renamed 2 to _v2
This commit is contained in:
parent
c9d7ea9fad
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18 changed files with 761 additions and 976 deletions
45
config/alr.R
45
config/alr.R
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@ -27,6 +27,7 @@ aa_plip_dcs_other = aa_plip_dcs[!aa_plip_dcs%in%aa_plip_dcs_hbond]
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c2 = length(aa_plip_dcs_other) == length(aa_plip_dcs) - length(aa_plip_dcs_hbond)
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#==========
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# Arpeggio
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#===========
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@ -40,6 +41,18 @@ aa_arpeg_dcs_other = aa_arpeg_dcs[!aa_arpeg_dcs%in%c(aa_ligplus_dcs_other
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c3 = length(aa_arpeg_dcs_other) == length(aa_arpeg_dcs) - ( length(aa_ligplus_dcs_other) + length(aa_plip_dcs_other) )
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#######################################################################
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#NEW AFTER ADDING PLP to structure! huh
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# ADDED: 18 Aug 2022
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# PLIP server for co factor PLP (CONFUSING!)
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#and 2019 lit:lys42, M319, and Y364 : OFFSET is 24
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#K42: K66, Y271:Y295, M319:M343, W89: W113, W203: W227, H209:H233, Q321:Q345
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aa_pos_paper = sort(unique(c(66,70,112,113,164,196,227,233,237,252,254,255,295,342,343,344,345,388)))
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plp_pos_paper = sort(unique(c(66, 70, 112, 196, 227, 237, 252, 254, 255, 388)))
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#active_aa_pos = sort(unique(c(aa_pos_paper, active_aa_pos)))
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aa_pos_plp = sort(unique(c(plp_pos_paper, 66, 70, 112, 237, 252, 254, 255, 196)))
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#######################################################################
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#===============
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@ -47,7 +60,9 @@ c3 = length(aa_arpeg_dcs_other) == length(aa_arpeg_dcs) - ( length(aa_ligplus_dc
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#===============
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active_aa_pos = sort(unique(c(aa_ligplus_dcs
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, aa_plip_dcs
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, aa_arpeg_dcs)))
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, aa_arpeg_dcs
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, aa_pos_paper
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, aa_pos_plp)))
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#=================
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# Drug binding aa
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#=================
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@ -56,6 +71,12 @@ aa_pos_dcs = sort(unique(c(aa_ligplus_dcs
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, aa_arpeg_dcs)))
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aa_pos_drug = aa_pos_dcs
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#===============
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# Co-factor: PLP aa
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#===============
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aa_pos_plp = aa_pos_plp
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#===============
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# Hbond aa
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#===============
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@ -102,11 +123,25 @@ if ( all(c1, c2, c3) ) {
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, "\n\nNO other co-factors or ligands present\n")
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}
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#######################################################################
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######################################################################
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#NEW
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# PLIP server for co factor PLP (CONFUSING!)
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#and 2019 lit:lys42, M319, and Y364 : OFFSET is 24
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#K42: K66, Y271:Y295, M319:M343, W89: W113, W203: W227, H209:H233, Q321:Q345
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aa_pos_paper = sort(unique(c(66,70,112,113,164,196,227,233,237,252,254,255,295,342,343,344,345,388)))
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plp_pos_paper = sort(unique(c(66, 70, 112, 196, 227, 237, 252, 254, 255, 388)))
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active_aa_pos = sort(unique(c(aa_pos_paper, active_aa_pos)))
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aa_pos_plp = sort(unique(c(plp_pos_paper, 66, 70, 112, 237, 252, 254, 255, 196)))
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# add two key residues
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#aa_pos_drug = sort(unique(c(319, 364, aa_pos_drug)))
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#active_aa_pos = sort(unique(c(319, 364, active_aa_pos, aa_pos_plp)))
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# FIXME: these should be populated!
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aa_pos_lig1 = NULL
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aa_pos_lig1 = aa_pos_plp
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aa_pos_lig2 = NULL
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aa_pos_lig3 = NULL
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tile_map=data.frame(tile=c("ALR","DPA","CDL","Ca"),
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tile_colour=c("green","darkslategrey","navyblue","purple"))
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tile_map=data.frame(tile=c("ALR","PLP"),
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tile_colour=c("green","darkslategrey"))
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@ -120,3 +120,4 @@ aa_pos_lig3 = aa_pos_ca #purple
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tile_map=data.frame(tile=c("EMB","DPA","CDL","Ca"),
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tile_colour=c("green","darkslategrey","navyblue","purple"))
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drug_main_res = c(299 , 302, 303 , 306 , 327 , 592 , 594, 988, 1028)
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34
config/gid.R
34
config/gid.R
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@ -53,19 +53,19 @@ aa_arpeg_amp = c(123, 125, 213)
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# Active site
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#=============
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active_aa_pos = sort(unique(c(
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#rna_bind_aa_pos
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#, binding_aa_pos
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aa_ligplus_sry
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, aa_ligplus_sam
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, aa_ligplus_amp
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, aa_ligplus_rna
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, aa_plip_sry
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, aa_plip_sam
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, aa_plip_amp
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, aa_plip_rna
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, aa_arpeg_sry
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, aa_arpeg_sam
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, aa_arpeg_amp
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#rna_bind_aa_pos
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#, binding_aa_pos
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aa_ligplus_sry
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, aa_ligplus_sam
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, aa_ligplus_amp
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, aa_ligplus_rna
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, aa_plip_sry
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, aa_plip_sam
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, aa_plip_amp
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, aa_plip_rna
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, aa_arpeg_sry
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, aa_arpeg_sam
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, aa_arpeg_amp
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)))
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##############################################################
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@ -129,9 +129,11 @@ cat("\n==================================================="
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##############################################################
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# var for position customisation for plots
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aa_pos_lig1 = aa_pos_rna
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aa_pos_lig2 = aa_pos_sam
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aa_pos_drug
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aa_pos_lig1 = aa_pos_sam
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aa_pos_lig2 = aa_pos_rna
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aa_pos_lig3 = aa_pos_amp
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tile_map=data.frame(tile=c("GID","DPA","CDL","Ca"),
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tile_map=data.frame(tile=c("SRY","SAM","RNA","AMP"),
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tile_colour=c("green","darkslategrey","navyblue","purple"))
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@ -128,7 +128,7 @@ if(!require("stats4")) {
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}
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if(!require("data.table")) {
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install.packages("data.table")
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install.packages("data.table")
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library(data.table)
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}
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@ -247,7 +247,7 @@ consurf_colours = c(
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, "7" = rgb(0.98,0.78,0.86)
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, "8" = rgb(0.94,0.49,0.67)
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, "9" = rgb(0.63,0.16,0.37)
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)
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)
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##################################################
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@ -16,6 +16,7 @@ corr_data_extract <- function(df
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if ( missing(colnames_to_extract) || missing(colnames_display_key) ){
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# log10maf
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df$maf2 = log10(df$maf) # can't see otherwise
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sum(is.na(df$maf2))
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@ -70,7 +71,7 @@ corr_data_extract <- function(df
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if (tolower(gene)%in%geneL_na){
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colnames_to_extract = c(common_colnames,"mcsm_na_affinity", naDist_colname)
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display_colnames = c(display_common_colnames, "mCSM-NA", "NA-Dist")
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display_colnames = c(display_common_colnames, "mCSM-NA", "NCA-Dist")
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corr_df = df[,colnames_to_extract]
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colnames(corr_df) = display_colnames
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}
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@ -222,12 +222,12 @@ dm_om_wf_lf_data <- function(df
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}
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if (tolower(gene)%in%geneL_na){
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colnames_to_extract = c(common_colnames,"mcsm_na_affinity" , "mcsm_na_scaled" , "mcsm_na_outcome" , naDist_colname)
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colnames_to_extract = c(common_colnames ,"mcsm_na_affinity" , "mcsm_na_scaled" , "mcsm_na_outcome" , naDist_colname)
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display_colnames = c(display_common_colnames, "mcsm_na_affinity" , mcsm_na_dn , "mcsm_na_outcome" , na_dist_dn)
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comb_df_sl = df[, colnames_to_extract]
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# Rename cols: display names
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colnames(comb_df) = display_colnames
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colnames(comb_df_sl) = display_colnames
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# Affinity filtered data: mcsm-na --> naDist_colname
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comb_df_sl_na = comb_df_sl[comb_df_sl[[na_dist_dn]]<DistCutOff,]
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@ -41,7 +41,7 @@ LigDist_cutoff <<- 10
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DistCutOff <<- 10
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ppi2Dist_colname <<- "interface_dist"
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naDist_colname <<- "TBC"
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naDist_colname <<- "nca_distance" # added it
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#==================
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# Angstroms symbol
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@ -6,7 +6,7 @@
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# working dir and loading libraries
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getwd()
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source("~/git/LSHTM_analysis/scripts/Header_TT.R")
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source("~/git/LSHTM_analysis/scripts/plotting/plotting_colnames.R")
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# cmd args passed
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# in from other scripts
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# to call this
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@ -43,7 +43,7 @@ import_dirs(drug, gene)
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# call: plotting_data()
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#---------------------------
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if (!exists("infile_params") && exists("gene")){
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#if (!is.character(infile_params) && exists("gene")){ # when running as cmd
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#if (!is.character(infile_params) && exists("gene")){ # when running as cmd
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in_filename_params = paste0(tolower(gene), "_all_params.csv")
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infile_params = paste0(outdir, "/", in_filename_params)
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cat("\nInput file for mcsm comb data not specified, assuming filename: ", infile_params, "\n")
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@ -70,7 +70,7 @@ cat("\nLigand distance colname:", LigDist_colname
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# call: combining_dfs_plotting()
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#--------------------------------
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if (!exists("infile_metadata") && exists("gene")){
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#if (!is.character(infile_metadata) && exists("gene")){ # when running as cmd
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#if (!is.character(infile_metadata) && exists("gene")){ # when running as cmd
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in_filename_metadata = paste0(tolower(gene), "_metadata.csv") # part combined for gid
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infile_metadata = paste0(outdir, "/", in_filename_metadata)
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cat("\nInput file for gene metadata not specified, assuming filename: ", infile_metadata, "\n")
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@ -122,52 +122,52 @@ merged_df3 = all_plot_dfs[[2]]
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# ####################################################################
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#
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# #source(paste0(plot_script_path, "dm_om_data.R")) # calling the function directly instead
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# geneL_normal = c("pnca")
