generated ggpairs plots finally
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11 changed files with 657 additions and 939 deletions
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@ -38,7 +38,7 @@ 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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class(merged_df3)
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head(merged_df3$pos_count)
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nc_pc_CHANGE = which(colnames(merged_df3)== "pos_count"); nc_pc_CHANGE
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@ -198,10 +198,10 @@ rects <- data.frame(x = 1:6,
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)
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rects
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rects$text = c("-ve Lig affinty"
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, "+ve Lig affinity"
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, "+ve PPI2 affinity"
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, "-ve PPI2 affinity"
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rects$text = c("-ve Lig"
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, "+ve Lig"
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, "+ve PPI2"
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, "-ve PPI2"
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, "+ve stability"
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, "-ve stability")
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@ -221,7 +221,7 @@ peP = ggplot(rects, aes(x, y = 0, fill = colors, label = paste0(text,"\n", num_l
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coord_flip()+ scale_x_reverse() +
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# theme_void() # remove any axis markings
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theme_nothing() # remove any axis markings
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peP
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peP2 = ggplot(rects, aes(x, y = 0, fill = colors, label = paste0(text,"\n", num_labels))) +
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geom_tile() + # make square tiles
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@ -229,7 +229,7 @@ peP2 = ggplot(rects, aes(x, y = 0, fill = colors, label = paste0(text,"\n", num_
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scale_fill_identity(guide = "none") + # color the tiles with the colors in the data frame
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coord_fixed() + # make sure tiles are square
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theme_nothing() # remove any axis markings
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peP2
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# ------------------------------
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# bp site site count: ALL
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@ -252,24 +252,24 @@ posC_all = site_snp_count_bp(plotdf = df3
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#------------------------------
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# barplot for sensitivity:
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#------------------------------
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sensP = stability_count_bp(plotdf = df3
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, df_colname = "sensitivity"
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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 = "Sensitivity"
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, bar_fill_values = c("red", "blue")
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, subtitle_colour= "black"
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, sts = 10
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, lts = 8
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, ats = 8
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, als =8
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, ltis = 11
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, geom_ls =2
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)
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# sensP = stability_count_bp(plotdf = df3
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# , df_colname = "sensitivity"
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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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#
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# , yaxis_title = "Number of nsSNPs"
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# , leg_position = "none"
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# , subtitle_text = "Sensitivity"
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# , bar_fill_values = c("red", "blue")
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# , subtitle_colour= "black"
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# , sts = 10
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# , lts = 8
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# , ats = 8
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# , als =8
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# , ltis = 11
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# , geom_ls =2
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# )
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consurfP = stability_count_bp(plotdf = df3
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@ -290,3 +290,95 @@ consurfP = stability_count_bp(plotdf = df3
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consurfP
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####################
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# Sensitivity count
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####################
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table(df3$sensitivity)
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rect_sens=data.frame(mutation_class=c("Resistant","Sensitive")
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, tile_colour =c("red","blue")
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, numbers = c(table(df3$sensitivity)[[1]], table(df3$sensitivity)[[2]]))
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sensP = ggplot(rect_sens, aes(mutation_class, y = 0
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, fill = tile_colour
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, label = paste0("n=", numbers)
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)) +
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geom_tile(width = 1, height = 1) + # make square tiles
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geom_label(color = "black", size = 1.7,fill = "white", alpha=0.7) + # add white text in the middle
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scale_fill_identity(guide = "none") + # color the tiles with the colors in the data frame
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coord_fixed() + # make sure tiles are square
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#coord_flip()+ scale_x_reverse() +
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# theme_void() # remove any axis markings
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theme_nothing() # remove any axis markings
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sensP
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# sensP2 = sensP +
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# coord_flip() + scale_x_reverse()
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# sensP2
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##############################
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# FIXME for other genes: ATTEMPTED to derive numbers
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##############################
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#
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# table(str_df_short$pe_effect_outcome)
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# # extract the numbers
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# DD_lig_n = table(str_df_short$pe_effect_outcome)[[1]]
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# SS_lig_n = 0
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# DD_ppi2_n = table(str_df_short$pe_effect_outcome)[[2]]
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# SS_ppi2_n = table(str_df_short$pe_effect_outcome)[[4]]
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# DD_stability_n = table(str_df_short$pe_effect_outcome)[[3]]
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# SS_stability_n = table(str_df_short$pe_effect_outcome)[[5]]
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#
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# nums = c(DD_lig_n, SS_lig_n,DD_ppi2_n,SS_ppi2_n, DD_stability_n, SS_stability_n )
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#
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# rect_pe = data.frame(x = 1:6
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# , pe_effect_type=c("-ve Lig aff"
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# , "+ve Lig aff"
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# , "-ve PPI2 aff"
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# , " +ve PPI2 aff"
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# , "-ve stability"
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# , "+ve stability")
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#
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# , tile_colour =c("#ffd700" #gold
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# ,"#f0e68c" # khaki
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# , "#ff1493" #deeppink
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# , "#da70d6" #orchid
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# , "#F8766D" # Sred
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# , "#00BFC4") #Sblue
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# # , numbers = c(DD_lig_n
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# # , SS_lig_n
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# # , DD_ppi2_n
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# # , SS_ppi2_n
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# # , DD_stability_n
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# # , SS_stability_n )
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# , numbers = nums
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# )
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#
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# rect_pe$num_labels = paste0("n=", rect_pe$numbers)
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# rect_pe
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#
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# # create plot
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# peP = ggplot(rect_pe, aes(x=pe_effect_type , y = 0, fill = tile_colour
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# , label = paste0(pe_effect_type,"\n", num_labels))) +
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# geom_tile(width = 1, height = 1) + # make square tiles
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# geom_text(color = "black", size = 1.7) + # add white text in the middle
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# scale_fill_identity(guide = "none") + # color the tiles with the colors in the data frame
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# coord_fixed() + # make sure tiles are square
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# coord_flip()+ scale_x_reverse() +
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# # theme_void() # remove any axis markings
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# theme_nothing() # remove any axis markings
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# peP
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#
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# peP2 = ggplot(rect_pe, aes(x=pe_effect_type, y = 0, fill = tile_colour
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# , label = paste0(pe_effect_type,"\n", num_labels))) +
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# geom_tile() +
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# geom_text(color = "black", size = 1.6) +
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# scale_fill_identity(guide = "none") +
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# coord_fixed() +
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# theme_nothing()
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# peP2
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