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10 changed files with 833 additions and 285 deletions
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scripts/dm_om_data.R
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0
scripts/dm_om_data.R
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@ -40,12 +40,12 @@ bp_stability_hmap <- function(plot_df = merged_df3
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#, bar_col_colname = "group"
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, stability_colname = "duet_scaled" # Only here so that you can do function(df)
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, stability_outcome_colname = "duet_outcome" # Only here so that you can do function(df)
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, p_title = "DUMMY TITLE" # Only here so that you can do function(df)
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, my_xaxls = 12 # x-axis label size
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, my_yaxls = 20 # y-axis label size
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, my_xaxts = 18 # x-axis text size
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, my_yaxts = 20 # y-axis text size
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, my_pts = 20 # plot-title size
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, p_title = "DUMMY TITLE", # Only here so that you can do function(df)
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my_xaxls = 6, # x-axis label size
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my_yaxls = 6, # y-axis label size
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my_xaxts = 9, # x-axis text size
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my_yaxts = 10, # y-axis text size
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my_pts = 10 # plot-title size
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, my_xlab = "Position"
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, my_ylab = "No. of nsSNPs"
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@ -68,7 +68,7 @@ bp_stability_hmap <- function(plot_df = merged_df3
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# Build data with colours
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# ~ ligand distance
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#=========================
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plot_df = generate_distance_colour_map(plot_df, debug=TRUE)
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# plot_df = generate_distance_colour_map(plot_df, debug=TRUE)
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# order the df by position and ensure it is a factor
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plot_df = plot_df[order(plot_df[[xvar_colname]]), ]
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@ -104,7 +104,7 @@ bp_stability_hmap <- function(plot_df = merged_df3
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# , ordered = T)
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)) +
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geom_bar(aes(fill = group)
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, colour = "grey") +
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, colour = "grey", size=0.125) +
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scale_fill_manual( values = subcols_bp_hmap
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, guide = "none") +
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@ -120,11 +120,12 @@ bp_stability_hmap <- function(plot_df = merged_df3
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, hjust = 1
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, vjust = 0)
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, axis.title.x = element_blank()
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, axis.ticks = element_blank()
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#, axis.title.x = element_text(size = my_xaxts)
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, axis.title.y = element_text(size = my_yaxts )
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, plot.title = element_text(size = my_pts
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, hjust = 0.5)
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, panel.grid = element_blank()
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# , panel.grid = element_blank()
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, panel.background = element_rect(fill = "transparent", colour=NA)
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) +
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@ -132,25 +133,20 @@ bp_stability_hmap <- function(plot_df = merged_df3
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, x = my_xlab
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, y = my_ylab),
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NULL,
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ggplot(plot_df,
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aes(x=factor(position), # THIS STUPID FUCKING FACTOR THING
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)
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) +
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geom_tile(aes(y=0),
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fill=plot_df$ligD_colours) +
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scale_x_discrete("Position", labels=factor(plot_df$position)) +
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theme_nothing() +
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theme(plot.background = element_rect(fill = "transparent", colour=NA),
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plot.margin = margin(t=0,b=0)) +
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labs(x = NULL, y = NULL), #end of distance-heat-bar
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NULL,
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position_annotation(plot_df),
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position_annotation(plot_df,
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aa_pos_drug=aa_pos_drug,
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active_aa_pos=active_aa_pos,
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aa_pos_lig1=aa_pos_lig1,
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aa_pos_lig2=aa_pos_lig2,
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aa_pos_lig3=aa_pos_lig3
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)
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,
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#generate_distance_legend(plot_df),
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ncol = 1,
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align = "v",
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rel_heights = c(10,-0.1,1,-0.1,1)
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rel_heights = c(10,-0.1,1)
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#rel_widths = c(9/10, 0.4/10)
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)
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}
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#bp_stability_hmap(small_df3)
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#bp_stability_hmap(merged_df3)
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@ -3,7 +3,7 @@ generate_distance_colour_map = function(plot_df,
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xvar_colname = "position",
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lig_dist_colname = "ligand_distance",
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#lig_dist_colours = c("green", "yellow", "orange", "red"),
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lig_dist_colours = c("tan", "black"),
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lig_dist_colours = c("green", "yellow", "magenta"),
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debug = TRUE
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)
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{
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@ -75,9 +75,9 @@ generate_distance_legend = function(plot_df,
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geom_tile(aes(fill = .data[[lig_dist_colname]])
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, colour = "white") +
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scale_fill_gradient2(midpoint = lig_mean
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, low = "tan"
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, mid = "grey50"
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, high = "black"
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, low = "green"
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, mid = "yellow"
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, high = "magenta"
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, breaks = labels
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, limits = c(lig_min, lig_max)
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, labels = labelsD
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@ -250,8 +250,16 @@ LogoPlotCustomH <- function(plot_df
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#scale_x_discrete(x_lab, labels=factor(unique_colour_map$position)) +
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scale_color_manual(values=unique_colour_map$ligD_colours) +
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scale_fill_manual(values=unique_colour_map$ligD_colours) +
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labs(y = NULL), NULL,
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position_annotation(plot_df, bg=theme_bgc),
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labs(y = NULL),
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NULL,
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position_annotation(plot_df,
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bg = theme_bgc,
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aa_pos_drug=aa_pos_drug,
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active_aa_pos=active_aa_pos,
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aa_pos_lig1=aa_pos_lig1,
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aa_pos_lig2=aa_pos_lig2,
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aa_pos_lig3=aa_pos_lig3
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),
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ncol=1, align='v', rel_heights = c(16,0,1,0,1)
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)
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@ -1,7 +1,7 @@
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########################a###########################################################
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# Input:
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# Data
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# plot_df: merged_df3 containing the OR column to use as y-axis or any other relevant column
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# mutable_df: merged_df3 containing the OR column to use as y-axis or any other relevant column
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# x_axis_colname = "position"
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# symbol_mut_colname = "mutant_type"
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@ -38,16 +38,16 @@ LogoPlotSnps <- function(plot_df
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, my_logo_col = "chemistry"
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, x_lab = "Position"
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, y_lab = "Count"
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, x_ats = 14 # text size
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, x_ats = 7 # text size
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, x_tangle = 90 # text angle
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, y_ats = 22
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, y_ats = 10
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, y_tangle = 0
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, x_tts = 20 # title size
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, y_tts = 23
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, x_tts = 10 # title size
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, y_tts = 10
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, leg_pos = "none" # can be top, left, right and bottom or c(0.8, 0.9)
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, leg_dir = "horizontal" #can be vertical or horizontal
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, leg_ts = 20 # leg text size
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, leg_tts = 16 # leg title size
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, leg_ts = 10 # leg text size
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, leg_tts = 8 # leg title size
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, tpos0 = 0 # 0 is a magic number that does my sensible default
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, tW0 = 1
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, tH0 = 0.2
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@ -56,6 +56,7 @@ LogoPlotSnps <- function(plot_df
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)
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{
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mutable_df=cbind(plot_df)
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# handle funky omit_snp_count. DOES NOT WORK YET
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if (class(omit_snp_count) != "numeric"){
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omit_snp_count <- as.numeric(unlist(str_extract_all(omit_snp_count, regex("[0-9]+"))))
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@ -65,51 +66,51 @@ LogoPlotSnps <- function(plot_df
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############################################
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# Generate "ligand distance" colour map
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plot_df = generate_distance_colour_map(plot_df, debug=TRUE)
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unique_colour_map = unique(plot_df[,c("position","ligD_colours")])
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unique_colour_map = unique_colour_map[order(unique_colour_map$position), ]
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rownames(unique_colour_map) = unique_colour_map$position
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unique_colour_map2 = unique_colour_map
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unique_colour_map2$position=as.factor(unique_colour_map2$position)
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unique_colour_map2$ligD_colours = as.factor(unique_colour_map2$ligD_colours)
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# mutable_df = generate_distance_colour_map(mutable_df, debug=TRUE)
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# unique_colour_map = unique(mutable_df[,c("position","ligD_colours")])
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# unique_colour_map = unique_colour_map[order(unique_colour_map$position), ]
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# rownames(unique_colour_map) = unique_colour_map$position
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# unique_colour_map2 = unique_colour_map
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# unique_colour_map2$position=as.factor(unique_colour_map2$position)
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# unique_colour_map2$ligD_colours = as.factor(unique_colour_map2$ligD_colours)
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#
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setDT(plot_df)[, mut_pos_occurrence := .N, by = .(eval(parse(text=x_axis_colname)))]
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setDT(mutable_df)[, mut_pos_occurrence := .N, by = .(eval(parse(text=x_axis_colname)))]
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if (debug) {
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table(plot_df[[x_axis_colname]])
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table(plot_df$mut_pos_occurrence)
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table(mutable_df[[x_axis_colname]])
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table(mutable_df$mut_pos_occurrence)
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}
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max_mut = max(table(plot_df[[x_axis_colname]]))
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max_mut = max(table(mutable_df[[x_axis_colname]]))
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# Subset Data as specified by user
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cat("\nDisplaying nsSNP position frequency:\n")
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print(table(plot_df$mut_pos_occurrence))
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print(table(mutable_df$mut_pos_occurrence))
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if ( (length(omit_snp_count) ==1) && (omit_snp_count == 0) ){
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my_data_snp = plot_df
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my_data_snp = mutable_df
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u = unique(my_data_snp[[x_axis_colname]])
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max_mult_mut = max(table(my_data_snp[[x_axis_colname]]))
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if (debug) {
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cat("\nNo filtering requested:"
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, "\nTotal no. of nsSNPs:", sum(table(plot_df$mut_pos_occurrence))
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, "\nTotal no. of nsSNPs omitted:", sum(table(plot_df$mut_pos_occurrence)[omit_snp_count])
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, "\nTotal no. of nsSNPs:", sum(table(mutable_df$mut_pos_occurrence))
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, "\nTotal no. of nsSNPs omitted:", sum(table(mutable_df$mut_pos_occurrence)[omit_snp_count])
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, "\nDim of data:", dim(my_data_snp)
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, "\nNo. of positions:", length(u)
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, "\nMax no. of muts at any position:", max_mult_mut)
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}
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} else {
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my_data_snp = subset(plot_df, !(mut_pos_occurrence%in%omit_snp_count) )
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my_data_snp = subset(mutable_df, !(mut_pos_occurrence%in%omit_snp_count) )
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exp_nrows = sum(table(plot_df$mut_pos_occurrence)) - sum(table(plot_df$mut_pos_occurrence)[omit_snp_count])
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exp_nrows = sum(table(mutable_df$mut_pos_occurrence)) - sum(table(mutable_df$mut_pos_occurrence)[omit_snp_count])
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got_rows = sum(table(my_data_snp$mut_pos_occurrence))
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u = unique(my_data_snp[[x_axis_colname]])
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max_mult_mut = max(table(my_data_snp[[x_axis_colname]]))
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if (debug) {
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if (got_rows == exp_nrows) {
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cat("\nPass: Position with the stated nsSNP frequency filtered:", omit_snp_count
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, "\nTotal no. of nsSNPs:", sum(table(plot_df$mut_pos_occurrence))
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, "\nTotal no. of nsSNPs omitted:", sum(table(plot_df$mut_pos_occurrence)[omit_snp_count])
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, "\nTotal no. of nsSNPs:", sum(table(mutable_df$mut_pos_occurrence))
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, "\nTotal no. of nsSNPs omitted:", sum(table(mutable_df$mut_pos_occurrence)[omit_snp_count])
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, "\nDim of subsetted data:", dim(my_data_snp)
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, "\nNo. of positions:", length(u)
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, "\nMax no. of muts at any position:", max_mult_mut)
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@ -145,7 +146,7 @@ LogoPlotSnps <- function(plot_df