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# geneL_na = c("gid", "rpob")
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# geneL_ppi2 = c("alr", "embb", "katg", "rpob")
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#
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# all_dm_om_df = dm_om_wf_lf_data(df = merged_df3, gene = gene)
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#
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# wf_duet = all_dm_om_df[['wf_duet']]
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# lf_duet = all_dm_om_df[['lf_duet']]
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#
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# wf_mcsm_lig = all_dm_om_df[['wf_mcsm_lig']]
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# lf_mcsm_lig = all_dm_om_df[['lf_mcsm_lig']]
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#
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# wf_foldx = all_dm_om_df[['wf_foldx']]
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# lf_foldx = all_dm_om_df[['lf_foldx']]
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#
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# wf_deepddg = all_dm_om_df[['wf_deepddg']]
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# lf_deepddg = all_dm_om_df[['lf_deepddg']]
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#
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# wf_dynamut2 = all_dm_om_df[['wf_dynamut2']]
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# lf_dynamut2 = all_dm_om_df[['lf_dynamut2']]
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#
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# wf_consurf = all_dm_om_df[['wf_consurf']]
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# lf_consurf = all_dm_om_df[['lf_consurf']]
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#
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# wf_snap2 = all_dm_om_df[['wf_snap2']]
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# lf_snap2 = all_dm_om_df[['lf_snap2']]
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#
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# wf_provean = all_dm_om_df[['wf_provean']]
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# lf_provean = all_dm_om_df[['lf_provean']]
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#
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# # NEW
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# wf_dist_gen = all_dm_om_df[['wf_dist_gen']]
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# lf_dist_gen = all_dm_om_df[['lf_dist_gen']]
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#
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# if (tolower(gene)%in%geneL_na){
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# wf_mcsm_na = all_dm_om_df[['wf_mcsm_na']]
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# lf_mcsm_na = all_dm_om_df[['lf_mcsm_na']]
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# }
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#
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# if (tolower(gene)%in%geneL_ppi2){
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# wf_mcsm_ppi2 = all_dm_om_df[['wf_mcsm_ppi2']]
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# lf_mcsm_ppi2 = all_dm_om_df[['lf_mcsm_ppi2']]
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# }
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#
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# s2 = c("\nSuccessfully sourced other_plots_data.R")
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# cat(s2)
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geneL_normal = c("pnca")
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geneL_na = c("gid", "rpob")
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geneL_ppi2 = c("alr", "embb", "katg", "rpob")
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all_dm_om_df = dm_om_wf_lf_data(df = merged_df3, gene = gene)
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wf_duet = all_dm_om_df[['wf_duet']]
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lf_duet = all_dm_om_df[['lf_duet']]
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wf_mcsm_lig = all_dm_om_df[['wf_mcsm_lig']]
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lf_mcsm_lig = all_dm_om_df[['lf_mcsm_lig']]
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wf_foldx = all_dm_om_df[['wf_foldx']]
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lf_foldx = all_dm_om_df[['lf_foldx']]
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wf_deepddg = all_dm_om_df[['wf_deepddg']]
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lf_deepddg = all_dm_om_df[['lf_deepddg']]
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wf_dynamut2 = all_dm_om_df[['wf_dynamut2']]
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lf_dynamut2 = all_dm_om_df[['lf_dynamut2']]
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wf_consurf = all_dm_om_df[['wf_consurf']]
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lf_consurf = all_dm_om_df[['lf_consurf']]
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wf_snap2 = all_dm_om_df[['wf_snap2']]
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lf_snap2 = all_dm_om_df[['lf_snap2']]
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wf_provean = all_dm_om_df[['wf_provean']]
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lf_provean = all_dm_om_df[['lf_provean']]
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# NEW
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wf_dist_gen = all_dm_om_df[['wf_dist_gen']]
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lf_dist_gen = all_dm_om_df[['lf_dist_gen']]
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if (tolower(gene)%in%geneL_na){
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wf_mcsm_na = all_dm_om_df[['wf_mcsm_na']]
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lf_mcsm_na = all_dm_om_df[['lf_mcsm_na']]
