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update order of ras gene plot
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@gwaybio
gwaybio committed Jul 7, 2017
1 parent ddbaf9c commit 01afdff
Showing 1 changed file with 33 additions and 31 deletions.
64 changes: 33 additions & 31 deletions scripts/viz/ras_summary_figures.R
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Original file line number Diff line number Diff line change
Expand Up @@ -67,9 +67,9 @@ pheatmap(t(prop_matrix * 100), scale = "none", cluster_rows = FALSE,
width = 8, height = 2)

# Plot heatmap without collapsing Ras genes
heat_ras_df <- heat_df %>% dplyr::select(c('NRAS_gain_y', "HRAS_gain_y",
"KRAS_gain_y", 'NRAS_y', 'HRAS_y',
'KRAS_y'))
heat_ras_df <- heat_df %>% dplyr::select(c("NRAS_gain_y", "HRAS_gain_y",
"KRAS_gain_y", "NRAS_y", "HRAS_y",
"KRAS_y"))
colnames(heat_ras_df) <- c("NRAS Gain", "HRAS Gain", "KRAS Gain",
"NRAS", "HRAS", "KRAS")
heat_ras_df <- as.data.frame(heat_ras_df)
Expand Down Expand Up @@ -229,7 +229,7 @@ ggplot(final_df, aes(Weight, ..count.., fill = Class)) +
scale_x_continuous(expand = c(0, 0), limits = c(0, 1)) +
scale_y_continuous(expand = c(0, 0)) + base_theme +
theme(legend.position = c(1.1, 0.65),
legend.background = element_rect(fill = alpha('white', 0)),
legend.background = element_rect(fill = alpha("white", 0)),
legend.text = element_text(size = 7),
plot.margin = unit(c(0.2, 1.5, 0, 0.1),"cm"),
axis.text.x = element_text(size = 9),
Expand Down Expand Up @@ -257,25 +257,25 @@ nuc_df <- mut_weight_df %>%

aa_df <- aa_df[order(aa_df$count, decreasing = TRUE),]
nuc_df <- nuc_df[order(nuc_df$count, decreasing = TRUE),]
write.table(aa_df, file = file.path(results_folder, 'tables',
'amino_acid_mutation_scores.tsv'),
sep = '\t', row.names = FALSE)
write.table(nuc_df, file = file.path(results_folder, 'tables',
'nucleotide_mutation_scores.tsv'),
sep = '\t', row.names = FALSE)
write.table(aa_df, file = file.path(results_folder, "tables",
"amino_acid_mutation_scores.tsv"),
sep = "\t", row.names = FALSE)
write.table(nuc_df, file = file.path(results_folder, "tables",
"nucleotide_mutation_scores.tsv"),
sep = "\t", row.names = FALSE)

# Plot summary distribution of variant classes prediction scores
braf_df <- final_df[complete.cases(final_df), ]
braf_df <- braf_df[braf_df$HGVSp == 'p.Val600Glu', ]
braf_df <- braf_df[braf_df$HGVSp == "p.Val600Glu", ]

braf_df$Disease <- dplyr::recode(braf_df$Disease,
"BLCA" = "Other", "CHOL" = "Other",
"GBM" = "Other", "HNSC" = "Other",
"KIRP" = "Other", "LGG" = "Other",
"READ" = "Other")

braf_plot_file <- file.path(results_folder, 'figures',
'brafv600e_distribution.svg')
braf_plot_file <- file.path(results_folder, "figures",
"brafv600e_distribution.svg")
braf_plot <- ggplot(braf_df, aes(Weight, fill = Disease)) +
geom_density(alpha = 0.4) + theme_bw() +
ylab("Density") + xlab("BRAFV600E Classifier Score")
Expand All @@ -292,9 +292,9 @@ ras_summary_count_df <- readr::read_tsv(ras_count_file,
"weight" = "d",
"total_status" = "c"))
ras_summary_count_df$copy_count <- factor(ras_summary_count_df$copy_count,
levels = c('0', '1', '2', '3','4',
'5', '6', '7', '8', '9',
'10'))
levels = c("0", "1", "2", "3","4",
"5", "6", "7", "8", "9",
"10"))
ras_summary_count_df$copy_count <-
dplyr::recode(ras_summary_count_df$copy_count, "6" = ">6", "7" = ">6",
"8" = ">6", "9" = ">6", "10" = ">6")
Expand All @@ -310,7 +310,7 @@ cop_ras_count <- ras_summary_count_df %>% group_by(copy_count) %>% tally()

