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331
scripts/plotting/plotting_thesis/alr/prominent_effects_alr.R
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331
scripts/plotting/plotting_thesis/alr/prominent_effects_alr.R
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########################################################
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pos_colname = "position"
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#-------------
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# from ~/git/LSHTM_analysis/scripts/plotting/plotting_colnames.R
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#-------------
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length(all_stability_cols); length(raw_stability_cols)
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length(scaled_stability_cols); length(outcome_stability_cols)
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length(affinity_dist_colnames)
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static_cols = c("mutationinformation",
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#"position",
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pos_colname,
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"sensitivity")
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other_cols_all = c(scaled_stability_cols, scaled_affinity_cols, affinity_dist_colnames)
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#omit avg cols and foldx_scaled_signC cols
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other_cols = other_cols_all[grep("avg", other_cols_all, invert = T)]
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other_cols = other_cols[grep("foldx_scaled_signC",other_cols, invert = T )]
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other_cols
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cols_to_extract = c(static_cols, other_cols)
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cat("\nExtracting cols:", cols_to_extract)
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expected_ncols = length(static_cols) + length(other_cols)
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expected_ncols
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str_df = merged_df3[, cols_to_extract]
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if (ncol(str_df) == expected_ncols){
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cat("\nPASS: successfully extracted cols for calculating prominent effects")
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}else{
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stop("\nAbort: Could not extract cols for calculating prominent effects")
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}
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#=========================
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# Masking affinity columns
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#=========================
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# First make values for affinity cols 0 when their corresponding dist >10
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head(str_df)
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# replace in place affinity values >10
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str_df[str_df["ligand_distance"]>10,"affinity_scaled"]=0
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str_df[str_df["ligand_distance"]>10,"mmcsm_lig_scaled"]=0
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#ppi2 gene: replace in place ppi2 affinity values where ppi2 dist >10
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if (tolower(gene)%in%geneL_ppi2){
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str_df[str_df["interface_dist"]>10,"mcsm_ppi2_scaled"]=0
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}
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# na gene: replace in place na affinity values where na dist >10
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if (tolower(gene)%in%geneL_na){
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str_df[str_df["nca_distance"]>10,"mcsm_na_scaled"]=0
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}
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colnames(str_df)
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head(str_df)
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scaled_cols_tc = other_cols[grep("scaled", other_cols)]
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################################################
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#===============
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# whole df
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#===============
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give_col=function(x,y,df=str_df){
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df[df[[pos_colname]]==x,y]
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}
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for (i in unique(str_df[[pos_colname]]) ){
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print(i)
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#cat(length(unique(str_df[[pos_colname]])))
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biggest = max(abs(give_col(i,scaled_cols_tc)))
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str_df[str_df[[pos_colname]]==i,'abs_max_effect'] = biggest
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str_df[str_df[[pos_colname]]==i,'effect_type']= names(
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give_col(i,scaled_cols_tc)[which(
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abs(
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give_col(i,scaled_cols_tc)
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) == biggest, arr.ind=T
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)[, "col"]])[1]
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effect_name = unique(str_df[str_df[[pos_colname]]==i,'effect_type'])#[1] # pick first one in case we have multiple exact values
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# get index/rowname for value of max effect, and then use it to get the original sign
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# here
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#ind = rownames(which(abs(str_df[str_df[[pos_colname]]==i,c('position',effect_name)][effect_name])== biggest, arr.ind=T))
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ind = rownames(which(abs(str_df[str_df[[pos_colname]]==i,c(pos_colname,effect_name)][effect_name])== biggest, arr.ind=T))
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str_df[str_df[[pos_colname]]==i,'effect_sign'] = sign(str_df[effect_name][ind,])[1]
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}
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# ends with suffix 2 if dups
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str_df$effect_type = sub("\\.[0-9]+", "", str_df$effect_type) # cull duplicate effect types that happen when there are exact duplicate values
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colnames(str_df)
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table(str_df$effect_type)
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# check
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str_df_check = str_df[str_df[[pos_colname]]%in%c(24, 32, 160, 303, 334),]
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#================
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# for Plots
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#================
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str_df_short = str_df[, c("mutationinformation",
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#"position",
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pos_colname,
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"sensitivity"
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, "effect_type"
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, "effect_sign")]
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table(str_df_short$effect_type)
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table(str_df_short$effect_sign)
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str(str_df_short)
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# assign pe outcome
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str_df_short$pe_outcome = ifelse(str_df_short$effect_sign<0, "DD", "SS")
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table(str_df_short$pe_outcome )
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table(str_df_short$effect_sign)
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#==============
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# group effect type:
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# lig, ppi2, nuc. acid, stability
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#==============
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affcols = c("affinity_scaled", "mmcsm_lig_scaled")
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ppi2_cols = c("mcsm_ppi2_scaled")
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#lig
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table(str_df_short$effect_type)
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str_df_short$effect_grouped = ifelse(str_df_short$effect_type%in%affcols
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, "lig"
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, str_df_short$effect_type)
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table(str_df_short$effect_grouped)
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#ppi2
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str_df_short$effect_grouped = ifelse(str_df_short$effect_grouped%in%ppi2_cols
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, "ppi2"
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, str_df_short$effect_grouped)
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table(str_df_short$effect_grouped)
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#stability
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str_df_short$effect_grouped = ifelse(!str_df_short$effect_grouped%in%c("lig",
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"ppi2"
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)
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, "stability"
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, str_df_short$effect_grouped)
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table(str_df_short$effect_grouped)
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# create a sign as well
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str_df_short$pe_effect_outcome = paste0(str_df_short$pe_outcome, "_"
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, str_df_short$effect_grouped)
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table(str_df_short$pe_effect_outcome)
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#####################################################################
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# Chimera: for colouring
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####################################################################
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#-------------------------------------
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# get df with unique position
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#--------------------------------------
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#data[!duplicated(data$x), ]
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str_df_plot = str_df_short[!duplicated(str_df[[pos_colname]]),]
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if (nrow(str_df_plot) == length(unique(str_df[[pos_colname]]))){
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cat("\nPASS: successfully extracted df with unique positions")
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}else{
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stop("\nAbort: Could not extract df with unique positions")
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}
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#-------------------------------------
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# generate colours for effect types
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#--------------------------------------
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str_df_plot_cols = str_df_plot[, c(pos_colname,
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"sensitivity",
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"pe_outcome",
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"effect_grouped",
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"pe_effect_outcome")]
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head(str_df_plot_cols)
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# colour intensity based on sign
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#str_df_plot_cols$colour_hue = ifelse(str_df_plot_cols$effect_sign<0, "bright", "light")
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str_df_plot_cols$colour_hue = ifelse(str_df_plot_cols$pe_outcome=="DD", "bright", "light")
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table(str_df_plot_cols$colour_hue); table(str_df_plot$pe_outcome)
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head(str_df_plot_cols)
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# colour based on effect
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table(str_df_plot_cols$pe_effect_outcome)
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# colors = c("#ffd700" #gold
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# , "#f0e68c" #khaki
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# , "#da70d6"# orchid
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# , "#ff1493"# deeppink
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# , "#a0522d" #sienna
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# , "#d2b48c" # tan
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# , "#00BFC4" #, "#007d85" #blue
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# , "#F8766D" )# red
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pe_colour_map = c("DD_lig" = "#ffd700" # gold
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, "SS_lig" = "#f0e68c" # khaki
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, "DD_nucleic_acid"= "#a0522d" # sienna
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, "SS_nucleic_acid"= "#d2b48c" # tan
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, "DD_ppi2" = "#da70d6" # orchid
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, "SS_ppi2" = "#ff1493" # deeppink
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, "DD_stability" = "#f8766d" # red
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, "SS_stability" = "#00BFC4") # blue
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#unlist(d[c('a', 'a', 'c', 'b')], use.names=FALSE)
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#map the colours
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str_df_plot_cols$colour_map= unlist(map(str_df_plot_cols$pe_effect_outcome
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,function(x){pe_colour_map[[x]]}
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))
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head(str_df_plot_cols$colour_map)
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table(str_df_plot_cols$colour_map)
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table(str_df_plot_cols$pe_effect_outcome)
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# str_df_plot_cols$colours = paste0(str_df_plot_cols$colour_hue
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# , "_"
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# , str_df_plot_cols$colour_map)
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# head(str_df_plot_cols$colours)
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# table(str_df_plot_cols$colours)
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#
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#
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# class(str_df_plot_cols$colour_map)
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# str(str_df_plot_cols)
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# sort by colour
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head(str_df_plot_cols)
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str_df_plot_cols = str_df_plot_cols[order(str_df_plot_cols$colour_map), ]
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head(str_df_plot_cols)
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#======================================
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# write file with prominent effects
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#======================================
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outdir_images = paste0("~/git/Writing/thesis/images/results/", tolower(gene), "/")
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write.csv(str_df_plot_cols, paste0(outdir_images, tolower(gene), "_prominent_effects.csv"))
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################################
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# printing for chimera
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###############################
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chain_suffix = ".A"
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str_df_plot_cols$pos_chain = paste0(str_df_plot_cols[[pos_colname]], chain_suffix)
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table(str_df_plot_cols$colour_map)
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table(str_df_plot_cols$pe_effect_outcome)
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#===================================================
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#-------------------
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# Ligand Affinity
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#-------------------
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# -ve Lig Aff
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dd_lig = str_df_plot_cols[str_df_plot_cols$pe_effect_outcome=="DD_lig",]
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if (nrow(dd_lig) == table(str_df_plot_cols$pe_effect_outcome)[['DD_lig']]){
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dd_lig_pos = dd_lig[[pos_colname]]
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}else{
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stop("Abort: DD affinity colour numbers mismtatch")
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}
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# +ve Lig Aff
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ss_lig = str_df_plot_cols[str_df_plot_cols$pe_effect_outcome=="SS_lig",]
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if (!empty(ss_lig)){
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if (nrow(ss_lig) == table(str_df_plot_cols$pe_effect_outcome)[['SS_lig']]){
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ss_lig_pos = ss_lig[[pos_colname]]
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}else{
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stop("Abort: SS affinity colour numbers mismtatch")
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}
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#put in chimera cmd
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paste0(dd_lig_pos, chain_suffix)
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paste0(ss_lig_pos, chain_suffix)
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}
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#===================================================
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#-------------------
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# PPI2 Affinity
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#-------------------
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# -ve PPI2
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dd_ppi2 = str_df_plot_cols[str_df_plot_cols$pe_effect_outcome=="DD_ppi2",]
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if (nrow(dd_ppi2) == table(str_df_plot_cols$pe_effect_outcome)[['DD_ppi2']]){
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dd_ppi2_pos = dd_ppi2[[pos_colname]]
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}else{
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stop("Abort: DD PPI2 colour numbers mismtatch")
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}
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# +ve PPI2
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ss_ppi2 = str_df_plot_cols[str_df_plot_cols$pe_effect_outcome=="SS_ppi2",]
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if (nrow(ss_ppi2) == table(str_df_plot_cols$pe_effect_outcome)[['SS_ppi2']]){
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ss_ppi2_pos = ss_ppi2[[pos_colname]]
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}else{
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stop("Abort: SS PPI2 colour numbers mismtatch")
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}
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#put in chimera cmd
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paste0(dd_ppi2_pos,chain_suffix)
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paste0(ss_ppi2_pos,chain_suffix)
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#=========================================================
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#------------------------
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# Stability
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#------------------------
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# -ve Stability
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dd_stability = str_df_plot_cols[str_df_plot_cols$pe_effect_outcome=="DD_stability",]
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if (nrow(dd_stability) == table(str_df_plot_cols$pe_effect_outcome)[['DD_stability']]){
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dd_stability_pos = dd_stability[[pos_colname]]
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}else{
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stop("Abort: DD Stability colour numbers mismtatch")
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}
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# +ve Stability
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ss_stability = str_df_plot_cols[str_df_plot_cols$pe_effect_outcome=="SS_stability",]
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if (nrow(ss_stability) == table(str_df_plot_cols$pe_effect_outcome)[['SS_stability']]){
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ss_stability_pos = ss_stability[[pos_colname]]
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}else{
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stop("Abort: SS Stability colour numbers mismtatch")
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}
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#put in chimera cmd
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# stabiliting first as it has less numbers
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paste0(ss_stability_pos, chain_suffix)
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paste0(dd_stability_pos, chain_suffix)
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####################################################################
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