plot_title = paste(common_bp_title, "ligand")
)
, subtitle_colour= "black"
, sts = 10
, lts = 8
, ats = 12
, als = 11
, ltis = 11
, geom_ls = 2.5)
mLigP
#------------------------------
# barplot for ligand affinity:
# <10 Ang of ligand
@ -292,236 +115,74 @@ mmLigP = stability_count_bp(plotdf = df3_lig
, df_colname = "mmcsm_lig_outcome"
#, leg_title = "mmCSM-lig"
#, label_categories = labels_mmlig
#, bp_plot_title = paste(common_bp_title, "ligand")
, yaxis_title = ""
, leg_position = "none"
, subtitle_text = "mmCSM-lig"
, geom_ls = geom_ls
, bar_fill_values = c("#F8766D", "#00BFC4")
, sts = sts
, subtitle_colour= subtitle_colour
#, bp_plot_title = paste(common_bp_title, "ligand")
, subtitle_colour= "black"
, sts = 10
, lts = 8
, ats = 12
, als = 11
, ltis = 11
, geom_ls = 2.5
)
mmLigP
#------------------------------
# barplot for ppi2 affinity
# <10 Ang of interface
#------------------------------
ppi2P = stability_count_bp(plotdf = df3_ppi2
, df_colname = "mcsm_ppi2_outcome"
#, leg_title = "mCSM-ppi2"
#, label_categories = labels_ppi2
, yaxis_title = ""
, leg_position = "none"
, subtitle_text = "mCSM-ppi2"
, geom_ls = geom_ls
, bar_fill_values = c("#F8766D", "#00BFC4")
, sts = sts
, subtitle_colour= subtitle_colour
, bp_plot_title = paste(common_bp_title, "interface")
)
if (tolower(gene)%in%geneL_ppi2){
ppi2P = stability_count_bp(plotdf = df3_ppi2
, df_colname = "mcsm_ppi2_outcome"
#, leg_title = "mCSM-ppi2"
#, label_categories = labels_ppi2
#, bp_plot_title = paste(common_bp_title, "PP-interface")
# # extract common legend
# common_legend_aff = get_legend(mLigP +
# guides(color = guide_legend(nrow = 1)) +
# theme(legend.position = "top"))
#
# #==========================
# # output: AFFINITY PLOTS
# #==========================
# bp_affinity_CLP = paste0(outdir_images
# ,tolower(gene)
# ,"_bp_affinity_CL.svg" )
#
# print(paste0("plot filename:", bp_stability_CLP))
# svg(bp_affinity_CLP, width = 15, height = 6.5)
#
# cowplot::plot_grid(
# common_legend,
# cowplot::plot_grid(mLigP, mmLigP
# , ppi2P
# , nrow = 1
# , ncol = 3
# #, labels = c("(a)", "(b)", "(c)", "(d)")
# , labels = "AUTO"
# , label_size = 25)
# , ncol = 1
# , nrow = 2
# , rel_heights = c(0.4/10,9/10))
# #, rel_widths = c(1,1,1))
#
#
# dev.off()
, yaxis_title = "Number of nsSNPs"
, leg_position = "none"
, subtitle_text = "mCSM-ppi2"
, bar_fill_values = c("#F8766D", "#00BFC4")
, subtitle_colour= "black"
, sts = 10
, lts = 8
, ats = 12
, als = 11
, ltis = 11
, geom_ls = 2.5
)
ppi2P
}
#----------------------------
# barplot for ppi2 affinity
# <10 Ang of interface
#------------------------------
if (tolower(gene)%in%geneL_na){
################################################################
nca_distP = stability_count_bp(plotdf = df3_na
, df_colname = "mcsm_na_outcome"
#, leg_title = "mCSM-NA"
#, label_categories =
#, bp_plot_title = paste(common_bp_title, "Dist to NA")
#####################################################################
#============
# Plot labels
#============
tit1 = "Stability outcome"
tit2 = "Affinity outcome"
tit3 = "Conservation outcome"
pt_size = 30
theme_georgia <- function(...) {
theme_gray(base_family = "sans", ...) +
theme(plot.title = element_text(face = "bold"))
, yaxis_title = "Number of nsSNPs"
, leg_position = "none"
, subtitle_text = "mCSM-NA"
, bar_fill_values = c("#F8766D", "#00BFC4")
, subtitle_colour= "black"
, sts = 10
, lts = 8
, ats = 12
, als = 11
, ltis = 11
, geom_ls = 2.5
)
nca_distP
}
title_theme <- calc_element("plot.title", theme_georgia())
pt1 = ggdraw() +
draw_label(
tit1,
fontfamily = title_theme$family,
fontface = title_theme$face,
#size = title_theme$size
size = pt_size
)
pt2 = ggdraw() +
draw_label(
tit2,
fontfamily = title_theme$family,
fontface = title_theme$face,
size = pt_size
)
pt3 = ggdraw() +
draw_label(
tit3,
fontfamily = title_theme$family,
fontface = title_theme$face,
size = pt_size
)
# extract common legend
common_legend_outcome = get_legend(mLigP +
guides(color = guide_legend(nrow = 1)) +
theme(legend.position = "top"))
my_label_size = 25
#======================
# Output plot function
#======================
OutPlotBP = function(x){
cowplot::plot_grid(
cowplot::plot_grid(pt1,
common_legend_outcome,
cowplot::plot_grid( duetP, foldxP
, deepddgP, dynamut2P
, nrow = 2
, ncol = 2
, labels = c("A", "B", "C","D")
, label_size = my_label_size
)
, ncol = 1
, rel_heights = c(7, 3, 90)),
cowplot::plot_grid(pt2,
cowplot::plot_grid(mLigP, mmLigP, ppi2P
, nrow = 1
, ncol = 3
, labels = c("E","F", "G")
, label_size = my_label_size
)
, ncol = 1
, rel_heights = c(1, 9)),
cowplot::plot_grid(pt3,
cowplot::plot_grid(consurfP, proveanP, snap2P
, nrow = 1
, ncol = 3
, labels = c("H", "I", "J")
, labels_x = 0.2
, label_size = my_label_size
, rel_widths = c(0.2, 0.2, 0.2)
)
, ncol = 1
, rel_heights = c(0.07, 0.93)
),
nrow = 3,
rel_heights = c(0.58, 0.25, 0.27),
align = "hv"
)
}
#=====================
# OutPlot: svg and png
#======================
#ratio 11.69 by 8.27
w = 8.27*2
h = 11.69*2
#svg
bp_all_CLP = paste0(outdir_images
,tolower(gene)
,"_bp_all_CL.svg")
cat(paste0("plot filename:", bp_all_CLP))
svg(bp_all_CLP, width = w, height = h)
OutPlotBP()
dev.off()
#png
bp_all_CLP_png = paste0(outdir_images
,tolower(gene)
,"_bp_all_CL.png")
cat(paste0("plot filename:", bp_all_CLP_png))
png(bp_all_CLP_png, width = w, height = h, units = "in", res = 300 )
OutPlotBP()
dev.off()
#####################################################################
# test
#
# setDT(df3)[, pos_count2 := .N, by = .(eval(parse(text = "position")))]
# foo = df3[, c("mutationinformation", "position")]
# df4 = foo[, c("mutationinformation", "position")]
#
#
# var_pos = "position"
# df4 =
# df4 %>%
# dplyr::add_count(eval(parse(text = var_pos)))
#
# class(df4)
# df4 = as.data.frame(df4)
# class(df4)
# nc_change = which(colnames(df4) == "n")
# colnames(df4)[nc_change] <- "pos_count"
# class(df4)
#
# setDT(df4)[, pos_count2 := .N, by = .(eval(parse(text = "position")))]
# class(df4)
#
# all(df4$pos_count==df4$pos_count2)
#
# # %>%
# #group_by(pos_count = position)
#
# # df4 =
# # df4 %>%
# # dplyr::group_by(position) %>%
# # count(position)
#foo2 = df4[, c("mutationinformation", "position", "pos_count")]
#####################################################################
# ------------------------------
# bp site site count: ALL
# <10 Ang ligand
# ------------------------------
posC_all = site_snp_count_bp(plotdf = df3
, df_colname = "position"
, xaxis_title = "Number of nsSNPs"
, yaxis_title = "Number of Sites"
, subtitle_size = 20)
# ------------------------------
# bp site site count: mCSM-lig
@ -532,55 +193,233 @@ common_bp_title = paste0("Sites <", DistCutOff, angstroms_symbol)
posC_lig = site_snp_count_bp(plotdf = df3_lig
, df_colname = "position"
, xaxis_title = "Number of nsSNPs"
, yaxis_title = "Number of Sites"#+ annotate("text", x = 1.5, y = 2.2, label = "Text No. 1")
#, subtitle_text = paste0(common_bp_title, " ligand")
, yaxis_title = "Number of Sites"
, subtitle_colour = "chocolate4"
, subtitle_text = ""
, subtitle_size = 8
, subtitle_colour = subtitle_colour)
, geom_ls = 2.6
, leg_text_size = 10
, axis_text_size = 10
, axis_label_size = 10)
posC_lig
# ------------------------------
# bp site site count: ppi2
# < 10 Ang interface
# ------------------------------
if (tolower(gene)%in%geneL_ppi2){
posC_ppi2 = site_snp_count_bp(plotdf = df3_ppi2
, df_colname = "position"
, xaxis_title = "Number of nsSNPs"
, yaxis_title = "Number of Sites"
, subtitle_colour = "chocolate4"
, subtitle_text = ""
, subtitle_size = 8
, geom_ls = 2.6
, leg_text_size = 10
, axis_text_size = 10
, axis_label_size = 10)
posC_ppi2
}
posC_ppi2 = site_snp_count_bp(plotdf = df3_ppi2
, df_colname = "position"
, xaxis_title = "Number of nsSNPs"
, yaxis_title = "Number of Sites"
, subtitle_text = paste0(common_bp_title, " interface")
, subtitle_size = 20
, subtitle_colour = subtitle_colour)
posC_ppi2
# ------------------------------
#FIXME: bp site site count: na
# < 10 Ang TBC
# bp site site count: NCA dist
# < 10 Ang nca
# ------------------------------
# posC_na = site_snp_count_bp(plotdf = df3_na
# , df_colname = "position"
# , xaxis_title = ""
# , yaxis_title = "")
if (tolower(gene)%in%geneL_na){
#===========================
# output: SITE SNP count:
# all + affinity
#==========================
# my_label_size = 25
# pos_count_combined_CLP = paste0(outdir_images
# ,tolower(gene)
# ,"_pos_count_PS_AFF.svg")
#
#
# svg(pos_count_combined_CLP, width = 20, height = 5.5)
# print(paste0("plot filename:", pos_count_combined_CLP))
#
# cowplot::plot_grid(posC_all, posC_lig, posC_ppi2
# #, posC_na
# , nrow = 1
# , ncol = 3
# , labels = "AUTO"
# , label_size = my_label_size)
#
# dev.off()
posC_nca = site_snp_count_bp(plotdf = df3_na
, df_colname = "position"
, xaxis_title = "Number of nsSNPs"
, yaxis_title = "Number of Sites"
, subtitle_colour = "chocolate4"
, subtitle_text = ""
, subtitle_size = 8
, geom_ls = 2.6
, leg_text_size = 10
, axis_text_size = 10
, axis_label_size = 10)
posC_nca
}
#===============================================================
# ------------------------------
# bp site site count: ALL
# <10 Ang ligand
# ------------------------------
posC_all = site_snp_count_bp(plotdf = df3
, df_colname = "position"
, xaxis_title = "Number of nsSNPs"
, yaxis_title = "Number of Sites"
, subtitle_colour = "chocolate4"
, subtitle_text = "All mutations sites"
, subtitle_size = 8
, geom_ls = 2.6
, leg_text_size = 10
, axis_text_size = 10
, axis_label_size = 10)
posC_all
##################################################################
consurfP = stability_count_bp(plotdf = df3
, df_colname = "consurf_outcome"
#, leg_title = "ConSurf"
#, label_categories = labels_consurf
, yaxis_title = "Number of nsSNPs"
, leg_position = "top"
, subtitle_text = "ConSurf"
, bar_fill_values = consurf_colours # from globals
, subtitle_colour= "black"
, sts = 10
, lts = 8
, ats = 8
, als = 8
, ltis = 11
, geom_ls = 2)
consurfP
####################
# Sensitivity count: Mutations
####################
table(df3$sensitivity)
rect_sens=data.frame(mutation_class=c("Resistant","Sensitive")
, tile_colour =c("red","blue")
, numbers = c(table(df3$sensitivity)[[1]], table(df3$sensitivity)[[2]]))
sensP = ggplot(rect_sens, aes(mutation_class, y = 0
, fill = tile_colour
, label = paste0("n=", numbers)
)) +
geom_tile(width = 1, height = 1) + # make square tiles
geom_label(color = "black", size = 1.7,fill = "white", alpha=0.7) + # add white text in the middle
scale_fill_identity(guide = "none") + # color the tiles with the colors in the data frame
coord_fixed() + # make sure tiles are square
#coord_flip()+ scale_x_reverse() +
# theme_void() # remove any axis markings
theme_nothing() # remove any axis markings
sensP
# sensP2 = sensP +
# coord_flip() + scale_x_reverse()
# sensP2
##############################################################
#===================
# Stability
#===================
# duetP
duetP = stability_count_bp(plotdf = df3
, df_colname = "duet_outcome"
, leg_title = "mCSM-DUET"
#, label_categories = labels_duet
, yaxis_title = "Number of nsSNPs"
, leg_position = "none"
, subtitle_text = "mCSM-DUET"
, bar_fill_values = c("#F8766D", "#00BFC4")
, subtitle_colour= "black"
, sts = 10
, lts = 8
, ats = 12
, als = 11
, ltis = 11
, geom_ls = 2.5
)
duetP
# foldx
foldxP = stability_count_bp(plotdf = df3
, df_colname = "foldx_outcome"
#, leg_title = "FoldX"
#, label_categories = labels_foldx
, yaxis_title = ""
, leg_position = "none"
, subtitle_text = "FoldX"
, bar_fill_values = c("#F8766D", "#00BFC4")
, sts = 10
, lts = 8
, ats = 12
, als = 11
, ltis = 11
, geom_ls = 2.5
)
foldxP
# deepddg
deepddgP = stability_count_bp(plotdf = df3
, df_colname = "deepddg_outcome"
#, leg_title = "DeepDDG"
#, label_categories = labels_deepddg
, yaxis_title = ""
, leg_position = "none"
, subtitle_text = "DeepDDG"
, bar_fill_values = c("#F8766D", "#00BFC4")
, sts = 10
, lts = 8
, ats = 12
, als = 11
, ltis = 11
, geom_ls = 2.5
)
deepddgP
# deepddg
dynamut2P = stability_count_bp(plotdf = df3
, df_colname = "ddg_dynamut2_outcome"
#, leg_title = "Dynamut2"
#, label_categories = labels_ddg_dynamut2_outcome
, yaxis_title = ""
, leg_position = "none"
, subtitle_text = "Dynamut2"
, bar_fill_values = c("#F8766D", "#00BFC4")
, sts = 10
, lts = 8
, ats = 12
, als = 11
, ltis = 11
, geom_ls = 2.5
)
dynamut2P
# provean
proveanP = stability_count_bp(plotdf = df3
, df_colname = "provean_outcome"
#, leg_title = "PROVEAN"
#, label_categories = labels_provean
, yaxis_title = "Number of nsSNPs"
, leg_position = "none" # top
, subtitle_text = "PROVEAN"
, bar_fill_values = c("#D01C8B", "#F1B6DA") # light pink and deep
, sts = 10
, lts = 8
, ats = 12
, als = 11
, ltis = 11
, geom_ls = 2.5
)
proveanP
# snap2
snap2P = stability_count_bp(plotdf = df3
, df_colname = "snap2_outcome"
#, leg_title = "SNAP2"
#, label_categories = labels_snap2
, yaxis_title = ""
, leg_position = "none" # top
, subtitle_text = "SNAP2"
, bar_fill_values = c("#D01C8B", "#F1B6DA") # light pink and deep
, sts = 10
, lts = 8
, ats = 12
, als = 11
, ltis = 11
, geom_ls = 2.5)
snap2P
#####################################################################################

198
scripts/plotting/plotting_thesis/basic_barplots_layout.R
View file

@ -1,198 +0,0 @@
# source basic_barplots.R
#============
# Plot labels
#============
tit1 = "Stability outcome"
tit2 = "Affinity outcome"
tit3 = "Conservation outcome"
pt_size = 30
theme_georgia <- function(...) {
theme_gray(base_family = "sans", ...) +
theme(plot.title = element_text(face = "bold"))
}
title_theme <- calc_element("plot.title", theme_georgia())
pt1 = ggdraw() +
draw_label(
tit1,
fontfamily = title_theme$family,
fontface = title_theme$face,
#size = title_theme$size
size = pt_size
)
pt2 = ggdraw() +
draw_label(
tit2,
fontfamily = title_theme$family,
fontface = title_theme$face,
size = pt_size
)
pt3 = ggdraw() +
draw_label(
tit3,
fontfamily = title_theme$family,
fontface = title_theme$face,
size = pt_size
)
# extract common legend
common_legend_outcome = get_legend(mLigP +
guides(color = guide_legend(nrow = 1)) +
theme(legend.position = "top"))
my_label_size = 25
#======================
# Output plot function
#======================
OutPlotBP = function(x){
cowplot::plot_grid(
cowplot::plot_grid(pt1,
common_legend_outcome,
cowplot::plot_grid( duetP, foldxP
, deepddgP, dynamut2P
, nrow = 2
, ncol = 2
, labels = c("A", "B", "C","D")
, label_size = my_label_size
)
, ncol = 1
, rel_heights = c(7, 3, 90)),
cowplot::plot_grid(pt2,
cowplot::plot_grid(mLigP, mmLigP, ppi2P
, nrow = 1
, ncol = 3
, labels = c("E","F", "G")
, label_size = my_label_size
)
, ncol = 1
, rel_heights = c(1, 9)),
cowplot::plot_grid(pt3,
cowplot::plot_grid(consurfP, proveanP, snap2P
, nrow = 1
, ncol = 3
, labels = c("H", "I", "J")
, labels_x = 0.2
, label_size = my_label_size
, rel_widths = c(0.2, 0.2, 0.2)
)
, ncol = 1
, rel_heights = c(0.07, 0.93)
),
nrow = 3,
rel_heights = c(0.58, 0.25, 0.27),
align = "hv"
)
}
#=====================
# OutPlot: svg and png
#======================
#ratio 11.69 by 8.27
w = 8.27*2
h = 11.69*2
#svg
bp_all_CLP = paste0(outdir_images
,tolower(gene)
,"_bp_all_CL.svg")
cat(paste0("plot filename:", bp_all_CLP))
svg(bp_all_CLP, width = w, height = h)
OutPlotBP()
dev.off()
#png
bp_all_CLP_png = paste0(outdir_images
,tolower(gene)
,"_bp_all_CL.png")
cat(paste0("plot filename:", bp_all_CLP_png))
png(bp_all_CLP_png, width = w, height = h, units = "in", res = 300 )
OutPlotBP()
dev.off()
#####################################################################
#####################################################################
# ------------------------------
# bp site site count: ALL
# <10 Ang ligand
# ------------------------------
posC_all = site_snp_count_bp(plotdf = df3
, df_colname = "position"
, xaxis_title = "Number of nsSNPs"
, yaxis_title = "Number of Sites"
, subtitle_size = 20)
# ------------------------------
# bp site site count: mCSM-lig
# < 10 Ang ligand
# ------------------------------
common_bp_title = paste0("Sites <", DistCutOff, angstroms_symbol)
posC_lig = site_snp_count_bp(plotdf = df3_lig
, df_colname = "position"
, xaxis_title = "Number of nsSNPs"
, yaxis_title = "Number of Sites"#+ annotate("text", x = 1.5, y = 2.2, label = "Text No. 1")
, subtitle_text = paste0(common_bp_title, " ligand")
, subtitle_size = 20
, subtitle_colour = subtitle_colour)
# ------------------------------
# bp site site count: ppi2
# < 10 Ang interface
# ------------------------------
posC_ppi2 = site_snp_count_bp(plotdf = df3_ppi2
, df_colname = "position"
, xaxis_title = "Number of nsSNPs"
, yaxis_title = "Number of Sites"
, subtitle_text = paste0(common_bp_title, " interface")
, subtitle_size = 20
, subtitle_colour = subtitle_colour)
# ------------------------------
#FIXME: bp site site count: na
# < 10 Ang TBC
# ------------------------------
# posC_na = site_snp_count_bp(plotdf = df3_na
# , df_colname = "position"
# , xaxis_title = ""
# , yaxis_title = "")
#===========================
# output: SITE SNP count:
# all + affinity
#==========================
my_label_size = 25
pos_count_combined_CLP = paste0(outdir_images
,tolower(gene)
,"_pos_count_PS_AFF.svg")
svg(pos_count_combined_CLP, width = 20, height = 5.5)
print(paste0("plot filename:", pos_count_combined_CLP))
cowplot::plot_grid(posC_all, posC_lig, posC_ppi2
#, posC_na
, nrow = 1
, ncol = 3
, labels = "AUTO"
, label_size = my_label_size)
dev.off()
#===============================================================

35
scripts/plotting/plotting_thesis/basic_barplots_layout_v2.R
View file

@ -10,8 +10,9 @@ mmLigP
posC_lig
ppi2P
posC_ppi2
peP
sensP
peP
#========================
# Common title settings
#=========================
@ -157,9 +158,9 @@ ppi2_affT = ggdraw() +
###########################################################
p2 = cowplot::plot_grid(cowplot::plot_grid(ppi2_affT, common_legend_outcome, nrow=2),
cowplot::plot_grid(ppi2P, posC_ppi2
, nrow = 1
, rel_widths = c(1.2,1.8)
, align = "h"),
, nrow = 1
, rel_widths = c(1.2,1.8)
, align = "h"),
nrow = 2,
rel_heights = c(1,8)
)
@ -184,7 +185,7 @@ p3 = cowplot::plot_grid(cowplot::plot_grid(peT_allT, nrow = 2
align = "v",
axis = "lr",
rel_heights = c(1,8)
),
),
rel_heights = c(1,18),
nrow = 2,axis = "lr")
p3
@ -207,7 +208,7 @@ cowplot::plot_grid(p1, p2, p3
, label_size = 12
, rel_widths = c(3,2,2)
#, rel_heights = c(1)
)
)
dev.off()
##################################################
@ -234,8 +235,8 @@ h = 3
# dev.off()
conCLP = paste0(outdir_images
,tolower(gene)
,"_consurf_BP.png")
,tolower(gene)
,"_consurf_BP.png")
print(paste0("plot filename:", conCLP))
png(conCLP, units = "in", width = w, height = h, res = 300 )
@ -243,15 +244,27 @@ consurfP
dev.off()
#================================
# Sensitivity numbers: geom_tile
# Sensitivity mutation numbers: geom_tile
#================================
sensCLP = paste0(outdir_images
,tolower(gene)
,"_sensN_tile.png")
,tolower(gene)
,"_sensN_tile.png")
print(paste0("plot filename:", sensCLP))
png(sensCLP, units = "in", width = 1, height = 1, res = 300 )
sensP
dev.off()
#================================
# Sensitivity SITE numbers: geom_tile
#================================
sens_siteCLP = paste0(outdir_images
,tolower(gene)
,"_sens_siteC_tile.png")
print(paste0("plot filename:", sens_siteCLP))
png(sens_siteCLP, units = "in", width = 1, height = 1, res = 300 )
sens_siteP
dev.off()
###########################################################

174
scripts/plotting/plotting_thesis/basic_barplots2.R → scripts/plotting/plotting_thesis/basic_barplots_v2.R
View file

@ -25,16 +25,21 @@
#=============
# Data: Input
#==============
#source("~/git/LSHTM_analysis/config/alr.R")
source("~/git/LSHTM_analysis/config/embb.R")
#source("~/git/LSHTM_analysis/config/katg.R")
#source("~/git/LSHTM_analysis/config/gid.R")
#source("~/git/LSHTM_analysis/config/pnca.R")
#source("~/git/LSHTM_analysis/config/embb.R")
#source("~/git/LSHTM_analysis/config/gid.R")
source("~/git/LSHTM_analysis/config/alr.R")
#source("~/git/LSHTM_analysis/config/katg.R")
#source("~/git/LSHTM_analysis/config/rpob.R")
source("~/git/LSHTM_analysis/scripts/plotting/get_plotting_dfs.R")
source("~/git/LSHTM_analysis/scripts/plotting/plotting_colnames.R")
#source("~/git/LSHTM_analysis/scripts/plotting/plotting_colnames.R") sourced by above
# sanity check
cat("\nSourced plotting cols as well:", length(plotting_cols))
####################################################
class(merged_df3)
merged_df3 = as.data.frame(merged_df3)
@ -52,7 +57,7 @@ merged_df3 = merged_df3[, !colnames(merged_df3)%in%c("pos_count")]
head(merged_df3$pos_count)
df3 = merged_df3[, colnames(merged_df3)%in%plotting_cols]
"nca_distance"%in%colnames(df3)
#=======
# output
@ -129,25 +134,54 @@ mmLigP
# barplot for ppi2 affinity
# <10 Ang of interface
#------------------------------
ppi2P = stability_count_bp(plotdf = df3_ppi2
, df_colname = "mcsm_ppi2_outcome"
#, leg_title = "mCSM-ppi2"
#, label_categories = labels_ppi2
#, bp_plot_title = paste(common_bp_title, "PP-interface")
if (tolower(gene)%in%geneL_ppi2){
ppi2P = stability_count_bp(plotdf = df3_ppi2
, df_colname = "mcsm_ppi2_outcome"
#, leg_title = "mCSM-ppi2"
#, label_categories = labels_ppi2
#, bp_plot_title = paste(common_bp_title, "PP-interface")
, yaxis_title = "Number of nsSNPs"
, leg_position = "none"
, subtitle_text = "mCSM-ppi2"
, bar_fill_values = c("#F8766D", "#00BFC4")
, subtitle_colour= "black"
, sts = 10
, lts = 8
, ats = 12
, als = 11
, ltis = 11
, geom_ls = 2.5
)
ppi2P
}
#----------------------------
# barplot for ppi2 affinity
# <10 Ang of interface
#------------------------------
if (tolower(gene)%in%geneL_na){
nca_distP = stability_count_bp(plotdf = df3_na
, df_colname = "mcsm_na_outcome"
#, leg_title = "mCSM-NA"
#, label_categories =
#, bp_plot_title = paste(common_bp_title, "Dist to NA")
, yaxis_title = "Number of nsSNPs"
, leg_position = "none"
, subtitle_text = "mCSM-NA"
, bar_fill_values = c("#F8766D", "#00BFC4")
, subtitle_colour= "black"
, sts = 10
, lts = 8
, ats = 12
, als = 11
, ltis = 11
, geom_ls = 2.5
)
nca_distP
}
, yaxis_title = "Number of nsSNPs"
, leg_position = "none"
, subtitle_text = "mCSM-ppi2"
, bar_fill_values = c("#F8766D", "#00BFC4")
, subtitle_colour= "black"
, sts = 10
, lts = 8
, ats = 12
, als = 11
, ltis = 11
, geom_ls = 2.5
)
ppi2P
#####################################################################
# ------------------------------
@ -173,19 +207,43 @@ posC_lig
# bp site site count: ppi2
# < 10 Ang interface
# ------------------------------
if (tolower(gene)%in%geneL_ppi2){
posC_ppi2 = site_snp_count_bp(plotdf = df3_ppi2
, df_colname = "position"
, xaxis_title = "Number of nsSNPs"
, yaxis_title = "Number of Sites"
, subtitle_colour = "chocolate4"
, subtitle_text = ""
, subtitle_size = 8
, geom_ls = 2.6
, leg_text_size = 10
, axis_text_size = 10
, axis_label_size = 10)
posC_ppi2
}
# ------------------------------
# bp site site count: NCA dist
# < 10 Ang nca
# ------------------------------
if (tolower(gene)%in%geneL_na){
posC_nca = site_snp_count_bp(plotdf = df3_na
, df_colname = "position"
, xaxis_title = "Number of nsSNPs"
, yaxis_title = "Number of Sites"
, subtitle_colour = "chocolate4"
, subtitle_text = ""
, subtitle_size = 8
, geom_ls = 2.6
, leg_text_size = 10
, axis_text_size = 10
, axis_label_size = 10)
posC_nca
}
posC_ppi2 = site_snp_count_bp(plotdf = df3_ppi2
, df_colname = "position"
, xaxis_title = "Number of nsSNPs"
, yaxis_title = "Number of Sites"
, subtitle_colour = "chocolate4"
, subtitle_text = ""
, subtitle_size = 8
, geom_ls = 2.6
, leg_text_size = 10
, axis_text_size = 10
, axis_label_size = 10)
posC_ppi2
#===============================================================
# PE count
rects <- data.frame(x = 1:6,
@ -246,7 +304,7 @@ posC_all = site_snp_count_bp(plotdf = df3
, leg_text_size = 10
, axis_text_size = 10
, axis_label_size = 10)
posC_all
##################################################################
#------------------------------
@ -290,10 +348,8 @@ consurfP = stability_count_bp(plotdf = df3
consurfP
####################
# Sensitivity count
# Sensitivity count: Mutations
####################
table(df3$sensitivity)
@ -320,6 +376,36 @@ sensP
# sensP2 = sensP +
# coord_flip() + scale_x_reverse()
# sensP2
#===============================
# Sensitivity count: Site
#==============================
table(df3$sensitivity)
#--------
# embb
#--------
#rsc = 54
#ccc = 46
#ssc = 470
rect_rs_siteC =data.frame(mutation_class=c("A_Resistant sites"
, "B_Common sites"
, "C_Sensitive sites"),
tile_colour =c("red",
"purple",
"blue"),
numbers = c(rsc, ccc, ssc),
order = c(1, 2, 3))
rect_rs_siteC$labels = paste0(rect_rs_siteC$mutation_class, "\nn=", rect_rs_siteC$ numbers)
sens_siteP = ggplot(rect_rs_siteC, aes(mutation_class, y = 0,
fill = tile_colour,
label = paste0("n=", numbers))) +
geom_tile(width = 1, height = 1) +
geom_label(color = "black", size = 1.7,fill = "white", alpha=0.7) +
theme_nothing()
sens_siteP
##############################################################
#===================
@ -360,7 +446,7 @@ foldxP = stability_count_bp(plotdf = df3
, ltis = 11
, geom_ls = 2.5
)
foldxP
# deepddg
deepddgP = stability_count_bp(plotdf = df3
@ -378,7 +464,7 @@ deepddgP = stability_count_bp(plotdf = df3
, ltis = 11
, geom_ls = 2.5
)
deepddgP
# deepddg
dynamut2P = stability_count_bp(plotdf = df3
@ -398,7 +484,6 @@ dynamut2P = stability_count_bp(plotdf = df3
)
dynamut2P
# provean
proveanP = stability_count_bp(plotdf = df3
, df_colname = "provean_outcome"
@ -415,6 +500,7 @@ proveanP = stability_count_bp(plotdf = df3
, ltis = 11
, geom_ls = 2.5
)
proveanP
# snap2
snap2P = stability_count_bp(plotdf = df3
@ -431,7 +517,7 @@ snap2P = stability_count_bp(plotdf = df3
, als = 11
, ltis = 11
, geom_ls = 2.5)
snap2P
##############################################################

2
scripts/plotting/plotting_thesis/corr_plots_thesis.R
View file

@ -263,7 +263,7 @@ if (tolower(gene)%in%geneL_ppi2){
# NA affinity
#================
if (tolower(gene)%in%geneL_na){
corr_df_na = corr_df_na[corr_df_na["NA-Dist"]<DistCutOff,]
corr_df_na = corr_df_na[corr_df_na["NCA-Dist"]<DistCutOff,]
corr_na_colnames = c(static_cols
, "mCSM-NA"

8
scripts/plotting/plotting_thesis/dm_om_plots.R
View file

@ -63,7 +63,7 @@ distanceP
# check
wilcox.test(wf_dist_genP$`PPI Dist(Å)`[wf_dist_genP$mutation_info_labels=="R"]
, wf_dist_genP$`PPI Dist(Å)`[wf_dist_genP$mutation_info_labels=="S"], paired = FALSE)
, wf_dist_genP$`PPI Dist(Å)`[wf_dist_genP$mutation_info_labels=="S"], paired = FALSE)
wilcox.test(wf_dist_genP$`Lig Dist(Å)`[wf_dist_genP$mutation_info_labels=="R"]
, wf_dist_genP$`Lig Dist(Å)`[wf_dist_genP$mutation_info_labels=="S"], paired = FALSE)
@ -83,8 +83,8 @@ dist_data_lig$param_type = factor(dist_data_lig$param_type)
table(dist_data_lig$param_type)
levels(dist_data_lig$param_type)
distanceP_lig = lf_bp2(dist_data_lig
#, p_title = ""
, violin_quantiles = c(0.5), monochrome = F)
#, p_title = ""
, violin_quantiles = c(0.5), monochrome = F)
distanceP_lig
@ -120,7 +120,7 @@ if (tolower(gene)%in%geneL_na){
, violin_quantiles = c(0.5), monochrome = F)
distanceP_na
}
}
#==============
# Plot:DUET
#==============

9
scripts/plotting/plotting_thesis/dm_om_plots_layout.R
View file

@ -92,8 +92,9 @@ OutPlot_dm_om = function(x){
NULL,
cowplot::plot_grid(pt3,
cowplot::plot_grid( #distanceP
distanceP_lig, distanceP_ppi2
#, distanceP_na
distanceP_lig
#, distanceP_ppi2
, distanceP_na
, nrow = 1
, labels = c("F", "G")
, label_size = my_label_size)
@ -118,8 +119,8 @@ OutPlot_dm_om = function(x){
),NULL,
cowplot::plot_grid(pt5,
cowplot::plot_grid(mcsmligP, mcsmlig2P
, mcsmppi2P
#, mcsmnaP
#, mcsmppi2P
, mcsmnaP
, nrow = 1
, labels = c("K", "L", "M")
, label_size = my_label_size)

14
scripts/plotting/plotting_thesis/gg_pairs_all.R
View file

@ -52,13 +52,15 @@ corr_plotdf = corr_data_extract(merged_df3
, extract_scaled_cols = F)
aff_dist_cols = colnames(corr_plotdf)[grep("Dist", colnames(corr_plotdf))]
static_cols = c("Log10(MAF)")
#, "Log10(OR)")
static_cols = c("Log10(MAF)"
, "Log10(OR)"
)
############################################################
#=============================================
# Creating masked df for affinity data
#=============================================
corr_affinity_df = corr_plotdf
#----------------------
# Mask affinity columns
#-----------------------
@ -70,7 +72,7 @@ if (tolower(gene)%in%geneL_ppi2){
}
# if (tolower(gene)%in%geneL_na){
# corr_affinity_df[corr_affinity_df["NA-Dist"]>DistCutOff,"mCSM-NA"]=0
# corr_affinity_df[corr_affinity_df["NCA-Dist"]>DistCutOff,"mCSM-NA"]=0
# }
# count 0
@ -89,10 +91,12 @@ corr_ps_colnames = c(static_cols
, "Dynamut2"
, aff_dist_cols
, "dst_mode")
corr_df_ps = corr_plotdf[, corr_ps_colnames]
# Plot #1
plot_corr_df_ps = my_gg_pairs(corr_df_ps, plot_title="Stability estimates")
##########################################################
#================
# Conservation
@ -101,7 +105,7 @@ corr_conservation_cols = c( static_cols
, "ConSurf"
, "SNAP2"
, "PROVEAN"
, aff_dist_cols
#, aff_dist_cols
, "dst_mode"
)
@ -166,6 +170,6 @@ png(paste0(outdir_images
,"_CorrC.png"), height =7, width=7, unit="in",res=300)
cowplot::plot_grid(ggmatrix_gtable(plot_corr_df_aff),
labels = "C",
labels = "C",
label_size = 12)
dev.off()

4
scripts/plotting/plotting_thesis/linage_bp_dist.R
View file

@ -61,7 +61,7 @@ lin_countP = lin_count_bp(lf_data = lineage_dfL[['lin_lf']]
, y_scale_percent = FALSE
, y_label = c("Count")
)
lin_countP
#===============================
# lineage SNP diversity count
#===============================
@ -88,7 +88,7 @@ lin_diversityP = lin_count_bp_diversity(lf_data = lineage_dfL[['lin_wf']]
, subtitle_text = NULL
, sts = 20
, subtitle_colour = "#350E20FF")
lin_diversityP
#=============================================
# Output plots: Lineage count and Diversity
#=============================================