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}
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if (tolower(gene)%in%geneL_ppi2){
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wf_mcsm_ppi2 = all_dm_om_df[['wf_mcsm_ppi2']]
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lf_mcsm_ppi2 = all_dm_om_df[['lf_mcsm_ppi2']]
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}
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s2 = c("\nSuccessfully sourced other_plots_data.R")
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cat(s2)
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#
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# ####################################################################
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# # Data for Lineage barplots: WF and LF dfs
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@ -175,48 +175,48 @@ merged_df3 = all_plot_dfs[[2]]
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# # location: scripts/functions/lineage_plot_data.R
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# ####################################################################
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#
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# #source(paste0(plot_script_path, "lineage_data.R"))
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source(paste0(plot_script_path, "lineage_data.R"))
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# # converted to a function. Moved lineage_data.R to redundant/
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# lineage_dfL = lineage_plot_data(merged_df2
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# , lineage_column_name = "lineage"
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# , remove_empty_lineage = F
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# , lineage_label_col_name = "lineage_labels"
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# , id_colname = "id"
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# , snp_colname = "mutationinformation"
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# )
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#
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# lin_wf = lineage_dfL[['lin_wf']]
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# lin_lf = lineage_dfL[['lin_lf']]
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#
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# s3 = c("\nSuccessfully sourced lineage_data.R")
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# cat(s3)
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#
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# ####################################################################
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# # Data for corr plots:
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# # My function: corr_data_extract()
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# # location: scripts/functions/corr_plot_data.R
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# ####################################################################
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# # make sure the above script works because merged_df2_combined is needed
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# merged_df3 = as.data.frame(merged_df3)
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#
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# corr_df_m3_f = corr_data_extract(merged_df3
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lineage_dfL = lineage_plot_data(merged_df2
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, lineage_column_name = "lineage"
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, remove_empty_lineage = F
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, lineage_label_col_name = "lineage_labels"
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, id_colname = "id"
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, snp_colname = "mutationinformation"
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)
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lin_wf = lineage_dfL[['lin_wf']]
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lin_lf = lineage_dfL[['lin_lf']]
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s3 = c("\nSuccessfully sourced lineage_data.R")
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cat(s3)
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####################################################################
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# Data for corr plots:
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# My function: corr_data_extract()
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# location: scripts/functions/corr_plot_data.R
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####################################################################
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# make sure the above script works because merged_df2_combined is needed
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merged_df3 = as.data.frame(merged_df3)
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corr_df_m3_f = corr_data_extract(merged_df3
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, 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##################################################"
|
||||
|
|
@ -229,17 +229,17 @@ merged_df3 = all_plot_dfs[[2]]
|
|||
# , "\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)
|
||||
|
|
|
|||
|
|
@ -133,7 +133,7 @@ colsNames_to_output_lig = c("Mutation"
|
|||
, "Odds Ratio"
|
||||
, "P-value"
|
||||
, "Adjusted P-value"
|
||||
, "P-value significance")
|
||||
, "Adjusted P-value significance")
|
||||
|
||||
colnames(Out_df_ligS) = colsNames_to_output_lig
|
||||
head(Out_df_ligS)
|
||||
|
|
@ -179,12 +179,13 @@ Out_df_ppi2S = Out_df_ppi2[order(-Out_df_ppi2$or_mychisq, Out_df_ppi2$maf_percen
|
