From 6ca7d934b7de7cb8a8eaf9e2d0a1b77d65515479 Mon Sep 17 00:00:00 2001 From: Princy Parsana Date: Tue, 12 Feb 2019 16:57:30 -0500 Subject: [PATCH] new supp figures added - wgcna comparisons, fnr --- publication_figures/src/makefig3old.R | 68 +++++++++ publication_figures/src/supp_fig13.R | 86 +++++++++++ publication_figures/src/supp_fig14.R | 136 ++++++++++++++++++ publication_figures/src/supp_fig8.R | 71 +++++++++ .../suppfig15/average_module_size.png | Bin 0 -> 64320 bytes publication_figures/suppfig15/gray_modules.R | 44 ++++++ .../suppfig15/make_suppfig15.R | 9 ++ publication_figures/suppfig15/module_size.R | 55 +++++++ .../suppfig15/num_genes_gray_modules.png | Bin 0 -> 71284 bytes publication_figures/suppfig15/num_modules.R | 45 ++++++ publication_figures/suppfig15/num_modules.png | Bin 0 -> 215313 bytes .../suppfig15/wgcna_module_size.Rds | Bin 0 -> 50794 bytes publication_figures/suppfig5/make_suppfig5.R | 80 +++++++++++ publication_figures/suppfig5/suppfig5.pdf | Bin 0 -> 15373 bytes 14 files changed, 594 insertions(+) create mode 100755 publication_figures/src/makefig3old.R create mode 100644 publication_figures/src/supp_fig13.R create mode 100644 publication_figures/src/supp_fig14.R create mode 100755 publication_figures/src/supp_fig8.R create mode 100644 publication_figures/suppfig15/average_module_size.png create mode 100755 publication_figures/suppfig15/gray_modules.R create mode 100755 publication_figures/suppfig15/make_suppfig15.R create mode 100755 publication_figures/suppfig15/module_size.R create mode 100644 publication_figures/suppfig15/num_genes_gray_modules.png create mode 100755 publication_figures/suppfig15/num_modules.R create mode 100644 publication_figures/suppfig15/num_modules.png create mode 100755 publication_figures/suppfig15/wgcna_module_size.Rds create mode 100644 publication_figures/suppfig5/make_suppfig5.R create mode 100644 publication_figures/suppfig5/suppfig5.pdf diff --git a/publication_figures/src/makefig3old.R b/publication_figures/src/makefig3old.R new file mode 100755 index 0000000..3a00f29 --- /dev/null +++ b/publication_figures/src/makefig3old.R @@ -0,0 +1,68 @@ +library(cowplot) +library(ggplot2) +theme_set(theme_cowplot(font_size=9)) +lambda <- seq(0.3,1.0, length.out = 50) +cat.plot <- c("uncorrected", "RIN", "multi-covariate", "PC") +# select category to plot +select_category <- function(category_name, pr_table){ + pr_table <- pr_table[which(pr_table$type %in% category_name),] + pr_table$type <- factor(pr_table$type, levels = category_name) + pr_table$lambda <- rep(lambda,length(category_name)) + pr_table +} +plot.thyroid <- readRDS("/work-zfs/abattle4/parsana/networks_correction/results/PR/pr_density_glasso_canonical_thyroid.Rds") +plot.thyroid <- select_category(cat.plot, plot.thyroid) +plot.thyroid <- ggplot(plot.thyroid, aes(x = lambda, y = 1-precision, colour = type)) + geom_point(size =0.3) + + xlab("lambda") + ylab("FDR")+ggtitle("Thyroid") +plot.lung <- readRDS("/work-zfs/abattle4/parsana/networks_correction/results/PR/pr_density_glasso_canonical_lung.Rds") +plot.lung <- select_category(cat.plot, plot.lung) +plot.lung <- ggplot(plot.lung, aes(x = lambda, y = 1-precision, colour = type)) + geom_point(size = 0.3) + + xlab("lambda") + ylab("FDR")+ggtitle("Lung") +plot.blood <- readRDS("/work-zfs/abattle4/parsana/networks_correction/results/PR/pr_density_glasso_canonical_blood.Rds") +plot.blood <- select_category(cat.plot, plot.blood) +plot.blood <- ggplot(plot.blood, aes(x = lambda, y = 1-precision, colour = type)) + geom_point(size = 0.3) + + xlab("lambda") + ylab("FDR")+ggtitle("Whole Blood") +plot.skin <- readRDS("/work-zfs/abattle4/parsana/networks_correction/results/PR/pr_density_glasso_canonical_skin.Rds") +plot.skin <- select_category(cat.plot, plot.skin) +plot.skin <- ggplot(plot.skin, aes(x = lambda, y = 1-precision, colour = type)) + geom_point(size = 0.3) + + xlab("lambda") + ylab("FDR")+ggtitle("skin") +plot.muscle <- readRDS("/work-zfs/abattle4/parsana/networks_correction/results/PR/pr_density_glasso_canonical_muscle.Rds") +plot.muscle <- select_category(cat.plot, plot.muscle) +plot.muscle <- ggplot(plot.muscle, aes(x = lambda, y = 1-precision, colour = type)) + geom_point(size = 0.3) + + xlab("lambda") + ylab("FDR")+ggtitle("muscle") +plot.subcutaneous <- readRDS("/work-zfs/abattle4/parsana/networks_correction/results/PR/pr_density_glasso_canonical_subcutaneous.Rds") +plot.subcutaneous <- select_category(cat.plot, plot.subcutaneous) +plot.subcutaneous <- ggplot(plot.subcutaneous, aes(x = lambda, y = 1-precision, colour = type)) + geom_point(size = 0.3) + + xlab("lambda") + ylab("FDR")+ggtitle("subcutaneous") +plot.artery <- readRDS("/work-zfs/abattle4/parsana/networks_correction/results/PR/pr_density_glasso_canonical_artery.Rds") +plot.artery <- select_category(cat.plot, plot.artery) +plot.artery <- ggplot(plot.artery, aes(x = lambda, y = 1-precision, colour = type)) + geom_point(size = 0.3) + + xlab("lambda") + ylab("FDR")+ggtitle("artery") +plot.nerve <- readRDS("/work-zfs/abattle4/parsana/networks_correction/results/PR/pr_density_glasso_canonical_nerve.Rds") +plot.nerve <- select_category(cat.plot, plot.nerve) +plot.nerve <- ggplot(plot.nerve, aes(x = lambda, y = 1-precision, colour = type)) + geom_point(size = 0.3) + + xlab("lambda") + ylab("FDR")+ggtitle("nerve") +############################### +fig3 <- plot_grid(plot.blood + xlim(0.3,1.0) + ylim(0, 1) + theme(legend.position="none"), + plot.lung + xlim(0.3,1.0) + ylim(0, 1) + theme(legend.position="none"), + # plot.muscle + xlim(0.3,1.0) + ylim(0, 1) + theme(legend.position="none"), + # plot.artery + xlim(0.3,1.0) + ylim(0, 1) + theme(legend.position="none"), + # plot.skin + xlim(0.3,1.0) + ylim(0, 1) + theme(legend.position="none"), + # plot.nerve + xlim(0.3,1.0) + ylim(0, 1) + theme(legend.position="none"), + # plot.subcutaneous + xlim(0.3,1.0) + ylim(0, 1) + theme(legend.position="none"), + plot.thyroid + xlim(0.3,1.0) + ylim(0, 1) + theme(legend.position="none"), + align = 'vh', + labels = c("a", "b", "c"), + hjust = -1, + nrow = 1 +) +legend <- get_legend(plot.lung + + theme(legend.key = element_rect(color = "black", linetype = "solid", size = 0.5), + legend.key.size = unit(0.3, "cm"), legend.key.height=unit(1.5,"line")) + + guides(colour = guide_legend(override.aes = list(size= 1)))) +fig3a <- plot_grid( fig3, legend, rel_widths = c(3, .4)) + +pdf("/work-zfs/abattle4/parsana/networks_correction/publication_figures/fig3.pdf", height = 2.5, width = 7.2) +print(fig3a) +dev.off() + diff --git a/publication_figures/src/supp_fig13.R b/publication_figures/src/supp_fig13.R new file mode 100644 index 0000000..180c686 --- /dev/null +++ b/publication_figures/src/supp_fig13.R @@ -0,0 +1,86 @@ +library(cowplot) +library(ggplot2) +library(parallel) +library(igraph) +library(reshape2) +theme_set(theme_cowplot(font_size=9)) + +create.igraph <- function(network){ + # network[which(network != 0)] <- 1 + diag(network) <- 0 + network[network!=0] <- 1 + graph_from_adjacency_matrix(network, mode = "undirected") +} + + +fn <- dir("../../networks/", recursive=T, full.names = T) +fn <- fn[grep("glasso", fn)] +fn <- fn[grep(paste("raw", "rin", "mc", "/pc/", sep = "|"), fn)] + +igraph_net <- mclapply(fn, function(x){ + print(x) + load(x) + net.igraph <- lapply(dat.net[1:10], function(eachNet){ + eachNet[lower.tri(eachNet)] <- t(eachNet)[lower.tri(eachNet)] + create.igraph(eachNet) + }) + net.igraph + }, mc.cores = 16) + +# igraph_net <- lapply(igraph_net, function(x,y){ +# y <- y +# this_net <- lapply(x, function(thisx, thisy){ +# V(thisx)$name <- thisy +# thisx +# }, y) +# this_net +# }, dat.gene.symbol$gene_symbol) + + +nodes_with_20 <- mclapply(igraph_net, function(x) + sapply(x, function(y) sum(degree(y) >=20)), mc.cores = 16) + +names(nodes_with_20) <- fn + +raw <- nodes_with_20[grep("raw", fn)] +rin <- nodes_with_20[grep("rin", fn)] +mc <- nodes_with_20[grep("mc", fn)] +pc <- nodes_with_20[grep("/pc/", fn)] + +hub_net <- melt(data.frame(raw, rin, mc, pc)) + +hub_net$tissue <- NA +hub_net$tissue[grep("Artery", hub_net$variable)] <- "Tibial Artery" +hub_net$tissue[grep("Blood", hub_net$variable)] <- "Whole Blood" +hub_net$tissue[grep("Lung", hub_net$variable)] <- "Lung" +hub_net$tissue[grep("Muscle", hub_net$variable)] <- "Muscle" +hub_net$tissue[grep("Subcutaneous", hub_net$variable)] <- "Adipose Subcutaneous" +hub_net$tissue[grep("Nerve", hub_net$variable)] <- "Nerve Tibial" +hub_net$tissue[grep("Thyroid", hub_net$variable)] <- "Thyroid" +hub_net$tissue[grep("Skin", hub_net$variable)] <- "Sun exposed Skin" + +hub_net$version <- NA +hub_net$version[grep("raw", hub_net$variable)] <- "Uncorrected" +hub_net$version[grep("rin", hub_net$variable)] <- "RIN" +hub_net$version[grep("mc", hub_net$variable)] <- "multi-covariate" +hub_net$version[grep("..pc.", hub_net$variable)] <- "PC" + + +select_category <- function(category_name, pr_table){ + pr_table <- pr_table[which(pr_table$version %in% category_name),] + pr_table$version <- factor(pr_table$version, levels = category_name) + pr_table +} + +which_version <- c("Uncorrected", "RIN", "multi-covariate", "PC") + +plot_this <- select_category(which_version, hub_net) + +thisplot <- ggplot(aes(y = value, x = tissue, fill = version), + data = plot_this)+ +geom_boxplot()+ ylab("# Nodes with >=20 neighbors")+ xlab("Tissue") + theme_classic()+theme(axis.text.x = element_text(angle = 90, hjust = 1)) + +pdf("../suppfig13.pdf", height = 7, width = 12) +print(thisplot) +dev.off() + diff --git a/publication_figures/src/supp_fig14.R b/publication_figures/src/supp_fig14.R new file mode 100644 index 0000000..a62b2fd --- /dev/null +++ b/publication_figures/src/supp_fig14.R @@ -0,0 +1,136 @@ +library(cowplot) +library(ggplot2) +theme_set(theme_cowplot(font_size=9)) +cutheights <- seq(0.9,1.0, length.out = 50) + +cat.plot <- c("uncorrected", "RIN", "multi-covariate", "PC") + +# select category to plot +select_category <- function(category_name, pr_table){ + pr_table <- pr_table[which(pr_table$type %in% category_name),] + pr_table$type <- factor(pr_table$type, levels = category_name) + pr_table$cutheights <- rep(cutheights,length(category_name)) + pr_table$fnr <- pr_table$false_negatives/ ((4978 * (4978 - 1))/2 - pr_table$density) + pr_table +} + +plot.thyroid <- readRDS("/work-zfs/abattle4/parsana/networks_correction/results/PR/pr_density_wgcna-signed_canonical_thyroid.Rds") +plot.thyroid <- select_category(cat.plot, plot.thyroid) +plot.thyroid <- ggplot(plot.thyroid, aes(x = cutheights, y = fnr, colour = type)) + geom_point(size =0.3) + + xlab("cut-heights") + ylab("FNR")+ggtitle("Thyroid") +plot.lung <- readRDS("/work-zfs/abattle4/parsana/networks_correction/results/PR/pr_density_wgcna-signed_canonical_lung.Rds") +plot.lung <- select_category(cat.plot, plot.lung) +plot.lung <- ggplot(plot.lung, aes(x = cutheights, y = fnr, colour = type)) + geom_point(size = 0.3) + + xlab("cut-heights") + ylab("FNR")+ggtitle("Lung") +plot.blood <- readRDS("/work-zfs/abattle4/parsana/networks_correction/results/PR/pr_density_wgcna-signed_canonical_blood.Rds") +plot.blood <- select_category(cat.plot, plot.blood) +plot.blood <- ggplot(plot.blood, aes(x = cutheights, y = fnr, colour = type)) + geom_point(size = 0.3) + + xlab("cut-heights") + ylab("FNR")+ggtitle("Whole Blood") +plot.skin <- readRDS("/work-zfs/abattle4/parsana/networks_correction/results/PR/pr_density_wgcna-signed_canonical_skin.Rds") +plot.skin <- select_category(cat.plot, plot.skin) +plot.skin <- ggplot(plot.skin, aes(x = cutheights, y = fnr, colour = type)) + geom_point(size = 0.3) + + xlab("cut-heights") + ylab("FNR")+ggtitle("skin") +plot.muscle <- readRDS("/work-zfs/abattle4/parsana/networks_correction/results/PR/pr_density_wgcna-signed_canonical_muscle.Rds") +plot.muscle <- select_category(cat.plot, plot.muscle) +plot.muscle <- ggplot(plot.muscle, aes(x = cutheights, y = fnr, colour = type)) + geom_point(size = 0.3) + + xlab("cut-heights") + ylab("FNR")+ggtitle("muscle") +plot.subcutaneous <- readRDS("/work-zfs/abattle4/parsana/networks_correction/results/PR/pr_density_wgcna-signed_canonical_subcutaneous.Rds") +plot.subcutaneous <- select_category(cat.plot, plot.subcutaneous) +plot.subcutaneous <- ggplot(plot.subcutaneous, aes(x = cutheights, y = fnr, colour = type)) + geom_point(size = 0.3) + + xlab("cut-heights") + ylab("FNR")+ggtitle("subcutaneous") +plot.artery <- readRDS("/work-zfs/abattle4/parsana/networks_correction/results/PR/pr_density_wgcna-signed_canonical_artery.Rds") +plot.artery <- select_category(cat.plot, plot.artery) +plot.artery <- ggplot(plot.artery, aes(x = cutheights, y = fnr, colour = type)) + geom_point(size = 0.3) + + xlab("cut-heights") + ylab("FNR")+ggtitle("artery") +plot.nerve <- readRDS("/work-zfs/abattle4/parsana/networks_correction/results/PR/pr_density_wgcna-signed_canonical_nerve.Rds") +plot.nerve <- select_category(cat.plot, plot.nerve) +plot.nerve <- ggplot(plot.nerve, aes(x = cutheights, y = fnr, colour = type)) + geom_point(size = 0.3) + + xlab("cut-heights") + ylab("FNR")+ggtitle("nerve") +############################### +fig2 <- plot_grid(plot.blood + xlim(0.9,1.0) + ylim(0.045, 0.050) + theme(legend.position="none"), + plot.lung + xlim(0.9,1.0) + ylim(0.045, 0.050) + theme(legend.position="none"), + # plot.muscle + xlim(0.9,1.0) + + theme(legend.position="none"), + # plot.artery + xlim(0.9,1.0) + + theme(legend.position="none"), + # plot.skin + xlim(0.9,1.0) + + theme(legend.position="none"), + # plot.nerve + xlim(0.9,1.0) + + theme(legend.position="none"), + # plot.subcutaneous + xlim(0.9,1.0) + + theme(legend.position="none"), + plot.thyroid + xlim(0.9,1.0) + ylim(0.045, 0.050) + theme(legend.position="none"), + align = 'vh', + labels = c("a", "b", "c"), + hjust = -1, + nrow = 1 +) +legend <- get_legend(plot.lung + + theme(legend.key = element_rect(color = "black", linetype = "solid", size = 0.5), + legend.key.size = unit(0.3, "cm"), legend.key.height=unit(1.5,"line")) + + guides(colour = guide_legend(override.aes = list(size= 1)))) +fig2a <- plot_grid( fig2, legend, rel_widths = c(3, .4)) + + +####################################### Fig 2b glasso ################################### +lambda <- seq(0.3,1.0, length.out = 50) +cat.plot <- c("uncorrected", "RIN", "multi-covariate", "PC") +# select category to plot +select_category <- function(category_name, pr_table){ + pr_table <- pr_table[which(pr_table$type %in% category_name),] + pr_table$type <- factor(pr_table$type, levels = category_name) + pr_table$lambda <- rep(lambda,length(category_name)) + pr_table$fnr <- pr_table$false_negatives/ ((4978 * (4978 - 1))/2 - pr_table$density) + pr_table +} +plot.thyroid <- readRDS("/work-zfs/abattle4/parsana/networks_correction/results/PR/pr_density_glasso_canonical_thyroid.Rds") +plot.thyroid <- select_category(cat.plot, plot.thyroid) +plot.thyroid <- ggplot(plot.thyroid, aes(x = lambda, y = fnr, colour = type)) + geom_point(size =0.3) + + xlab("lambda") + ylab("FNR")+ggtitle("Thyroid") +plot.lung <- readRDS("/work-zfs/abattle4/parsana/networks_correction/results/PR/pr_density_glasso_canonical_lung.Rds") +plot.lung <- select_category(cat.plot, plot.lung) +plot.lung <- ggplot(plot.lung, aes(x = lambda, y = fnr, colour = type)) + geom_point(size = 0.3) + + xlab("lambda") + ylab("FNR")+ggtitle("Lung") +plot.blood <- readRDS("/work-zfs/abattle4/parsana/networks_correction/results/PR/pr_density_glasso_canonical_blood.Rds") +plot.blood <- select_category(cat.plot, plot.blood) +plot.blood <- ggplot(plot.blood, aes(x = lambda, y = fnr, colour = type)) + geom_point(size = 0.3) + + xlab("lambda") + ylab("FNR")+ggtitle("Whole Blood") +plot.skin <- readRDS("/work-zfs/abattle4/parsana/networks_correction/results/PR/pr_density_glasso_canonical_skin.Rds") +plot.skin <- select_category(cat.plot, plot.skin) +plot.skin <- ggplot(plot.skin, aes(x = lambda, y = fnr, colour = type)) + geom_point(size = 0.3) + + xlab("lambda") + ylab("FNR")+ggtitle("skin") +plot.muscle <- readRDS("/work-zfs/abattle4/parsana/networks_correction/results/PR/pr_density_glasso_canonical_muscle.Rds") +plot.muscle <- select_category(cat.plot, plot.muscle) +plot.muscle <- ggplot(plot.muscle, aes(x = lambda, y = fnr, colour = type)) + geom_point(size = 0.3) + + xlab("lambda") + ylab("FNR")+ggtitle("muscle") +plot.subcutaneous <- readRDS("/work-zfs/abattle4/parsana/networks_correction/results/PR/pr_density_glasso_canonical_subcutaneous.Rds") +plot.subcutaneous <- select_category(cat.plot, plot.subcutaneous) +plot.subcutaneous <- ggplot(plot.subcutaneous, aes(x = lambda, y = fnr, colour = type)) + geom_point(size = 0.3) + + xlab("lambda") + ylab("FNR")+ggtitle("subcutaneous") +plot.artery <- readRDS("/work-zfs/abattle4/parsana/networks_correction/results/PR/pr_density_glasso_canonical_artery.Rds") +plot.artery <- select_category(cat.plot, plot.artery) +plot.artery <- ggplot(plot.artery, aes(x = lambda, y = fnr, colour = type)) + geom_point(size = 0.3) + + xlab("lambda") + ylab("FNR")+ggtitle("artery") +plot.nerve <- readRDS("/work-zfs/abattle4/parsana/networks_correction/results/PR/pr_density_glasso_canonical_nerve.Rds") +plot.nerve <- select_category(cat.plot, plot.nerve) +plot.nerve <- ggplot(plot.nerve, aes(x = lambda, y = fnr, colour = type)) + geom_point(size = 0.3) + + xlab("lambda") + ylab("FNR")+ggtitle("nerve") +############################### +fig3 <- plot_grid(plot.blood + xlim(0.3,1.0) + ylim(0.045, 0.050)+ theme(legend.position="none"), + plot.lung + xlim(0.3,1.0) + ylim(0.045, 0.050)+ theme(legend.position="none"), + # plot.muscle + xlim(0.3,1.0) + theme(legend.position="none"), + # plot.artery + xlim(0.3,1.0) + theme(legend.position="none"), + # plot.skin + xlim(0.3,1.0) + theme(legend.position="none"), + # plot.nerve + xlim(0.3,1.0) + theme(legend.position="none"), + # plot.subcutaneous + xlim(0.3,1.0) + theme(legend.position="none"), + plot.thyroid + xlim(0.3,1.0) + ylim(0.045, 0.050) + theme(legend.position="none"), + align = 'vh', + labels = c("a", "b", "c"), + hjust = -1, + nrow = 1 +) + +legend <- get_legend(plot.lung + + theme(legend.key = element_rect(color = "black", linetype = "solid", size = 0.5), + legend.key.size = unit(0.3, "cm"), legend.key.height=unit(1.5,"line")) + + guides(colour = guide_legend(override.aes = list(size= 1)))) +fig2b <- plot_grid( fig3, legend, rel_widths = c(3, .4)) + +pdf("../suppfig14.pdf", height = 5, width = 7.2) +plot_grid(fig2a, fig2b, nrow = 2) +dev.off() diff --git a/publication_figures/src/supp_fig8.R b/publication_figures/src/supp_fig8.R new file mode 100755 index 0000000..45e1cb9 --- /dev/null +++ b/publication_figures/src/supp_fig8.R @@ -0,0 +1,71 @@ +library(cowplot) +library(ggplot2) +theme_set(theme_cowplot(font_size=9)) +cutheights <- seq(0.9,1.0, length.out = 50) +cat.plot <- c("uncorrected", "RIN", "multi-covariate", "PC") + +# select category to plot +select_category <- function(category_name, pr_table){ + pr_table <- pr_table[which(pr_table$type %in% category_name),] + pr_table$type <- factor(pr_table$type, levels = category_name) + pr_table$cutheights <- rep(cutheights,length(category_name)) + pr_table +} +plot.thyroid <- readRDS("/work-zfs/abattle4/parsana/networks_correction/results/PR/pr_density_wgcna-signed_shared_thyroid.Rds") +plot.thyroid <- select_category(cat.plot, plot.thyroid) +plot.thyroid <- ggplot(plot.thyroid, aes(x = cutheights, y = 1-precision, colour = type)) + geom_point(size =0.3) + + xlab("cut-heights") + ylab("FDR")+ggtitle("Thyroid") +plot.lung <- readRDS("/work-zfs/abattle4/parsana/networks_correction/results/PR/pr_density_wgcna-signed_shared_lung.Rds") +plot.lung <- select_category(cat.plot, plot.lung) +plot.lung <- ggplot(plot.lung, aes(x = cutheights, y = 1-precision, colour = type)) + geom_point(size = 0.3) + + xlab("cut-heights") + ylab("FDR")+ggtitle("Lung") +plot.blood <- readRDS("/work-zfs/abattle4/parsana/networks_correction/results/PR/pr_density_wgcna-signed_shared_blood.Rds") +plot.blood <- select_category(cat.plot, plot.blood) +plot.blood <- ggplot(plot.blood, aes(x = cutheights, y = 1-precision, colour = type)) + geom_point(size = 0.3) + + xlab("cut-heights") + ylab("FDR")+ggtitle("Whole Blood") +plot.skin <- readRDS("/work-zfs/abattle4/parsana/networks_correction/results/PR/pr_density_wgcna-signed_shared_skin.Rds") +plot.skin <- select_category(cat.plot, plot.skin) +plot.skin <- ggplot(plot.skin, aes(x = cutheights, y = 1-precision, colour = type)) + geom_point(size = 0.3) + + xlab("cut-heights") + ylab("FDR")+ggtitle("skin") +plot.muscle <- readRDS("/work-zfs/abattle4/parsana/networks_correction/results/PR/pr_density_wgcna-signed_shared_muscle.Rds") +plot.muscle <- select_category(cat.plot, plot.muscle) +plot.muscle <- ggplot(plot.muscle, aes(x = cutheights, y = 1-precision, colour = type)) + geom_point(size = 0.3) + + xlab("cut-heights") + ylab("FDR")+ggtitle("muscle") +plot.subcutaneous <- readRDS("/work-zfs/abattle4/parsana/networks_correction/results/PR/pr_density_wgcna-signed_shared_subcutaneous.Rds") +plot.subcutaneous <- select_category(cat.plot, plot.subcutaneous) +plot.subcutaneous <- ggplot(plot.subcutaneous, aes(x = cutheights, y = 1-precision, colour = type)) + geom_point(size = 0.3) + + xlab("cut-heights") + ylab("FDR")+ggtitle("subcutaneous") +plot.artery <- readRDS("/work-zfs/abattle4/parsana/networks_correction/results/PR/pr_density_wgcna-signed_shared_artery.Rds") +plot.artery <- select_category(cat.plot, plot.artery) +plot.artery <- ggplot(plot.artery, aes(x = cutheights, y = 1-precision, colour = type)) + geom_point(size = 0.3) + + xlab("cut-heights") + ylab("FDR")+ggtitle("artery") +plot.nerve <- readRDS("/work-zfs/abattle4/parsana/networks_correction/results/PR/pr_density_wgcna-signed_shared_nerve.Rds") +plot.nerve <- select_category(cat.plot, plot.nerve) +plot.nerve <- ggplot(plot.nerve, aes(x = cutheights, y = 1-precision, colour = type)) + geom_point(size = 0.3) + + xlab("cut-heights") + ylab("FDR")+ggtitle("nerve") +############################### +legend <- get_legend(plot.lung + + theme(legend.key = element_rect(color = "black", linetype = "solid", size = 0.5), + legend.key.size = unit(0.3, "cm"), legend.key.height=unit(1.5,"line")) + + guides(colour = guide_legend(override.aes = list(size= 1)))) + + +fig2 <- plot_grid(plot.blood + xlim(0.9,1.0) + ylim(0.5, 1) + theme(legend.position="none"), + plot.lung + xlim(0.9,1.0) + ylim(0.5, 1) + theme(legend.position="none"), + plot.thyroid + xlim(0.9,1.0) + ylim(0.5, 1) + theme(legend.position="none"), + plot.muscle + xlim(0.9,1.0) + ylim(0.5, 1) + theme(legend.position="none"), + plot.artery + xlim(0.9,1.0) + ylim(0.5, 1) + theme(legend.position="none"), + plot.skin + xlim(0.9,1.0) + ylim(0.5, 1) + theme(legend.position="none"), + plot.nerve + xlim(0.9,1.0) + ylim(0.5, 1) + theme(legend.position="none"), + plot.subcutaneous + 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lapply(seq_len(ncol(modraw)), function(i) modraw[,i]) +modpc <- lapply(seq_len(ncol(modpc)), function(i) modpc[,i]) + +grayraw <- sapply(modraw, function(x) sapply(x, function(y) length(y)-1)) +graypc <- sapply(modpc, function(x) sapply(x, function(y) length(y)-1)) + +colnames(grayraw)<- colnames(modsize[[1]]) +colnames(graypc) <- colnames(modsize[[1]]) + +grayraw <- melt(grayraw[,c(1:5)]) +graypc <- melt(graypc[,c(1:5)]) + +grayraw$cutheights <- rep(seq(0.9,1.0,length.out = 50)[46:50], 5) +grayraw$Type <- "uncorrected" +graypc$cutheights <- rep(seq(0.9,1.0,length.out = 50)[46:50], 5) +graypc$Type <- "PC" +graypc$Tissue <- graypc$Var2 +grayraw$Tissue <- grayraw$Var2 + +graymerged <- rbind(grayraw, graypc) + +p <- ggplot(graymerged, aes(x = cutheights, y = value, col = Type, + shape = Tissue))+ + geom_line(aes(linetype= Type))+ + geom_point()+ + xlab("Cut-heights")+ + ylab("# of modules")+ + theme_classic() + +ggsave(filename = "num_modules.png", plot = p, width = 5, height = 4, units = 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b/publication_figures/suppfig5/make_suppfig5.R new file mode 100644 index 0000000..2e99b41 --- /dev/null +++ b/publication_figures/suppfig5/make_suppfig5.R @@ -0,0 +1,80 @@ +library(parallel) +library(recount) +library(ggplot2) +library(RColorBrewer) +library(reshape2) +library(cowplot) +#### test association of expression PCs with gc coefficients + + +load("/work-zfs/abattle4/parsana/networks_correction/data/raw_subset.Rdata") +load("/work-zfs/abattle4/parsana/networks_correction/data/pc_loadings.Rdata") + + gc.coeff <- lapply(dat.expr, function(x) x$gc) + # fit linear model + gc.pc <- mapply(function(p,q,r){ + lm.gc.fit <- lm(p[,1:r] ~ q) + lm.gc.fit + }, pc.loadings, gc.coeff, num.pc.estimates, SIMPLIFY = FALSE + ) + # extract p-values + gc.pc.pvals <- lapply(gc.pc, function(lm.gc.fit){ + pval <- sapply(summary(lm.gc.fit), function(x) x$coefficients[2,4]) + pval <- p.adjust(pval, method = "BH") + pval + }) + # extract percent variance explained/ R2 + r2.pcs <- lapply(gc.pc, function(lm.gc.fit){ + pval <- sapply(summary(lm.gc.fit), function(x) x$r.squared) + pval + }) + + # merge the lists to a matrix + r2.pcs <- sapply(r2.pcs, function(x) {length(x) <- 37; x}) + gc.pc.pvals <- sapply(gc.pc.pvals, function(x) {length(x) <- 37; x}) + rownames(r2.pcs) <- paste("PC",1:37,sep="") + rownames(gc.pc.pvals) <- paste("PC",1:37,sep="") + +# prepare data for plotting with ggplot2 +plot.r2 <- melt(r2.pcs) +plot.pvals <- melt(gc.pc.pvals) + + # set color pallete + # myPalette <- colorRampPalette(rev(brewer.pal(11, "Spectral")), space="Lab") + myPalette <- colorRampPalette(brewer.pal(9,'Blues'), space = "Lab") + + # set default font size + theme_set(theme_classic(base_size = 9)) + + # plot p-values + pval.fig <- ggplot(plot.pvals, aes(Var2, Var1)) + geom_tile(aes(fill = value), + colour = "white") + + geom_text(size = 2, aes(label = prettyNum(value, digits=3, width=4, format="fg") +)) + + scale_fill_gradientn(colours = rev(myPalette(10)[1:6]), limits = c(0,1) +, values = c(0, 0.005, 0.05, 0.1, 0.5, 1) +) + xlab("") + ylab("Principal Components") + theme(axis.text.x=element_text(colour="black", angle = 90), axis.text.y=element_text(colour="black")) + ggsave("pvals.png") + + # plot r2 + r2.fig <- ggplot(plot.r2, aes(Var2, Var1)) + geom_tile(aes(fill = value), + colour = "white") + + geom_text(size = 2, aes(label = prettyNum(value, digits=3, width=4, format="fg") +)) + + scale_fill_gradientn(colours = myPalette(10)[1:6], limits = c(0,1) + , values = c(0, 0.05, 0.1, 0.2, 0.3, 0.4, 1) +) + xlab("") + ylab("Principal Components") + theme(axis.text.x=element_text(colour="black", angle = 90), axis.text.y=element_text(colour="black")) + ggsave("r2.png") + +fig.out <- plot_grid(pval.fig + theme(legend.position="bottom", legend.direction = "horizontal"), + r2.fig + theme(legend.position="bottom", legend.direction="horizontal"), + align = 'vh', + labels = c("a", "b"), + hjust = -1, + nrow = 1 + ) + +pdf("suppfig5.pdf", height = 8.5, width = 7) +print(fig.out) 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