removed setDT and replaced with dplyr alt in position_count_bp.R
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5 changed files with 210 additions and 110 deletions
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@ -35,6 +35,26 @@ source("~/git/LSHTM_analysis/config/embb.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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class(merged_df3)
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merged_df3 = as.data.frame(merged_df3)
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class(df3)
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head(df3$pos_count)
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nc_pc_CHANGE = which(colnames(merged_df3)== "pos_count")
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colnames(merged_df3)[nc_pc_CHANGE] = "df2_pos_count_all"
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head(merged_df3$pos_count)
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head(merged_df3$pos_count_all)
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# DROP pos_count column
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# merged_df3$pos_count <-NULL
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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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#=======
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# output
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#=======
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@ -42,36 +62,21 @@ 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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# consurf_colOld = "consurf_colour_rev"
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# consurf_colNew = "consurf_outcome"
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# df3[[consurf_colNew]] = df3[[consurf_colOld]]
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# df3[[consurf_colNew]] = as.factor(df3[[consurf_colNew]])
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# df3[[consurf_colNew]]
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consurf_colname = "consurf_outcome"
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levels(df3[[consurf_colname]])
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# SNAP2 labels
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snap2_colname = "snap2_outcome"
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levels(df3[[snap2_colname]])
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##############################################################
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gene_all_cols = colnames(df3)[colnames(df3)%in%all_cols]
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gene_outcome_cols = colnames(df3)[colnames(df3)%in%c(outcome_cols_stability
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, outcome_cols_affinity
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, outcome_cols_conservation)]
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gene_outcome_cols
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#=======================================================================
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#------------------------------
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# stability barplots:
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outcome_cols_stability
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# label_categories should be = levels(as.factor(plot_df[[df_colname]]))
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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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@ -158,6 +163,95 @@ dynamut2P
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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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@ -264,74 +358,7 @@ ppi2P = stability_count_bp(plotdf = df3_ppi2
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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_cols_conservation
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#==========================
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# 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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# Plot labels
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@ -457,6 +484,41 @@ OutPlotBP()
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dev.off()
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#####################################################################
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# test
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setDT(df3)[, pos_count2 := .N, by = .(eval(parse(text = "position")))]
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foo = df3[, c("mutationinformation", "position")]
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df4 = foo[, c("mutationinformation", "position")]
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var_pos = "position"
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df4 =
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df4 %>%
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dplyr::add_count(eval(parse(text = var_pos)))
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class(df4)
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df4 = as.data.frame(df4)
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class(df4)
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nc_change = which(colnames(df4) == "n")
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colnames(df4)[nc_change] <- "pos_count"
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class(df4)
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setDT(df4)[, pos_count2 := .N, by = .(eval(parse(text = "position")))]
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class(df4)
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all(df4$pos_count==df4$pos_count2)
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# %>%
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#group_by(pos_count = position)
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# df4 =
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# df4 %>%
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# dplyr::group_by(position) %>%
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# count(position)
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foo2 = df4[, c("mutationinformation", "position", "pos_count")]
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#####################################################################
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# ------------------------------
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# bp site site count: ALL
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