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if (is.matrix(tab_mt)){
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if (debug) {
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cat("\nCreating mutant matrix..."
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#, "\nRownames of mutant matrix:", rownames(tab_mt)
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#, "\nRowna mes of mutant matrix:", rownames(tab_mt)
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#, "\nColnames of mutant matrix:", colnames(tab_mt)
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)
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}
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@ -211,114 +212,95 @@ LogoPlotSnps <- function(plot_df
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#####################################
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# Generating logo plots for nsSNPs
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#####################################
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cowplot::plot_grid(
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#-------------------
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# Mutant logo plot
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#-------------------
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ggseqlogo(tab_mt
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, method = 'custom'
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, col_scheme = my_logo_col
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, seq_type = 'aa') +
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#-------------------
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# Mutant logo plot
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#-------------------
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logo_top =ggseqlogo(tab_mt
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, method = 'custom'
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, col_scheme = my_logo_col
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, seq_type = 'aa') +
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scale_x_continuous(breaks = 1:ncol(tab_mt)
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, expand = c(0.01,0)
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, labels = colnames(tab_mt))+
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scale_x_continuous(breaks = 1:ncol(tab_mt)
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, expand = c(0.01,0)
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, labels = colnames(tab_mt))+
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scale_y_continuous(breaks = 0:(max_mult_mut-1)
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, labels = c(1:max_mult_mut)
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, limits = c(0, max_mult_mut)) +
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ylab(y_lab) +
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theme(text=element_text(family="FreeSans")
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, legend.position = leg_pos
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, legend.direction = leg_dir
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, legend.title = element_text(size = leg_tts
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, colour = ytt_col)
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, legend.text = element_text(size = leg_ts)
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scale_y_continuous(breaks = 0:(max_mult_mut-1)
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, labels = c(1:max_mult_mut)
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, limits = c(0, max_mult_mut)) +
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ylab(y_lab) +
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theme(text=element_text(family="FreeSans")
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, legend.position = leg_pos
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, legend.direction = leg_dir
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, legend.title = element_text(size = leg_tts
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, colour = ytt_col)
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, legend.text = element_text(size = leg_ts)
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, axis.text.x = element_text(size = x_ats
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, angle = x_tangle
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, hjust = 1
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, vjust = 0.4
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, colour = xfont_bgc)
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, axis.text.y = element_text(size = y_ats
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, angle = y_tangle
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, hjust = 1
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, vjust = -1.0
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, colour = yfont_bgc)
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, axis.title.x = element_text(size = x_tts
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, colour = xtt_col)
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, axis.title.y = element_text(size = y_tts
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, colour = ytt_col)
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, axis.text.x = element_text(size = x_ats
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, angle = x_tangle
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#, hjust = 1
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#, vjust = 0.4
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, colour = xfont_bgc)
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, axis.text.y = element_text(size = y_ats
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, angle = y_tangle
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, hjust = 1
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, vjust = -1.0
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, colour = yfont_bgc)
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# , axis.title.x = element_text(size = x_tts
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# , colour = xtt_col)
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, axis.title.x = element_blank()
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, axis.title.y = element_text(size = y_tts
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, colour = ytt_col)
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, plot.background = element_rect(fill = theme_bgc, colour=NA)
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),
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ggseqlogo(tab_wt
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, method = 'custom'
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, col_scheme = my_logo_col
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, seq_type = 'aa') +
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scale_x_continuous(breaks = 1:ncol(tab_wt)
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, expand = c(0.01,0)
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, labels = as.factor(colnames(tab_wt))) +
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theme(text = element_text(family="FreeSans")
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, legend.position = "none"
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, axis.text.x = element_blank()
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, axis.text.y = element_blank()
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, axis.title.x = element_blank()
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, axis.title.y = element_blank()
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, plot.background = element_rect(fill = theme_bgc, colour=NA)
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) +
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labs(x=NULL, y=NULL),
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ggplot(
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data=unique_colour_map2,
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aes(
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x=factor(position), 0 # heat-mapped distance tiles along the bot
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, fill = position
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, colour = position
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, linetype = "blank"
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)
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, plot.background = element_rect(fill = theme_bgc, colour=NA)
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)
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logo_bottom = ggseqlogo(tab_wt
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, method = 'custom'
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, col_scheme = my_logo_col
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, seq_type = 'aa') +
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scale_x_continuous(breaks = 1:ncol(tab_wt)
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, expand = c(0.01,0)
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, labels = as.factor(colnames(tab_wt))) +
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theme(text = element_text(family="FreeSans")
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, legend.position = "none"
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, axis.text.x = element_blank()
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, axis.text.y = element_blank()
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, axis.title.x = element_blank()
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, axis.title.y = element_blank()
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, plot.background = element_rect(fill = theme_bgc, colour=NA)
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) +
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geom_tile() +
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theme(
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axis.text.x = element_blank()
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, axis.ticks.x = element_blank()
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# axis.text.x = element_text(size = x_ats
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# , angle = x_tangle
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# , hjust = 1
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# , vjust = 0.4
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# , colour = xfont_bgc)
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, axis.text.y = element_blank()
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, axis.ticks.y = element_blank()
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, axis.title.x = element_blank()
|
||||
labs(x=NULL, y=NULL)
|
||||
|
||||
# , axis.title.x = element_text(size = x_tts
|
||||
# , colour = xtt_col)
|
||||
# , axis.title.y = element_text(size = y_tts
|
||||
# , colour = ytt_col)
|
||||
# , legend.title = element_text(size = leg_tts
|
||||
# , colour = ytt_col)
|
||||
, legend.text = element_text(size = leg_ts)
|
||||
anno_bar = position_annotation(plot_df,
|
||||
bg = theme_bgc,
|
||||
aa_pos_drug=aa_pos_drug,
|
||||
active_aa_pos=active_aa_pos,
|
||||
aa_pos_lig1=aa_pos_lig1,
|
||||
aa_pos_lig2=aa_pos_lig2,
|
||||
aa_pos_lig3=aa_pos_lig3)
|
||||
|
||||
, legend.position = leg_pos
|
||||
, legend.direction = leg_dir
|
||||
, plot.background = element_rect(fill = theme_bgc, colour=NA)
|
||||
, plot.margin = margin(t=0)
|
||||
, panel.grid=element_blank()
|
||||
, panel.background = element_rect(fill = theme_bgc, colour=NA)
|
||||
) +
|
||||
scale_x_discrete(x_lab, labels=unique_colour_map$position) +
|
||||
#scale_x_discrete(x_lab, labels=factor(unique_colour_map$position)) +
|
||||
scale_color_manual(values=unique_colour_map$ligD_colours) +
|
||||
scale_fill_manual(values=unique_colour_map$ligD_colours) +
|
||||
labs(y = NULL)
|
||||
, NULL
|
||||
, position_annotation(plot_df, bg=theme_bgc)
|
||||
, ncol=1
|
||||
, align = "v"
|
||||
, axis='lr'
|
||||
, rel_heights = c(7/10, 2/7,1/7, -0.1, 0.5/7))
|
||||
aligned=align_plots(logo_top, logo_bottom, anno_bar, align='v', axis='lr')
|
||||
cowplot::plot_grid(
|
||||
aligned[[1]], aligned[[2]], aligned[[3]],
|
||||
ncol=1,
|
||||
#align = "v",
|
||||
rel_heights = c(7, 1,1),
|
||||
rel_widths = c(1,1,0.75)
|
||||
)
|
||||
|
||||
# cowplot::plot_grid(
|
||||
# logo_top,
|
||||
# #NULL,
|
||||
# logo_bottom,
|
||||
# #NULL,
|
||||
# anno_bar,
|
||||
# ncol=1,
|
||||
# align = "v",
|
||||
# rel_heights = c(7, 1,1)
|
||||
# )
|
||||
# top logo, bottom logo, heat bar, NULL, position annotation
|
||||
#------------------
|
||||
# Wild logo plot
|
||||
#------------------
|
||||
}
|
||||
|
||||
#LogoPlotSnps(small_df3)
|
||||
#LogoPlotSnps(mutable_df3)
|
||||
|
|
|
|||
|
|
@ -1,7 +1,15 @@
|
|||
# position_annotation takes a Data Frame (df) and returns a ggplot object.
|
||||
#
|
||||
# This plots position tiles for the (up to) three ligands as well as drug
|
||||
position_annotation=function(plot_df, bg="transparent"){
|
||||
position_annotation=function(plot_df,
|
||||
bg="transparent",
|
||||
aa_pos_drug=1:100,
|
||||
active_aa_pos=1:100,
|
||||
aa_pos_lig1=1:100,
|
||||
aa_pos_lig2=1:100,
|
||||
aa_pos_lig3=1:100
|
||||
)
|
||||
{
|
||||
x_ats = 12
|
||||
x_tangle = 90
|
||||
x_tts = 20
|
||||
|
|
@ -13,48 +21,117 @@ position_annotation=function(plot_df, bg="transparent"){
|
|||
leg_tts = 16
|
||||
leg_pos = "none"
|
||||
|
||||
# plot_df=plot_df[order(plot_df$ligand_distance),]
|
||||
#
|
||||
# plot_df$position = factor(plot_df$position)
|
||||
#plot_df = generate_distance_colour_map(plot_df, debug=TRUE)
|
||||
# plot_df$col_aa = ifelse(plot_df[["position"]]%in%active_aa_pos,
|
||||
# "brown", "transparent")
|
||||
plot_df$col_aa = ifelse(plot_df[["position"]]%in%active_aa_pos,
|
||||
"transparent", "transparent")
|
||||
|
||||
ggplot(plot_df,
|
||||
aes(x=factor(position), # THIS STUPID FUCKING FACTOR THING
|
||||
)
|
||||
) +
|
||||
geom_tile(aes(y=0, fill= bg_all, colour = bg_all)
|
||||
) +
|
||||
geom_tile(aes(y=1, fill= col_bg1, colour = col_bg1)
|
||||
) +
|
||||
geom_tile(aes(y=2, fill= col_bg2, colour = col_bg2)
|
||||
) +
|
||||
geom_tile(aes(y=3, fill= col_bg3, colour = col_bg3)
|
||||
) +
|
||||
plot_df$bg_all = plot_df$col_aa
|
||||
plot_df$bg_all = ifelse(plot_df[["position"]]%in%aa_pos_drug,
|
||||
"green", plot_df$bg_all)
|
||||
|
||||
scale_x_discrete("Position", labels=factor(plot_df$position)) +
|
||||
scale_color_manual(values = c(
|
||||
"brown"="brown",
|
||||
"green"="green",
|
||||
"transparent"="transparent",
|
||||
"slategrey"="slategrey",
|
||||
"navyblue"="navyblue",
|
||||
"purple"="purple"
|
||||
),
|
||||
expand=c(0,0)
|
||||
) +
|
||||
scale_fill_manual(values = c(
|
||||
"brown"="brown",
|
||||
"green"="green",
|
||||
"transparent"="transparent",
|
||||
"slategrey"="slategrey",
|
||||
"navyblue"="navyblue",
|
||||
"purple"="purple"
|
||||
),
|
||||
expand=c(0,0)
|
||||
) +
|
||||
#scale_x_continuous(expand=c(0,0)) +
|
||||
#scale_y_continuous(expand=c(0,0)) +
|
||||
theme_nothing() +
|
||||
plot_df$col_bg1 = plot_df$bg_all
|
||||
plot_df$col_bg1 = ifelse(plot_df[["position"]]%in%aa_pos_lig1,
|
||||
"slategrey", plot_df$col_bg1)
|
||||
|
||||
theme(plot.background = element_rect(fill = bg, colour=NA),
|
||||
plot.margin = margin(t=0,b=0)) +
|
||||
labs(x = NULL, y = NULL)
|
||||
plot_df$col_bg2 = plot_df$col_bg1
|
||||
plot_df$col_bg2 = ifelse(plot_df[["position"]]%in%aa_pos_lig2,
|
||||
"navyblue", plot_df$col_bg2)
|
||||
|
||||
|
||||
plot_df$col_bg3 = plot_df$col_bg2
|
||||
plot_df$col_bg3 = ifelse(plot_df[["position"]]%in%aa_pos_lig3
|
||||
, "purple", plot_df$col_bg3)
|
||||
|
||||
plot_df = generate_distance_colour_map(plot_df, debug=TRUE)
|
||||
|
||||
cowplot::plot_grid(
|
||||
ggplot(plot_df,
|
||||
aes(x=factor(position), # THIS STUPID FUCKING FACTOR THING
|
||||
)
|
||||
) +
|
||||
geom_tile(aes(y=0),
|
||||
fill=plot_df$ligD_colours) +
|
||||
#scale_x_discrete("Position", labels=factor(plot_df$position)) +
|
||||
#theme_nothing() +
|
||||
theme(plot.background = element_rect(fill = "transparent", colour=NA),
|
||||
plot.margin = margin(t=0,b=0),
|
||||
axis.ticks.x = element_blank(),
|
||||
axis.ticks.y = element_blank(),
|
||||
axis.text.y = element_blank(),
|
||||
panel.grid = element_blank(),
|
||||
panel.background = element_rect(fill = "transparent", colour=NA),
|
||||
) +
|
||||
labs(x = NULL, y = NULL), #end of distance-heat-bar
|
||||
#NULL,
|
||||
ggplot(plot_df,
|
||||
aes(x=factor(position), # THIS STUPID FUCKING FACTOR THING
|
||||
#reorder(ligand_distance)
|
||||
)
|
||||
) +
|
||||
# geom_tile(aes(y = 0, fill = col_aa, colour = col_aa)
|
||||
# ) +
|
||||
geom_tile(aes(y = 1, fill = bg_all, colour = bg_all)
|
||||
) +
|
||||
geom_tile(aes(y = 2, fill = col_bg1, colour = col_bg1)
|
||||
) +
|
||||
geom_tile(aes(y = 3, fill = col_bg2, colour = col_bg2)
|
||||
) +
|
||||
geom_tile(aes(y = 4, fill = col_bg3, colour = col_bg3)
|
||||
) +
|
||||
|
||||
#scale_x_discrete("Position", labels=factor(plot_df$position)) +
|
||||
scale_color_manual(values = c(
|
||||
"brown"="brown",
|
||||
"green"="#00ff00",
|
||||
"transparent"="transparent",
|
||||
"slategrey"="#2f4f4f",
|
||||
"navyblue"="#000080",
|
||||
"purple"="#a020f0"
|
||||
),
|
||||
expand=c(0,0)
|
||||
) +
|
||||
scale_fill_manual(values = c(
|
||||
"brown"="brown",
|
||||
"green"="#00ff00",
|
||||
"transparent"="transparent",
|
||||
"slategrey"="#2f4f4f",
|
||||
"navyblue"="#000080",
|
||||
"purple"="#a020f0"
|
||||
),
|
||||
expand=c(0,0)
|
||||
) +
|
||||
#scale_x_continuous(expand=c(0,0)) +
|
||||
#scale_y_continuous(expand=c(0,0)) +
|
||||
theme_nothing() +
|
||||
|
||||
theme(plot.background = element_rect(fill = bg, colour=NA),
|
||||
plot.margin = margin(t=0,b=0)) +
|
||||
labs(x = NULL, y = NULL),
|
||||
ncol=1,
|
||||
rel_heights = c(1,
|
||||
#-0.1,
|
||||
1)
|
||||
)
|
||||
}
|
||||
|
||||
position_annotation(merged_df3,
|
||||
aa_pos_drug=aa_pos_drug,
|
||||
active_aa_pos=active_aa_pos,
|
||||
aa_pos_lig1=aa_pos_lig1,
|
||||
aa_pos_lig2=aa_pos_lig2,
|
||||
aa_pos_lig3=aa_pos_lig3
|
||||
)
|
||||
#
|
||||
# # proof that you can use this function to pass arbitrary lists of numbers :-)
|
||||
# position_annotation(merged_df3,
|
||||
# aa_pos_drug=1:1000,
|
||||
# active_aa_pos=1:1000,
|
||||
# aa_pos_lig1=1:1000,
|
||||
# aa_pos_lig2=1:1000,
|
||||
# aa_pos_lig3=1:1000
|
||||
# )
|
||||
|
|
@ -13,12 +13,6 @@
|
|||
# input args
|
||||
#==========================================================
|
||||
wideP_consurf3 <- function(plot_df
|
||||
, aa_pos_drug = NULL
|
||||
, aa_pos_lig1 = NULL
|
||||
, aa_pos_lig2 = NULL
|
||||
, aa_pos_lig3 = NULL
|
||||
, active_aa_pos = NULL
|
||||
|
||||
, xvar_colname = "position"
|
||||
, yvar_colname = "consurf_score"
|
||||
, yvar_colourN_colname = "consurf_colour_rev" # num from 0-1
|
||||
|
|
@ -65,26 +59,13 @@ wideP_consurf3 <- function(plot_df
|
|||
, annotate_ligand_distance = T
|
||||
, leg_title2 = "Ligand Distance"
|
||||
, lig_dist_colname = LigDist_colname # from globals
|
||||
, lig_dist_colours = c("green", "yellow", "orange", "red")
|
||||
, lig_dist_colours = c("tan", "black")
|
||||
, tpos0 = 0 # 0 is a magic number that does my sensible default
|
||||
, tW0 = 1
|
||||
, tH0 = 0.3
|
||||
|
||||
# Custom 3: x-axis: geom tiles ~ active sites and ligand
|
||||
, annotate_active_sites = T
|
||||
|
||||
, drug_aa_colour = "purple"
|
||||
, active_aa_colour = "brown"
|
||||
|
||||
, aa_colour_lig1 = "blue"
|
||||
, tpos1 = 0
|
||||
|
||||
, aa_colour_lig2 = "cyan"
|
||||
, tpos2 = 0
|
||||
|
||||
, aa_colour_lig3 = "cornflowerblue"
|
||||
, tpos3 = 0
|
||||
|
||||
, default_gt_clr = "white"
|
||||
, build_plot_df=FALSE
|
||||
, debug=FALSE
|
||||
|
|
@ -174,68 +155,6 @@ wideP_consurf3 <- function(plot_df
|
|||
, ligD_cols = plot_df$ligD_colours))
|
||||
}
|
||||
|
||||
###############################################
|
||||
# Custom 3: x-axis geom tiles ~ active sites
|
||||
################################################
|
||||
|
||||
#==========================
|
||||
# Build Data with colours
|
||||
# ~ on active sites
|
||||
#==========================
|
||||
aa_colour_colname = "bg_all"
|
||||
aa_colour_colname1 = "col_bg1"
|
||||
aa_colour_colname2 = "col_bg2"
|
||||
aa_colour_colname3 = "col_bg3"
|
||||
|
||||
if (build_plot_df) {
|
||||
if(annotate_active_sites) {
|
||||
cat("\nAnnotation for xvar requested. Building colours for annotation...")
|
||||
|
||||
|
||||
#--------------------------------------------------
|
||||
# column colour 0: Active site + drug binding sites
|
||||
#--------------------------------------------------
|
||||
plot_df[[aa_colour_colname]] = ifelse(plot_df[[xvar_colname]]%in%aa_pos_drug
|
||||
, drug_aa_colour
|
||||
, ifelse(plot_df[[xvar_colname]]%in%active_aa_pos
|
||||
, active_aa_colour, default_gt_clr ))
|
||||
plot_df[[aa_colour_colname]]
|
||||
cat("\nColumn created 'bg_all':", length(plot_df[[aa_colour_colname]]))
|
||||
|
||||
#------------------------------------------------
|
||||
# column colour 1: Ligand 1 + drug binding sites
|
||||
#------------------------------------------------
|
||||
cat("\nAssigning colours to drug binding and ligand-1 binding residues")
|
||||
plot_df[[aa_colour_colname1]] = plot_df[[aa_colour_colname]]
|
||||
plot_df[[aa_colour_colname1]] = ifelse(plot_df[[xvar_colname]]%in%aa_pos_lig1
|
||||
, aa_colour_lig1, plot_df[[aa_colour_colname]])
|
||||
#------------------------------------------------
|
||||
# column colour 2: Ligand 2
|
||||
#------------------------------------------------
|
||||
plot_df[[aa_colour_colname2]] = plot_df[[aa_colour_colname1]]
|
||||
plot_df[[aa_colour_colname2]] = ifelse(plot_df[[xvar_colname]]%in%aa_pos_lig2
|
||||
, aa_colour_lig2, plot_df[[aa_colour_colname1]])
|
||||
|
||||
#------------------------------------------------
|
||||
# column colour 3: Ligand 3
|
||||
#------------------------------------------------
|
||||
plot_df[[aa_colour_colname3]] = plot_df[[aa_colour_colname2]]
|
||||
plot_df[[aa_colour_colname3]] = ifelse(plot_df[[xvar_colname]]%in%aa_pos_lig3
|
||||
, aa_colour_lig3, plot_df[[aa_colour_colname2]])
|
||||
|
||||
}
|
||||
} else {
|
||||
# set these to the string "DUMMY" so that the build-up-the-tiles bit works
|
||||
aa_pos_drug = "DUMMY"
|
||||
aa_pos_lig1 = "DUMMY"
|
||||
active_aa_pos = "DUMMY"
|
||||
if (aa_colour_colname2 %in% colnames(merged_df3)) {
|
||||
aa_pos_lig2 = "DUMMY"
|
||||
if (aa_colour_colname3 %in% colnames(merged_df3)) {
|
||||
aa_pos_lig2 = "DUMMY"
|
||||
}
|
||||
}
|
||||
}
|
||||
###################
|
||||
# start plot
|
||||
###################
|
||||
|
|
@ -355,7 +274,14 @@ wideP_consurf3 <- function(plot_df
|
|||
plot.margin = margin(t=0,b=0)) +
|
||||
labs(x = NULL, y = NULL), #end of distance-heat-bar
|
||||
NULL,
|
||||
position_annotation(plot_df, bg = panel_col),
|
||||
position_annotation(plot_df,
|
||||
bg = panel_col,
|
||||
aa_pos_drug=aa_pos_drug,
|
||||
active_aa_pos=active_aa_pos,
|
||||
aa_pos_lig1=aa_pos_lig1,
|
||||
aa_pos_lig2=aa_pos_lig2,
|
||||
aa_pos_lig3=aa_pos_lig3
|
||||
),
|
||||
ncol=1,
|
||||
align='v',
|
||||
axis='lr',
|
||||
|
|
|
|||
366
scripts/plotting/plotting_thesis/corr_plots_thesis_ggpairs.R
Normal file
366
scripts/plotting/plotting_thesis/corr_plots_thesis_ggpairs.R
Normal file
|
|
@ -0,0 +1,366 @@
|
|||
#!/usr/bin/env Rscript
|
||||
#source("~/git/LSHTM_analysis/config/alr.R")
|
||||
source("~/git/LSHTM_analysis/config/embb.R")
|
||||
#source("~/git/LSHTM_analysis/config/katg.R")
|
||||
#source("~/git/LSHTM_analysis/config/gid.R")
|
||||
#source("~/git/LSHTM_analysis/config/pnca.R")
|
||||
#source("~/git/LSHTM_analysis/config/rpob.R")
|
||||
|
||||
# get plottting dfs
|
||||
source("~/git/LSHTM_analysis/scripts/plotting/get_plotting_dfs.R")
|
||||
source("~/git/LSHTM_analysis/scripts/plotting/plotting_colnames.R")
|
||||
####################################################
|
||||
|
||||
# ggpairs wrapper
|
||||
|
||||
my_gg_pairs=function(plot_df){
|
||||
ggpairs(plot_df, columns = 1:(ncol(plot_df)-1),
|
||||
upper = list(continuous = wrap('cor',
|
||||
method = "spearman",
|
||||
title="ρ",
|
||||
digits=2,
|
||||
title_args=c(colour="black")
|
||||
)
|
||||
),
|
||||
lower = list(
|
||||
continuous = wrap("points", alpha = 0.7, size=0.5),
|
||||
combo = wrap("dot", alpha = 0.7, size=0.5)
|
||||
),
|
||||
aes(colour = factor(ifelse(plot_df$dst_mode==0, "S", "R")), alpha = 0.5),
|
||||
title="Stability") +
|
||||
|
||||
scale_colour_manual(values = c("red", "blue")) +
|
||||
scale_fill_manual(values = c("red", "blue")) +
|
||||
theme(
|
||||
text = element_text(size=12, face="bold")
|
||||
)
|
||||
}
|
||||
|
||||
|
||||
#=======
|
||||
# output
|
||||
#=======
|
||||
outdir_images = paste0("~/git/Writing/thesis/images/results/", tolower(gene), "/")
|
||||
|
||||
#=======
|
||||
# Input
|
||||
#=======
|
||||
merged_df3 = as.data.frame(merged_df3)
|
||||
corr_plotdf = corr_data_extract(merged_df3
|
||||
, gene = gene
|
||||
, drug = drug
|
||||
, extract_scaled_cols = F)
|
||||
colnames(corr_plotdf)
|
||||
|
||||
if (all(colnames(corr_df_m3_f) == colnames(corr_plotdf))){
|
||||
cat("PASS: corr plot colnames match for dashboard")
|
||||
}else{
|
||||
stop("Abort: corr plot colnames DO NOT match for dashboard")
|
||||
}
|
||||
|
||||
#corr_plotdf = corr_df_m3_f #for downstream code
|
||||
|
||||
aff_dist_cols = colnames(corr_plotdf)[grep("Dist", colnames(corr_plotdf))]
|
||||
aff_dist_cols
|
||||
|
||||
|
||||
static_cols = c("Log10(MAF)"
|
||||
, "Log10(OR)"
|
||||
#, "-Log10(P)"
|
||||
)
|
||||
|
||||
#================
|
||||
# stability
|
||||
#================
|
||||
#affinity_dist_colnames# lIg DIst and ppi Di
|
||||
corr_ps_colnames = c(static_cols
|
||||
, "DUET"
|
||||
, "FoldX"
|
||||
, "DeepDDG"
|
||||
, "Dynamut2"
|
||||
, aff_dist_cols
|
||||
, "dst_mode")
|
||||
|
||||
if (all(corr_ps_colnames%in%colnames(corr_plotdf))){
|
||||
cat("PASS: all colnames exist for correlation")
|
||||
}else{
|
||||
stop("Abort: all colnames DO NOT exist for correlation")
|
||||
}
|
||||
corr_df_ps = corr_plotdf[, corr_ps_colnames]
|
||||
complete_obs_ps = nrow(corr_df_ps) - sum(is.na(corr_df_ps$`Log(OR)`))
|
||||
cat("\nComplete muts for Conservation for", gene, ":", complete_obs_ps)
|
||||
|
||||
color_coln = which(colnames(corr_df_ps) == "dst_mode")
|
||||
#end = which(colnames(corr_df_ps) == drug)
|
||||
#ncol_omit = 2
|
||||
#corr_end = end-ncol_omit
|
||||
corr_end = color_coln-1
|
||||
|
||||
#------------------------
|
||||
# Output: stability corrP
|
||||
#------------------------
|
||||
corr_psP = paste0(outdir_images
|
||||
,tolower(gene)
|
||||
,"_corr_stability.svg" )
|
||||
|
||||
cat("Corr plot stability with coloured dots:", corr_psP)
|
||||
svg(corr_psP, width = 15, height = 15)
|
||||
|
||||
my_corr_pairs(corr_data_all = corr_df_ps
|
||||
, corr_cols = colnames(corr_df_ps[1:corr_end])
|
||||
, corr_method = "spearman"
|
||||
, colour_categ_col = colnames(corr_df_ps[color_coln]) #"dst_mode"
|
||||
, categ_colour = c("red", "blue")
|
||||
, density_show = F
|
||||
, hist_col = "coral4"
|
||||
, dot_size = 1.6
|
||||
, ats = 1.5
|
||||
, corr_lab_size =2.5
|
||||
, corr_value_size = 1)
|
||||
|
||||
dev.off()
|
||||
#===============
|
||||
# CONSERVATION
|
||||
#==============
|
||||
corr_conservation_cols = c( static_cols
|
||||
, "ConSurf"
|
||||
, "SNAP2"
|
||||
, "PROVEAN"
|
||||
, aff_dist_cols
|
||||
, "dst_mode"
|
||||
, drug)
|
||||
|
||||
if (all(corr_conservation_cols%in%colnames(corr_plotdf))){
|
||||
cat("PASS: all colnames exist for ConSurf-correlation")
|
||||
}else{
|
||||
stop("Abort: all colnames DO NOT exist for ConSurf-correlation")
|
||||
}
|
||||
|
||||
corr_df_cons = corr_plotdf[, corr_conservation_cols]
|
||||
complete_obs_cons = nrow(corr_df_cons) - sum(is.na(corr_df_cons$`Log(OR)`))
|
||||
cat("\nComplete muts for Conservation for", gene, ":", complete_obs_cons)
|
||||
|
||||
color_coln = which(colnames(corr_df_cons) == "dst_mode")
|
||||
# end = which(colnames(corr_df_cons) == drug)
|
||||
# ncol_omit = 2
|
||||
# corr_end = end-ncol_omit
|
||||
corr_end = color_coln-1
|
||||
|
||||
|
||||
#---------------------------
|
||||
# Output: Conservation corrP
|
||||
#----------------------------
|
||||
corr_consP = paste0(outdir_images
|
||||
,tolower(gene)
|
||||
,"_corr_conservation.svg" )
|
||||
|
||||
cat("Corr plot conservation coloured dots:", corr_consP)
|
||||
svg(corr_consP, width = 10, height = 10)
|
||||
|
||||
my_corr_pairs(corr_data_all = corr_df_cons
|
||||
, corr_cols = colnames(corr_df_cons[1:corr_end])
|
||||
, corr_method = "spearman"
|
||||
, colour_categ_col = colnames(corr_df_cons[color_coln]) #"dst_mode"
|
||||
, categ_colour = c("red", "blue")
|
||||
, density_show = F
|
||||
, hist_col = "coral4"
|
||||
, dot_size =1.1
|
||||
, ats = 1.5
|
||||
, corr_lab_size = 1.8
|
||||
, corr_value_size = 1)
|
||||
|
||||
dev.off()
|
||||
|
||||
#####################################################
|
||||
#DistCutOff = 10
|
||||
#LigDist_colname # = "ligand_distance" # from globals
|
||||
#ppi2Dist_colname = "interface_dist"
|
||||
#naDist_colname = "TBC"
|
||||
#####################################################
|
||||
|
||||
#================
|
||||
# ligand affinity
|
||||
#================
|
||||
corr_df_lig = corr_plotdf[corr_plotdf["Lig-Dist"]<DistCutOff,]
|
||||
|
||||
corr_lig_colnames = c(static_cols
|
||||
, "mCSM-lig"
|
||||
, "mmCSM-lig"
|
||||
, "dst_mode")
|
||||
#, drug)
|
||||
|
||||
if (all(corr_lig_colnames%in%colnames(corr_plotdf))){
|
||||
cat("PASS: all colnames exist for Lig-correlation")
|
||||
}else{
|
||||
stop("Abort: all colnames DO NOT exist for Lig-correlation")
|
||||
}
|
||||
|
||||
corr_df_lig = corr_plotdf[, corr_lig_colnames]
|
||||
complete_obs_lig = nrow(corr_df_lig) - sum(is.na(corr_df_lig$`Log(OR)`))
|
||||
cat("\nComplete muts for lig affinity for", gene, ":", complete_obs_lig)
|
||||
|
||||
color_coln = which(colnames(corr_df_lig) == "dst_mode")
|
||||
# end = which(colnames(corr_df_lig) == drug)
|
||||
# ncol_omit = 2
|
||||
# corr_end = end-ncol_omit
|
||||
corr_end = color_coln-1
|
||||
|
||||
#------------------------
|
||||
# Output: ligand corrP
|
||||
#------------------------
|
||||
corr_ligP = paste0(outdir_images
|
||||
,tolower(gene)
|
||||
,"_corr_lig.svg" )
|
||||
|
||||
cat("Corr plot affinity with coloured dots:", corr_ligP)
|
||||
svg(corr_ligP, width = 10, height = 10)
|
||||
|
||||
my_corr_pairs(corr_data_all = corr_df_lig
|
||||
, corr_cols = colnames(corr_df_lig[1:corr_end])
|
||||
, corr_method = "spearman"
|
||||
, colour_categ_col = colnames(corr_df_lig[color_coln]) #"dst_mode"
|
||||
, categ_colour = c("red", "blue")
|
||||
, density_show = F
|
||||
, hist_col = "coral4"
|
||||
, dot_size = 2
|
||||
, ats = 1.5
|
||||
, corr_lab_size =3
|
||||
, corr_value_size = 1)
|
||||
dev.off()
|
||||
####################################################
|
||||
#================
|
||||
# ppi2 affinity
|
||||
#================
|
||||
|
||||
if (tolower(gene)%in%geneL_ppi2){
|
||||
|
||||
corr_df_ppi2 = corr_plotdf[corr_plotdf["PPI-Dist"]<DistCutOff,]
|
||||
|
||||
corr_ppi2_colnames = c(static_cols
|
||||
, "mCSM-PPI2"
|
||||
, "dst_mode"
|
||||
, drug)
|
||||
|
||||
if (all(corr_ppi2_colnames%in%colnames(corr_plotdf))){
|
||||
cat("PASS: all colnames exist for mcsm-ppi2 correlation")
|
||||
}else{
|
||||
stop("Abort: all colnames DO NOT exist for mcsm-ppi2 correlation")
|
||||
}
|
||||
|
||||
corr_df_ppi2 = corr_plotdf[, corr_ppi2_colnames]
|
||||
complete_obs_ppi2 = nrow(corr_df_ppi2) - sum(is.na(corr_df_ppi2$`Log(OR)`))
|
||||
cat("\nComplete muts for ppi2 affinity for", gene, ":", complete_obs_ppi2)
|
||||
|
||||
color_coln = which(colnames(corr_df_ppi2) == "dst_mode")
|
||||
# end = which(colnames(corr_df_ppi2) == drug)
|
||||
# ncol_omit = 2
|
||||
# corr_end = end-ncol_omit
|
||||
corr_end = color_coln-1
|
||||
|
||||
#------------------------
|
||||
# Output: ppi2 corrP
|
||||
#------------------------
|
||||
corr_ppi2P = paste0(outdir_images
|
||||
,tolower(gene)
|
||||
,"_corr_ppi2.svg" )
|
||||
|
||||
cat("Corr plot ppi2 with coloured dots:", corr_ppi2P)
|
||||
svg(corr_ppi2P, width = 10, height = 10)
|
||||
|
||||
my_corr_pairs(corr_data_all = corr_df_ppi2
|
||||
, corr_cols = colnames(corr_df_ppi2[1:corr_end])
|
||||
, corr_method = "spearman"
|
||||
, colour_categ_col = colnames(corr_df_ppi2[color_coln]) #"dst_mode"
|
||||
, categ_colour = c("red", "blue")
|
||||
, density_show = F
|
||||
, hist_col = "coral4"
|
||||
, dot_size = 2
|
||||
, ats = 1.5
|
||||
, corr_lab_size = 3
|
||||
, corr_value_size = 1)
|
||||
|
||||
dev.off()
|
||||
}
|
||||
|
||||
# FIXME: ADD distance
|
||||
#==================
|
||||
# mCSSM-NA affinity
|
||||
#==================
|
||||
#================
|
||||
# NA affinity
|
||||
#================
|
||||
if (tolower(gene)%in%geneL_na){
|
||||
corr_df_na = corr_df_na[corr_df_na["NA-Dist"]<DistCutOff,]
|
||||
|
||||
corr_na_colnames = c(static_cols
|
||||
, "mCSM-NA"
|
||||
, "dst_mode"
|
||||
, drug)
|
||||
|
||||
if (all(corr_na_colnames%in%colnames(corr_plotdf))){
|
||||
cat("PASS: all colnames exist for mcsm-NA-correlation")
|
||||
}else{
|
||||
stop("Abort: all colnames DO NOT exist for mcsm-NA-correlation")
|
||||
}
|
||||
|
||||
corr_na_colnames%in%colnames(corr_plotdf)
|
||||
corr_df_na = corr_plotdf[, corr_na_colnames]
|
||||
complete_obs_na = nrow(corr_df_na) - sum(is.na(corr_df_na$`Log(OR)`))
|
||||
cat("\nComplete muts for NA affinity for", gene, ":", complete_obs_na)
|
||||
|
||||
color_coln = which(colnames(corr_df_na) == "dst_mode")
|
||||
# end = which(colnames(corr_df_na) == drug)
|
||||
# ncol_omit = 2
|
||||
# corr_end = end-ncol_omit
|
||||
corr_end = color_coln-1
|
||||
|
||||
#------------------------
|
||||
# Output: mCSM-NA corrP
|
||||
#------------------------
|
||||
corr_naP = paste0(outdir_images
|
||||
,tolower(gene)
|
||||
,"_corr_na.svg" )
|
||||
|
||||
cat("Corr plot mCSM-NA with coloured dots:", corr_naP)
|
||||
|
||||
svg(corr_naP, width = 10, height = 10)
|
||||
my_corr_pairs(corr_data_all = corr_df_na
|
||||
, corr_cols = colnames(corr_df_na[1:corr_end])
|
||||
, corr_method = "spearman"
|
||||
, colour_categ_col = colnames(corr_df_na[color_coln]) #"dst_mode"
|
||||
, categ_colour = c("red", "blue")
|
||||
, density_show = F
|
||||
, hist_col = "coral4"
|
||||
, dot_size = 2
|
||||
, ats = 1.5
|
||||
, corr_lab_size = 3
|
||||
, corr_value_size = 1)
|
||||
|
||||
dev.off()
|
||||
}
|
||||
####################################################
|
||||
#===============
|
||||
#ggpairs:
|
||||
#================
|
||||
#corr_df_ps$dst_mode = ifelse(corr_df_cons$dst_mode=="1", "R", "S")
|
||||
corr_plotting_df = corr_df_ps
|
||||
|
||||
|
||||
svg('~/tmp/foo.svg',
|
||||
width=10,
|
||||
height=10,
|
||||
units="in",
|
||||
res=300)
|
||||
my_gg_pairs(corr_plotting_df)
|
||||
dev.off()
|
||||
|
||||
png('~/tmp/foo.png',
|
||||
width=10,
|
||||
height=10,
|
||||
units="in",
|
||||
res=300)
|
||||
my_gg_pairs(corr_plotting_df)
|
||||
dev.off()
|
||||
|
||||
|
||||
#
|
||||
49
scripts/plotting/plotting_thesis/gg_pairs.R
Normal file
49
scripts/plotting/plotting_thesis/gg_pairs.R
Normal file
|
|
@ -0,0 +1,49 @@
|
|||
# Tweak for layout, fonts, and text sizes.
|
||||
#svg('~/tmp/foo.svg', width=10, height=10, )
|
||||
|
||||
|
||||
# Set the width/height to inches for print. 300 dpi is reasonably ok for "draft"
|
||||
# output. To raise quality while preserving sanity, increase 'res' and
|
||||
# DO NOT alter font/point/line sizes
|
||||
|
||||
|
||||
|
||||
#- [X] Black text for "Corr:" or replace with Rho symbol
|
||||
#- [X] 0/1 == R/S
|
||||
#- [X] "rho" symbol instead of "Corr:" text
|
||||
#- [X] Dot size a bit smaller
|
||||
#- [X] Plot lines slightly thinner
|
||||
#
|
||||
#
|
||||
png('~/tmp/foo.png',
|
||||
width=10,
|
||||
height=10,
|
||||
units="in",
|
||||
res=300)
|
||||
#
|
||||
corr_plotting_df = corr_df_ps
|
||||
|
||||
|
||||
ggpairs(corr_plotting_df, columns = 1:(ncol(corr_plotting_df)-1),
|
||||
upper = list(continuous = wrap('cor',
|
||||
method = "spearman",
|
||||
title="ρ",
|
||||
digits=2,
|
||||
title_args=c(colour="black")
|
||||
)
|
||||
),
|
||||
lower = list(
|
||||
continuous = wrap("points", alpha = 0.7, size=0.5),
|
||||
combo = wrap("dot", alpha = 0.7, size=0.5)
|
||||
),
|
||||
aes(colour = factor(ifelse(corr_plotting_df$dst_mode==0, "S", "R")), alpha = 0.5),
|
||||
title="Stability") +
|
||||
|
||||
scale_colour_manual(values = c("red", "blue")) +
|
||||
scale_fill_manual(values = c("red", "blue")) +
|
||||
theme(
|
||||
text = element_text(size=12, face="bold")
|
||||
)
|
||||
|
||||
dev.off()
|
||||
#Check all plots with LSHTM_analysis/scripts/plotting/plotting_colnames.R
|
||||
144
scripts/plotting/plotting_thesis/gg_pairs_all.R
Normal file
144
scripts/plotting/plotting_thesis/gg_pairs_all.R
Normal file
|
|
@ -0,0 +1,144 @@
|
|||
source("~/git/LSHTM_analysis/config/embb.R")
|
||||
source("~/git/LSHTM_analysis/scripts/plotting/plotting_colnames.R")
|
||||
source("~/git/LSHTM_analysis/scripts/plotting/get_plotting_dfs.R")
|
||||
|
||||
my_gg_pairs=function(plot_df){
|
||||
ggpairs(plot_df,
|
||||
columns = 1:(ncol(plot_df)-1),
|
||||
upper = list(
|
||||
continuous = wrap('cor',
|
||||
method = "spearman",
|
||||
title="ρ",
|
||||
digits=2,
|
||||
justify_labels = "left",
|
||||
title_args=c(colour="black")
|
||||
)
|
||||
),
|
||||
lower = list(
|
||||
continuous = wrap("points",
|
||||
alpha = 0.7,
|
||||
size=0.5),
|
||||
combo = wrap("dot",
|
||||
alpha = 0.7,
|
||||
size=0.5)
|
||||
),
|
||||
aes(colour = factor(ifelse(plot_df$dst_mode==0,
|
||||
"S",
|
||||
"R") ),
|
||||
alpha = 0.5),
|
||||
title="Stability") +
|
||||
|
||||
scale_colour_manual(values = c("red", "blue")) +
|
||||
scale_fill_manual(values = c("red", "blue")) +
|
||||
theme(text = element_text(size=12,
|
||||
face="bold") )
|
||||
}
|
||||
|
||||
DistCutOff = 10
|
||||
|
||||
merged_df3 = as.data.frame(merged_df3)
|
||||
corr_plotdf = corr_data_extract(merged_df3
|
||||
, gene = gene
|
||||
, drug = drug
|
||||
, extract_scaled_cols = F)
|
||||
|
||||
aff_dist_cols = colnames(corr_plotdf)[grep("Dist", colnames(corr_plotdf))]
|
||||
static_cols = c("Log10(MAF)"
|
||||
, "Log10(OR)")
|
||||
|
||||
corr_ps_colnames = c(static_cols
|
||||
, "DUET"
|
||||
, "FoldX"
|
||||
, "DeepDDG"
|
||||
, "Dynamut2"
|
||||
, aff_dist_cols
|
||||
, "dst_mode")
|
||||
corr_df_ps = corr_plotdf[, corr_ps_colnames]
|
||||
complete_obs_ps = nrow(corr_df_ps) - sum(is.na(corr_df_ps$`Log(OR)`))
|
||||
color_coln = which(colnames(corr_df_ps) == "dst_mode")
|
||||
corr_end = color_coln-1
|
||||
|
||||
# Plot #1
|
||||
plot_corr_df_ps = my_gg_pairs(corr_df_ps)
|
||||
|
||||
|
||||
corr_conservation_cols = c( static_cols
|
||||
, "ConSurf"
|
||||
, "SNAP2"
|
||||
, "PROVEAN"
|
||||
, aff_dist_cols
|
||||
, "dst_mode"
|
||||
)
|
||||
|
||||
corr_df_cons = corr_plotdf[, corr_conservation_cols]
|
||||
complete_obs_cons = nrow(corr_df_cons) - sum(is.na(corr_df_cons$`Log(OR)`))
|
||||
color_coln = which(colnames(corr_df_cons) == "dst_mode")
|
||||
corr_end = color_coln-1
|
||||
|
||||
# Plot #2
|
||||
|
||||
#my_gg_pairs(corr_df_cons)
|
||||
plot_corr_df_cons = my_gg_pairs(corr_df_cons)
|
||||
|
||||
|
||||
corr_df_lig = corr_plotdf[corr_plotdf["Lig-Dist"]<DistCutOff,]
|
||||
corr_lig_colnames = c(static_cols
|
||||
, "mCSM-lig"
|
||||
, "mmCSM-lig"
|
||||
, "dst_mode")
|
||||
|
||||
corr_df_lig = corr_plotdf[, corr_lig_colnames]
|
||||
|
||||
complete_obs_lig = nrow(corr_df_lig) - sum(is.na(corr_df_lig$`Log(OR)`))
|
||||
color_coln = which(colnames(corr_df_lig) == "dst_mode")
|
||||
corr_end = color_coln-1
|
||||
# Plot #3
|
||||
|
||||
#my_gg_pairs(corr_df_lig)
|
||||
plot_corr_df_lig = my_gg_pairs(corr_df_lig)
|
||||
|
||||
corr_df_ppi2 = corr_plotdf[corr_plotdf["PPI-Dist"]<DistCutOff,]
|
||||
corr_ppi2_colnames = c(static_cols
|
||||
, "mCSM-PPI2"
|
||||
, "dst_mode"
|
||||
)
|
||||
corr_df_ppi2 = corr_plotdf[, corr_ppi2_colnames]
|
||||
complete_obs_ppi2 = nrow(corr_df_ppi2) - sum(is.na(corr_df_ppi2$`Log(OR)`))
|
||||
color_coln = which(colnames(corr_df_ppi2) == "dst_mode")
|
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corr_end = color_coln-1
|
||||
|
||||
# NOTE: DELETE LOG OR FROM CORRELATION PLOTS!!!!!
|
||||
# NOTE: ALSO MAYBE DELETE DISTANCES AS WELL
|
||||
# NOTE: http://ggobi.github.io/ggally/reference/ggally_cor.html
|
||||
|
||||
# "***" if the p-value is < 0.001
|
||||
# "**" if the p-value is < 0.01
|
||||
# "*" if the p-value is < 0.05
|
||||
# "." if the p-value is < 0.10
|
||||
# "" otherwise
|
||||
#
|
||||
|
||||
# Plot #4
|
||||
#my_gg_pairs(corr_df_ppi2)
|
||||
plot_corr_df_ppi2 = my_gg_pairs(corr_df_ppi2)
|
||||
|
||||
|
||||
# corr_df_na = corr_df_na[corr_df_na["NA-Dist"]<DistCutOff,]
|
||||
# corr_na_colnames = c(static_cols
|
||||
# , "mCSM-NA"
|
||||
# , "dst_mode"
|
||||
# )
|
||||
#
|
||||
# corr_df_na = corr_plotdf[, corr_na_colnames]
|
||||
# complete_obs_na = nrow(corr_df_na) - sum(is.na(corr_df_na$`Log(OR)`))
|
||||
# color_coln = which(colnames(corr_df_na) == "dst_mode")
|
||||
# corr_end = color_coln-1
|
||||
#
|
||||
# # Plot #5
|
||||
# #my_gg_pairs(corr_df_na)
|
||||
# plot_corr_df_na = my_gg_pairs(corr_df_na)
|
||||
|
||||
|
||||
cowplot::plot_grid(ggmatrix_gtable(plot_corr_df_ps),ggmatrix_gtable(plot_corr_df_cons),
|
||||
ggmatrix_gtable(plot_corr_df_lig),ggmatrix_gtable(plot_corr_df_ppi2),
|
||||
nrow=2, ncol=2, rel_heights = 7,7,3,3)
|
||||
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