|||
colsNames_to_output_ppi2 = c("Mutation"
|
||||
, "position"
|
||||
, paste0("PPI2-Dist (", angstroms_symbol, ")")
|
||||
, paste0("mCSM-PPI2 (", delta_symbol, ")")
|
||||
, paste0("mCSM-PPI2 (", delta_symbol,delta_symbol,"G)")
|
||||
, "mCSM-PPI2 outcome"
|
||||
, paste0("MAF ","(%)")
|
||||
, "Odds Ratio"
|
||||
, "P-value"
|
||||
, "Adjusted P-value"
|
||||
, "P-value significance")
|
||||
, "Adjusted P-value significance")
|
||||
|
||||
colnames(Out_df_ppi2S) = colsNames_to_output_ppi2
|
||||
Out_df_ppi2S
|
||||
|
|
|
|||
729
scripts/plotting/plotting_thesis/basic_barplots.R
Executable file → Normal file
729
scripts/plotting/plotting_thesis/basic_barplots.R
Executable file → Normal file
|
|
@ -25,23 +25,28 @@
|
|||
#=============
|
||||
# Data: Input
|
||||
#==============
|
||||
#source("~/git/LSHTM_analysis/config/alr.R")
|
||||
source("~/git/LSHTM_analysis/config/embb.R")
|
||||
#source("~/git/LSHTM_analysis/config/katg.R")
|
||||
#source("~/git/LSHTM_analysis/config/gid.R")
|
||||
#source("~/git/LSHTM_analysis/config/pnca.R")
|
||||
#source("~/git/LSHTM_analysis/config/embb.R")
|
||||
#source("~/git/LSHTM_analysis/config/gid.R")
|
||||
|
||||
#source("~/git/LSHTM_analysis/config/alr.R")
|
||||
source("~/git/LSHTM_analysis/config/katg.R")
|
||||
#source("~/git/LSHTM_analysis/config/rpob.R")
|
||||
|
||||
source("~/git/LSHTM_analysis/scripts/plotting/get_plotting_dfs.R")
|
||||
source("~/git/LSHTM_analysis/scripts/plotting/plotting_colnames.R")
|
||||
#source("~/git/LSHTM_analysis/scripts/plotting/plotting_colnames.R") sourced by above
|
||||
# sanity check
|
||||
|
||||
cat("\nSourced plotting cols as well:", length(plotting_cols))
|
||||
|
||||
####################################################
|
||||
class(merged_df3)
|
||||
merged_df3 = as.data.frame(merged_df3)
|
||||
|
||||
class(df3)
|
||||
class(merged_df3)
|
||||
head(merged_df3$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
|
||||
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
|
||||
, yaxis_title = ""
|
||||
#, bp_plot_title = paste(common_bp_title, "PP-interface")
|
||||
|
||||
, yaxis_title = "Number of nsSNPs"
|
||||
, 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")
|
||||
, 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){
|
||||
|
||||
# # 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()
|
||||
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
|
||||
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
|
||||
# ------------------------------
|
||||
# posC_na = site_snp_count_bp(plotdf = df3_na
|
||||
# , df_colname = "position"
|
||||
# , xaxis_title = ""
|
||||
# , yaxis_title = "")
|
||||
, 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){
|
||||
|
||||
#===========================
|
||||
# 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
|
||||
|
||||
#####################################################################################
|
||||
|
|
|
|||
|
|
@ -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()
|
||||
|
||||
|
||||
#===============================================================
|
||||
|
|
@ -10,8 +10,9 @@ mmLigP
|
|||
posC_lig
|
||||
ppi2P
|
||||
posC_ppi2
|
||||
peP
|
||||
sensP
|
||||
peP
|
||||
|
||||
#========================
|
||||
# Common title settings
|
||||
#=========================
|
||||
|
|
@ -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()
|
||||
##################################################
|
||||
|
|
@ -243,7 +244,7 @@ consurfP
|
|||
|
||||
dev.off()
|
||||
#================================
|
||||
# Sensitivity numbers: geom_tile
|
||||
# Sensitivity mutation numbers: geom_tile
|
||||
#================================
|
||||
sensCLP = paste0(outdir_images
|
||||
,tolower(gene)
|
||||
|
|
@ -253,5 +254,17 @@ 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()
|
||||
|
||||
###########################################################
|
||||
|
||||
|
|
|
|||
|
|
@ -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
|
||||
|
|
@ -129,7 +134,8 @@ mmLigP
|
|||
# barplot for ppi2 affinity
|
||||
# <10 Ang of interface
|
||||
#------------------------------
|
||||
ppi2P = stability_count_bp(plotdf = df3_ppi2
|
||||
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
|
||||
|
|
@ -147,7 +153,35 @@ ppi2P = stability_count_bp(plotdf = df3_ppi2
|
|||
, ltis = 11
|
||||
, geom_ls = 2.5
|
||||
)
|
||||
ppi2P
|
||||
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
|
||||
}
|
||||
|
||||
#####################################################################
|
||||
|
||||
# ------------------------------
|
||||
|
|
@ -173,8 +207,9 @@ 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
|
||||
posC_ppi2 = site_snp_count_bp(plotdf = df3_ppi2
|
||||
, df_colname = "position"
|
||||
, xaxis_title = "Number of nsSNPs"
|
||||
, yaxis_title = "Number of Sites"
|
||||
|
|
@ -185,7 +220,30 @@ posC_ppi2 = site_snp_count_bp(plotdf = df3_ppi2
|
|||
, leg_text_size = 10
|
||||
, axis_text_size = 10
|
||||
, axis_label_size = 10)
|
||||
posC_ppi2
|
||||
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
|
||||
}
|
||||
|
||||
|
||||
#===============================================================
|
||||
# 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
|
||||
|
||||
##############################################################
|
||||
|
||||
|
|
@ -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"
|
||||
|
|
|
|||
|
|
@ -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)
|
||||
|
|
@ -120,7 +120,7 @@ if (tolower(gene)%in%geneL_na){
|
|||
, violin_quantiles = c(0.5), monochrome = F)
|
||||
|
||||
distanceP_na
|
||||
}
|
||||
}
|
||||
#==============
|
||||
# Plot:DUET
|
||||
#==============
|
||||
|
|
|
|||
|
|
@ -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)
|
||||
|
|
|
|||
|
|
@ -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"
|
||||
)
|
||||
|
||||
|
|
|
|||
|
|
@ -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
|
||||
#=============================================
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue