renamed 2 to _v2
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18 changed files with 761 additions and 976 deletions
753
scripts/plotting/plotting_thesis/basic_barplots.R
Executable file → Normal file
753
scripts/plotting/plotting_thesis/basic_barplots.R
Executable file → Normal file
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@ -25,23 +25,28 @@
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#=============
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# Data: Input
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#==============
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#source("~/git/LSHTM_analysis/config/alr.R")
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source("~/git/LSHTM_analysis/config/embb.R")
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#source("~/git/LSHTM_analysis/config/katg.R")
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#source("~/git/LSHTM_analysis/config/gid.R")
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#source("~/git/LSHTM_analysis/config/pnca.R")
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#source("~/git/LSHTM_analysis/config/embb.R")
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#source("~/git/LSHTM_analysis/config/gid.R")
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#source("~/git/LSHTM_analysis/config/alr.R")
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source("~/git/LSHTM_analysis/config/katg.R")
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#source("~/git/LSHTM_analysis/config/rpob.R")
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source("~/git/LSHTM_analysis/scripts/plotting/get_plotting_dfs.R")
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source("~/git/LSHTM_analysis/scripts/plotting/plotting_colnames.R")
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#source("~/git/LSHTM_analysis/scripts/plotting/plotting_colnames.R") sourced by above
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# sanity check
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cat("\nSourced plotting cols as well:", length(plotting_cols))
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####################################################
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class(merged_df3)
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merged_df3 = as.data.frame(merged_df3)
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class(df3)
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class(merged_df3)
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head(merged_df3$pos_count)
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nc_pc_CHANGE = which(colnames(merged_df3)== "pos_count")
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nc_pc_CHANGE = which(colnames(merged_df3)== "pos_count"); nc_pc_CHANGE
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colnames(merged_df3)[nc_pc_CHANGE] = "df2_pos_count_all"
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head(merged_df3$pos_count)
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head(merged_df3$df2_pos_count_all)
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@ -52,8 +57,7 @@ merged_df3 = merged_df3[, !colnames(merged_df3)%in%c("pos_count")]
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head(merged_df3$pos_count)
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df3 = merged_df3[, colnames(merged_df3)%in%plotting_cols]
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#"nca_distance"%in%colnames(df3)
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#=======
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# output
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@ -62,192 +66,9 @@ outdir_images = paste0("~/git/Writing/thesis/images/results/", tolower(gene), "/
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cat("plots will output to:", outdir_images)
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###########################################################
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# ConSurf labels
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#------------------------------
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# plot default sizes
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#------------------------------
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sts = 22
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subtitle_colour = "black"
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geom_ls = 10
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##############################################################
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#------------------------------
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# stability barplots:
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outcome_stability_cols
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# label_categories should be = levels(as.factor(plot_df[[df_colname]]))
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#-------------------------
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# duetP
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duetP = stability_count_bp(plotdf = df3
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, df_colname = "duet_outcome"
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, leg_title = "mCSM-DUET"
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#, label_categories = labels_duet
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, yaxis_title = "Number of nsSNPs"
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, leg_position = "none"
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, subtitle_text = "mCSM-DUET"
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, geom_ls = geom_ls
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, bar_fill_values = c("#F8766D", "#00BFC4")
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, sts = sts
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, subtitle_colour= subtitle_colour)
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# foldx
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foldxP = stability_count_bp(plotdf = df3
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, df_colname = "foldx_outcome"
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#, leg_title = "FoldX"
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#, label_categories = labels_foldx
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, yaxis_title = ""
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, leg_position = "none"
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, subtitle_text = "FoldX"
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, geom_ls = geom_ls
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, bar_fill_values = c("#F8766D", "#00BFC4")
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, sts = sts
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, subtitle_colour= subtitle_colour)
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# deepddg
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deepddgP = stability_count_bp(plotdf = df3
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, df_colname = "deepddg_outcome"
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#, leg_title = "DeepDDG"
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#, label_categories = labels_deepddg
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, yaxis_title = "Number of nsSNPs"
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, leg_position = "none"
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, subtitle_text = "DeepDDG"
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, geom_ls = geom_ls
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, bar_fill_values = c("#F8766D", "#00BFC4")
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, sts = sts
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, subtitle_colour= subtitle_colour)
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# deepddg
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dynamut2P = stability_count_bp(plotdf = df3
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, df_colname = "ddg_dynamut2_outcome"
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#, leg_title = "Dynamut2"
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#, label_categories = labels_ddg_dynamut2_outcome
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, yaxis_title = ""
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, leg_position = "none"
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, subtitle_text = "Dynamut2"
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, geom_ls = geom_ls
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, bar_fill_values = c("#F8766D", "#00BFC4")
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, sts = sts
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, subtitle_colour= subtitle_colour)
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dynamut2P
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# # extract common legend
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# common_legend = get_legend(duetP +
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# guides(color = guide_legend(nrow = 1)) +
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# theme(legend.position = "top"))
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#
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# #==========================
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# #output: STABILITY PLOTS
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# #===========================
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# bp_stability_CLP = paste0(outdir_images
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# , tolower(gene)
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# ,"_bp_stability_CL.svg")
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#
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# svg(bp_stability_CLP, width = 15, height = 12)
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# print(paste0("plot filename:", bp_stability_CLP))
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#
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# cowplot::plot_grid(
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# common_legend,
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# cowplot::plot_grid(duetP, foldxP
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# , deepddgP, dynamut2P
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# , nrow = 2
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# , ncol = 2
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# #, labels = c("(a)", "(b)", "(c)", "(d)")
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# , labels = "AUTO"
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# , label_size = 25)
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# , ncol = 1
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# , nrow = 2
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# , rel_heights = c(0.4/10,9/10))
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#
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# dev.off()
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###########################################################
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#=========================
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# Conservation outcome
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# check this var:
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outcome_conservation_cols
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all(df3$consurf_colour_rev == df3$consurf_outcome)
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#df3["consurf_outcome"] = as.factor(df3["consurf_outcome"])
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levels(df3[["consurf_outcome"]])
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#==========================
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table(df3$consurf_outcome)
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ggplot(df3, aes_string(x = "consurf_outcome")) +
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geom_bar(aes(fill = eval(parse(text = "consurf_outcome")))
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, show.legend = TRUE) +
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scale_fill_manual(name = ""
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, values = consurf_colours
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#, labels = levels(df3[["snap2_outcome"]])
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)
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# consurf# had to turn label categories off for consurf
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consurfP = stability_count_bp(plotdf = df3
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, df_colname = "consurf_outcome"
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#, leg_title = "ConSurf"
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#, label_categories = labels_consurf
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, yaxis_title = "Number of nsSNPs"
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, leg_position = "top"
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, subtitle_text = "ConSurf"
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, geom_ls = 5
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, bar_fill_values = consurf_colours # from globals
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, sts = sts
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, subtitle_colour= subtitle_colour)
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consurfP
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# provean
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proveanP = stability_count_bp(plotdf = df3
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, df_colname = "provean_outcome"
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#, leg_title = "PROVEAN"
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#, label_categories = labels_provean
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, yaxis_title = ""
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, leg_position = "top"
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, subtitle_text = "PROVEAN"
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, geom_ls = geom_ls
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, bar_fill_values = c("#D01C8B", "#F1B6DA") # light pink and deep
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, sts = sts
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, subtitle_colour= subtitle_colour)
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# snap2
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snap2P = stability_count_bp(plotdf = df3
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, df_colname = "snap2_outcome"
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#, leg_title = "SNAP2"
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#, label_categories = labels_snap2
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, yaxis_title = ""
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, leg_position = "top"
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, subtitle_text = "SNAP2"
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, geom_ls = geom_ls
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, bar_fill_values = c("#D01C8B", "#F1B6DA") # light pink and deep
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, sts = sts
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, subtitle_colour= subtitle_colour)
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#============================
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# output: CONSERVATION PLOTS
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#============================
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# bp_conservation_CLP = paste0(outdir_images
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# ,tolower(gene)
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# ,"_bp_conservation_CL.svg" )
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#
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# print(paste0("plot filename:", bp_conservation_CLP))
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# svg(bp_conservation_CLP, width = 15, height = 6.5)
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#
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# cowplot::plot_grid(proveanP, snap2P, consurfP
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# , nrow = 1
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# , ncol = 3
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# #, labels = c("(a)", "(b)", "(c)", "(d)")
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# , labels = "AUTO"
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# , label_size = 25
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# #, rel_heights = c(0.4/10,9/10))
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# , rel_widths = c(0.9, 0.9, 1.1))
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#
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#
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# dev.off()
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###########################################################
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#=========================
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# Affinity outcome
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# check this var: outcome_cols_affinity
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@ -272,17 +93,19 @@ common_bp_title = paste0("Sites <", DistCutOff, angstroms_symbol)
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mLigP = stability_count_bp(plotdf = df3_lig
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, df_colname = "ligand_outcome"
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#, leg_title = "mCSM-lig"
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#, label_categories = labels_lig
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#, bp_plot_title = paste(common_bp_title, "ligand")
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, yaxis_title = "Number of nsSNPs"
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, leg_position = "none"
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, subtitle_text = "mCSM-lig"
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, geom_ls = geom_ls
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, bar_fill_values = c("#F8766D", "#00BFC4")
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, sts = sts
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, subtitle_colour= subtitle_colour
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#, bp_plot_title = paste(common_bp_title, "ligand")
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)
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, subtitle_colour= "black"
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, sts = 10
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, lts = 8
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, ats = 12
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, als = 11
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, ltis = 11
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, geom_ls = 2.5)
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mLigP
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#------------------------------
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# barplot for ligand affinity:
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# <10 Ang of ligand
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@ -292,236 +115,74 @@ mmLigP = stability_count_bp(plotdf = df3_lig
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, df_colname = "mmcsm_lig_outcome"
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#, leg_title = "mmCSM-lig"
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#, label_categories = labels_mmlig
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#, bp_plot_title = paste(common_bp_title, "ligand")
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, yaxis_title = ""
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, leg_position = "none"
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, subtitle_text = "mmCSM-lig"
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, geom_ls = geom_ls
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, bar_fill_values = c("#F8766D", "#00BFC4")
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, sts = sts
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, subtitle_colour= subtitle_colour
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#, bp_plot_title = paste(common_bp_title, "ligand")
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, subtitle_colour= "black"
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, sts = 10
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, lts = 8
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, ats = 12
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, als = 11
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, ltis = 11
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, geom_ls = 2.5
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)
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mmLigP
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#------------------------------
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# barplot for ppi2 affinity
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# <10 Ang of interface
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#------------------------------
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ppi2P = stability_count_bp(plotdf = df3_ppi2
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, df_colname = "mcsm_ppi2_outcome"
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#, leg_title = "mCSM-ppi2"
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#, label_categories = labels_ppi2
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, yaxis_title = ""
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, leg_position = "none"
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, subtitle_text = "mCSM-ppi2"
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, geom_ls = geom_ls
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, bar_fill_values = c("#F8766D", "#00BFC4")
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, sts = sts
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, subtitle_colour= subtitle_colour
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, bp_plot_title = paste(common_bp_title, "interface")
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)
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# # extract common legend
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# common_legend_aff = get_legend(mLigP +
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# guides(color = guide_legend(nrow = 1)) +
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# theme(legend.position = "top"))
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#
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# #==========================
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# # output: AFFINITY PLOTS
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# #==========================
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# bp_affinity_CLP = paste0(outdir_images
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# ,tolower(gene)
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# ,"_bp_affinity_CL.svg" )
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#
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# print(paste0("plot filename:", bp_stability_CLP))
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# svg(bp_affinity_CLP, width = 15, height = 6.5)
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#
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# cowplot::plot_grid(
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# common_legend,
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# cowplot::plot_grid(mLigP, mmLigP
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# , ppi2P
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# , nrow = 1
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# , ncol = 3
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# #, labels = c("(a)", "(b)", "(c)", "(d)")
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# , labels = "AUTO"
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# , label_size = 25)
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# , ncol = 1
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# , nrow = 2
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# , rel_heights = c(0.4/10,9/10))
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# #, rel_widths = c(1,1,1))
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#
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#
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# dev.off()
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################################################################
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#####################################################################
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#============
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# Plot labels
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#============
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tit1 = "Stability outcome"
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tit2 = "Affinity outcome"
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tit3 = "Conservation outcome"
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pt_size = 30
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theme_georgia <- function(...) {
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theme_gray(base_family = "sans", ...) +
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theme(plot.title = element_text(face = "bold"))
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if (tolower(gene)%in%geneL_ppi2){
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ppi2P = stability_count_bp(plotdf = df3_ppi2
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, df_colname = "mcsm_ppi2_outcome"
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#, leg_title = "mCSM-ppi2"
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#, label_categories = labels_ppi2
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#, bp_plot_title = paste(common_bp_title, "PP-interface")
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, yaxis_title = "Number of nsSNPs"
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, leg_position = "none"
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, subtitle_text = "mCSM-ppi2"
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, bar_fill_values = c("#F8766D", "#00BFC4")
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, subtitle_colour= "black"
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, sts = 10
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, lts = 8
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, ats = 12
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, als = 11
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, ltis = 11
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, geom_ls = 2.5
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)
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ppi2P
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}
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#----------------------------
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# barplot for ppi2 affinity
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# <10 Ang of interface
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#------------------------------
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if (tolower(gene)%in%geneL_na){
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nca_distP = stability_count_bp(plotdf = df3_na
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, df_colname = "mcsm_na_outcome"
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#, leg_title = "mCSM-NA"
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#, label_categories =
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#, bp_plot_title = paste(common_bp_title, "Dist to NA")
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, yaxis_title = "Number of nsSNPs"
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, leg_position = "none"
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, subtitle_text = "mCSM-NA"
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, bar_fill_values = c("#F8766D", "#00BFC4")
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, subtitle_colour= "black"
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, sts = 10
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, lts = 8
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, ats = 12
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, als = 11
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, ltis = 11
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, geom_ls = 2.5
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)
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nca_distP
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}
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title_theme <- calc_element("plot.title", theme_georgia())
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pt1 = ggdraw() +
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draw_label(
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tit1,
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fontfamily = title_theme$family,
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fontface = title_theme$face,
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#size = title_theme$size
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size = pt_size
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)
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pt2 = ggdraw() +
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draw_label(
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tit2,
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fontfamily = title_theme$family,
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fontface = title_theme$face,
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size = pt_size
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)
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pt3 = ggdraw() +
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draw_label(
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tit3,
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fontfamily = title_theme$family,
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fontface = title_theme$face,
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size = pt_size
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)
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# extract common legend
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common_legend_outcome = get_legend(mLigP +
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guides(color = guide_legend(nrow = 1)) +
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theme(legend.position = "top"))
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my_label_size = 25
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#======================
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# Output plot function
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#======================
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OutPlotBP = function(x){
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cowplot::plot_grid(
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cowplot::plot_grid(pt1,
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common_legend_outcome,
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cowplot::plot_grid( duetP, foldxP
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, deepddgP, dynamut2P
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, nrow = 2
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, ncol = 2
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, labels = c("A", "B", "C","D")
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, label_size = my_label_size
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)
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, ncol = 1
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, rel_heights = c(7, 3, 90)),
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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 = "")
|
||||
|
||||
|
||||
#===========================
|
||||
# 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()
|
||||
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
|
||||
}
|
||||
|
||||
|
||||
#===============================================================
|
||||
|
||||
|
||||
# ------------------------------
|
||||
# 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
|
||||
|
||||
#####################################################################################
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue