138 lines
5.2 KiB
R
138 lines
5.2 KiB
R
#!/usr/bin/env Rscript
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#########################################################
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# TASK: Lineage dist plots for stability:
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# average the four tools
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# func from : lineage_dist.R
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# plotdf
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# , x_axis = "duet_scaled"
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# , y_axis = "lineage_labels"
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# , x_lab = "DUET"
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# , all_lineages = F
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# , use_lineages = c("L1", "L2", "L3", "L4")
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# , with_facet = F
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# , facet_wrap_var = "" # FIXME: document what this is for
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# , fill_categ = "mutation_info_labels"
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# , fill_categ_cols = c("#E69F00", "#999999")
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# , my_ats = 15 # axis text size
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# , my_als = 20 # axis label size
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# , my_leg_ts = 16
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# , my_leg_title = 16
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# , my_strip_ts = 20
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# , leg_pos = c(0.8, 0.9)
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# , leg_pos_wf = c("top", "left", "bottom", "right")
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# , leg_dir_wf = c("horizontal", "vertical")
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# , leg_label = ""
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#########################################################
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#=======
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# output
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#=======
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outdir_images = paste0("~/git/Writing/thesis/images/results/"
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, tolower(gene), "/")
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cat("plots will output to:", outdir_images)
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#########################################################
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#=======
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# Data
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#=======
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df2 = merged_df2
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#==================================
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# PREFORMATTING: for consistency
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# IMPORTANT for calculating effects
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#==================================
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head(df2$ddg_foldx)
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df2['ddg_foldxC'] = abs(df2$ddg_foldx)
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head(df2['ddg_foldxC'])
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# reverse signs for foldx scaled values for consistency with other tools
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df2['foldx_scaled_signC'] = abs(df2$foldx_scaled)
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# remove the old ones from
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rm_foldx_cols = c("ddg_foldx","foldx_scaled")
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raw_cols_stab_revised = raw_cols_stability[!raw_cols_stability%in%rm_foldx_cols]
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raw_cols_stab_revised = c(raw_cols_stab_revised,"ddg_foldxC")
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scaled_cols_stab_revised = scaled_cols_stability[!scaled_cols_stability%in%rm_foldx_cols]
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scaled_cols_stab_revised = c(scaled_cols_stab_revised, "foldx_scaled_signC")
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#=================
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# PREFORMATTING: for consistency
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#=================
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df2$sensitivity = ifelse(df2$dst_mode == 1, "R", "S")
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table(df2$sensitivity)
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cols_to_extract = colnames(df2)[colnames(df2)%in%c(common_cols
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, outcome_cols_stability
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, raw_cols_stability
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, scaled_cols_stability
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, raw_cols_stab_revised
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, scaled_cols_stab_revised
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, "lineage","lineage_labels")]
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df2_plot = df2[, cols_to_extract]
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all(table(df2_plot$lineage) == table(df2_plot$lineage_labels))
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# find which stability cols to average: should contain revised foldx
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if ("foldx_scaled_signC"%in%colnames(df2_plot)){
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cat("\nPASS: finding stability cols to average")
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cols2avg_new = which(colnames(df2_plot)%in%scaled_cols_stab_revised)
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}else{
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stop("\nAbort: Foldx column has opposing sign. Can't proceed to avergae.")
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}
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# ensemble average across predictors
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df2_plot['ens_stab_new'] = rowMeans(df2_plot[, cols2avg_new])
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head(df2_plot$position); head(df2_plot$mutationinformation)
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table(df2_plot['ens_stab_new'])
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# scaling average values
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df2_plot["ens_stab_new_scaled"] = lapply(df2_plot["ens_stab_new"]
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, function(x) {
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scales::rescale_mid(x
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, to = c(-1,1)
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, from = c( min(df2_plot["ens_stab_new"])
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, max(df2_plot["ens_stab_new"]))
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, mid = 0
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#, from = c(0,1))
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)})
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min(df2_plot['ens_stab_new']); max(df2_plot['ens_stab_new'])
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foo = df2_plot[c("cols2avg_new", "ens_stab_new_scaled")]
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min(df2_plot['ens_stab_new_scaled']); max(df2_plot['ens_stab_new_scaled'])
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###########################################################
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#====================
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# Output Lineage plot
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#====================
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linD_ens_stabP = paste0(outdir_images
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, tolower(gene)
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,"_linD_ens_stabP.svg")
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cat("\nOutput plot:", linD_ens_stabP)
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svg(linD_ens_stabP, width = 10, height = 10)
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linP_dm_om = lineage_distP(df2_plot
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, with_facet = F
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, x_axis = "ens_stab_new_scaled"
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, y_axis = "lineage_labels"
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, x_lab = "Average stability"
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#, fill_categ = "mutation_info_orig", fill_categ_cols = c("#E69F00", "#999999")
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, fill_categ = "sensitivity"
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, fill_categ_cols = c("red", "blue")
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, label_categories = c("Resistant", "Sensitive")
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, leg_label = ""
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, my_ats = 22 # axis text size
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, my_als = 22 # axis label size
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, my_leg_ts = 22
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, my_leg_title = 22
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, my_strip_ts = 22
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, alpha = 0.56
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)
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linP_dm_om
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dev.off()
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