# Combine to get summary tables
mut_sum <- dplyr::inner_join(mut_ras_count, mut_ras_prop, by = "mutation_count")
cop_sum <- dplyr::inner_join(cop_ras_count, cop_ras_prop, by = 'copy_count')
cop_sum <- dplyr::inner_join(cop_ras_count, cop_ras_prop, by = "copy_count")

med_weight <- median(ras_summary_count_df$weight)

Expand All @@ -330,7 +330,7 @@ mut <- ggplot(ras_summary_count_df, aes(x = mutation_count, y = weight)) +
scale_fill_manual(name = "RAS Status", values = c("#3B9AB2", "#F2300F"),
labels = c("0" = "Wild-Type", "1" = "Hyperactive")) +
geom_text(data = mut_sum, aes(x = mutation_count, y = 1.06,
label = paste0(n, '\n', mean_ras))) +
label = paste0(n, "\n", mean_ras))) +
classifier_count_theme +
labs(list(x = "Number of Ras Pathway Mutations", y = "RAS Classifier Score"))

Expand All @@ -340,7 +340,7 @@ cop <- ggplot(ras_summary_count_df, aes(x = copy_count, y = weight)) +
scale_fill_manual(name = "RAS Status", values = c("#3B9AB2", "#F2300F"),
labels = c("0" = "Wild-Type", "1" = "Hyperactive")) +
geom_text(data = cop_sum, aes(x = copy_count, y = 1.06,
label = paste0(n, '\n', mean_ras))) +
label = paste0(n, "\n", mean_ras))) +
classifier_count_theme +
labs(list(x = "Number of Ras Pathway Copy Number Events",
y = "RAS Classifier Score"))
Expand All @@ -364,23 +364,25 @@ auprc_violin <- ggplot(metric_ranks, aes(y = AUPRC, x = paste(ras),
fill = paste(ras))) +
geom_violin() +
theme(legend.position = "none") +
xlab('Ras Pathway Status')
xlab("") +
scale_x_discrete(labels = c("0" = "Other", "1" = "Ras Pathway Genes"))

auroc_violin <- ggplot(metric_ranks, aes(y = AUROC, x = paste(ras),
fill = paste(ras))) +
geom_violin() +
theme(legend.position = "none") +
geom_hline(yintercept = 0.5, linetype = "dashed") +
xlab('Ras Pathway Status')
xlab("") +
scale_x_discrete(labels = c("0" = "Other", "1" = "Ras Pathway Genes"))

auprc_plot <- ggplot(metric_ranks, aes(x = `AUPRC Rank`, y = AUPRC)) +
geom_point(color = 'darkgrey') +
geom_point(data = metric_ranks[metric_ranks$ras == 1, ], color = 'red')
geom_point(color = "darkgrey") +
geom_point(data = metric_ranks[metric_ranks$ras == 1, ], color = "red")

auroc_plot <- ggplot(metric_ranks, aes(x = `AUROC Rank`, y = AUROC)) +
geom_point(color = 'darkgrey') +
geom_point(color = "darkgrey") +
geom_hline(yintercept = 0.5, linetype = "dashed") +
geom_point(data = metric_ranks[metric_ranks$ras == 1, ], color = 'red')
geom_point(data = metric_ranks[metric_ranks$ras == 1, ], color = "red")

# Get the top genes by both metrics
top_auprc_genes <- metric_ranks[order(metric_ranks$`AUPRC Rank`), 1:2]
Expand All @@ -395,16 +397,16 @@ auroc_plot <- auroc_plot +
annotation_custom(top_auroc_table_grob, xmin = 10000,
xmax = 15000, ymin = 0.6, ymax = 0.95)

auprc_distribution_fig <- file.path(results_folder, 'figures',
'auprc_distribution.svg')
auprc_distribution_fig <- file.path(results_folder, "figures",
"auprc_distribution.svg")

svg(auprc_distribution_fig, width = 11.5, height = 7.5)
plot_grid(auprc_violin, auprc_plot, align = "h", ncol = 2)
plot_grid(auprc_plot, auprc_violin, align = "h", ncol = 2)
dev.off()

auroc_distribution_fig <- file.path(results_folder, 'figures',
'auroc_distribution.svg')
auroc_distribution_fig <- file.path(results_folder, "figures",
"auroc_distribution.svg")

svg(auroc_distribution_fig, width = 11, height = 7.5)
plot_grid(auroc_violin, auroc_plot, align = "h", ncol = 2)
plot_grid(auroc_plot, auroc_violin, align = "h", ncol = 2)
dev.off()

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