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---
output:
html_document: default
pdf_document: default
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE, cache = TRUE)
```
# Paper figures
## Setup required libraries and directories
```{r}
library(ggplot2)
library(ggrepel)
library(ggbeeswarm)
library(ggtext)
library(ggsci)
library(LSD)
library(dplyr)
library(corrplot)
library(ROCR)
library(ggiraphExtra)
library(RColorBrewer)
library(Hmisc)
library(reshape2)
library(circlize)
library(ComplexHeatmap)
library(ggridges)
dir.create("figures", showWarnings = FALSE)
```
## Figure 1
### DE volcano plot
```{r, eval = TRUE}
volcanoData = read.delim("results/S_cerevisiae/DESeq2/S_cerevisiae_merged_salt_lfc1_p05_results.tsv", stringsAsFactors = F)
genesInterest = c("ARO9", "GRE2", "CTT1", "ENA1", "ALD3", "STL1", "HSP12")
# Obtain data for plot
dataExt <- data.frame(gene = volcanoData$ensembl_gene_id,
geneExt = volcanoData$external_gene_name,
padj = volcanoData$padj,
lpadj = -log10(volcanoData$padj),
lfc = volcanoData$log2FoldChange)
# Remove rows that have NA values
dataExt <- na.omit(dataExt)
dataExt <- dataExt %>%
mutate(threshold = ifelse(dataExt$lfc>=1 & dataExt$padj <= 0.05, yes = "Upregulated", no = ifelse(dataExt$lfc<=-1 & dataExt$padj <= 0.05, yes = "Downregulated", no = "No change")))
# Changing -inf lpadj to 300
dataExt[is.infinite(dataExt$lpadj), "lpadj"] <- 300
# Annotation for sign genes and log2fc <= -1
down <- sum(dataExt$padj <= 0.05 & dataExt$lfc <= -1, na.rm = TRUE)
downAnn <- paste(paste("Downregulated:\n", down), "genes")
# Annotation for sign genes and log2fc >= 1
up <- sum(dataExt$padj <= 0.05 & dataExt$lfc >= 1, na.rm = TRUE)
upAnn <- paste(paste("Upregulated:\n", up), "genes")
p <- ggplot(dataExt, aes(x = lfc, y = lpadj)) +
geom_point(aes(color = factor(threshold)), size = 1.75, alpha = 0.8, na.rm = T) +
theme_bw(base_size = 9, base_family = "Arial") +
theme(legend.position = "none") +
theme(axis.title=element_text(size=9),
axis.text = element_text(colour = "black", size = 9),
plot.title = element_text(hjust = 0.5, face = "bold", size = 9)) +
xlab(expression(log[2]("Stress"/ "Basal"))) +
xlim(c((min(dataExt$lfc) - 0.5), (max(dataExt$lfc + 0.5)))) +
ylab(expression(-log[10]("adjusted p-value"))) +
geom_vline(xintercept = 1, colour = "grey42", linetype = "dashed") +
geom_vline(xintercept = -1, colour = "grey42", linetype = "dashed") +
geom_hline(yintercept = -log10(0.05), colour = "grey42", linetype = "dotdash") +
annotate(geom = "text",
label = downAnn,
fontface = 1,
family= theme_get()$text[["family"]],
size= 9*0.35, # To have pt in mm multiply by 0.35
x = -7.5, y = min(dataExt$lpadj) + 0.5, hjust = .5) +
annotate(geom = "text",
label = upAnn,
fontface = 1,
family= theme_get()$text[["family"]],
size= 9*0.35,
x = 6.5, y = min(dataExt$lpadj) + 0.5, hjust = .3) +
geom_text_repel(
data = subset(dataExt, geneExt %in% genesInterest),
aes(label = geneExt),
size = 9*0.35,
fontface = 'italic',
force = 10,
box.padding = unit(0.35, "lines"),
point.padding = unit(0.3, "lines")) +
scale_color_manual(values = c("Upregulated" = "#D75565",
"Downregulated" = "#6CA699",
"No change" = "gray")) +
ggtitle("Osmostress-responsive genes catalog")
# Plot and scale y-axis with log1p function for visualize better taking into account high values
figVolc <- p + scale_y_continuous(trans = "log1p") + scale_x_continuous(limits = c(-13,13))
figVolc
ggsave("figures/S_cerevisiae_merged_salt_lfc1_p05_volcano_plot.pdf", width = 2.9, height = 2.9, device = cairo_pdf)
```
## Figure S1
### PCA batch
```{r, eval=TRUE}
source("functions/PCABatchExt.R")
# Reading data
scores = read.delim("results/S_cerevisiae/DESeq2/S_cerevisiae_merged_salt_lfc1_p05_batchRemoved_pcagg_data.tsv",
stringsAsFactors = F,
row.names = 1)
# Fixing labels
scores$group = gsub("basal", "Basal", scores$group)
scores$group = gsub("salt", "Stress", scores$group)
scores$group = gsub("S_cerevisiae_Carme", " Studer *et al.,* 2016 (GSE80512)", scores$group)
scores$group = gsub("S_cerevisiae_H2A_T77", " This study #1", scores$group)
scores$group = gsub("S_cerevisiae_H4_mutants", " Viéitez *et al.,* 2020 (GSE130549)", scores$group)
scores$group = gsub("S_cerevisiae_Santi", " Silva *et al.,* 2017 (GSE98352)", scores$group)
scores$group = gsub("S_cerevisiae_Mariona_with_mutant", " This study #2", scores$group)
scores$group = factor(scores$group, levels = c("Basal: Silva *et al.,* 2017 (GSE98352)", "Basal: Studer *et al.,* 2016 (GSE80512)",
"Basal: Viéitez *et al.,* 2020 (GSE130549)", "Basal: This study #1",
"Basal: This study #2",
"Stress: Silva *et al.,* 2017 (GSE98352)", "Stress: Studer *et al.,* 2016 (GSE80512)",
"Stress: Viéitez *et al.,* 2020 (GSE130549)", "Stress: This study #1",
"Stress: This study #2"))
# Split groups
scores$grp = as.factor(unlist(lapply(strsplit(as.character(scores$group), ":"), function(x) return(x[1]))))
scores$grp2 = unlist(lapply(strsplit(as.character(scores$group), ":"), function(x) return(x[2])))
scores$grp2 = factor(scores$grp2, levels = c(" Silva *et al.,* 2017 (GSE98352)", " Studer *et al.,* 2016 (GSE80512)", " Viéitez *et al.,* 2020 (GSE130549)", " This study #1", " This study #2"))
# Execute function
ggPca <- PCABatchExt(x = scores,
groupName1 = "grp",
groupName2 = "grp2")
ggPca = ggPca + theme(legend.text = element_markdown(), legend.title = element_blank())
#Putting PC1Var and PC2Var manually. This is computed in DESeqQCResBatchExt script.
PC1Var = 96
PC2Var = 2
ggPca <- ggPca + xlab(paste0("PC1: ", PC1Var, "% variance")) + ylab(paste0("PC2: ", PC2Var, "% variance")) + guides(shape = FALSE)
ggsave(ggPca, filename = "figures/S_cerevisiae_merged_salt_lfc1_p05_batchRemoved_pcagg_fixed.pdf", width = 6.5, height = 2.5, device = cairo_pdf)
```
### Comparison with previous experiments
```{r, eval = TRUE}
fcPrevPlot = read.delim("results/S_cerevisiae/fcComparison_previousData.tab", stringsAsFactors = F)
cairo_pdf("figures/fcComparison_RNAseq_vs_Gasch_salt.pdf", width = 4.5, height = 4.5, family = "Arial")
par(mar = c(2.1, 2.1, 1.1, 1.1), ps = 9, tcl = -0.1, mgp = c(1.5, 0.1, 0))
comparisonplot(fcPrevPlot$RNASeq, fcPrevPlot$Gasch, cor = FALSE, xlim = c(-6,11), ylim = c(-4,6), main = "",
xlab = "RNA-seq catalog\n(log2 fold change)",
ylab = "Gasch et al., 2000\n(log2 fold change)")
dev.off()
cairo_pdf("figures/fcComparison_RNAseq_vs_tiling_salt.pdf", width = 4.5, height = 4.5, family = "Arial")
par(mar = c(2.1, 2.1, 1.1, 1.1), ps = 9, tcl = -0.1, mgp = c(1.5, 0.1, 0))
comparisonplot(fcPrevPlot$RNASeq, fcPrevPlot$Tilings, cor = FALSE, xlim = c(-6,11), ylim = c(-15, 20), main = "", cex.axis = 1,
xlab = "RNA-Seq catalog (log2 fold change)",
ylab = "Nadal-Ribelles et al., 2014 (log2 fold change)")
dev.off()
```
## Figure 2
### Heatmap of features
```{r, eval = TRUE}
# Load required function
source("functions/comparisonsHeatmapAnnotated.R")
# Read data
stress.properties.df <- read.delim("results/S_cerevisiae/stressConsensus_Properties_Full_Table.tsv", stringsAsFactors = F)
# Feature info data frame
prop.names.format <- read.delim("data/original_data/features_info.txt", stringsAsFactors = F)
# Properties we want to plot
properties.toPlot <- colnames(stress.properties.df[,5:ncol(stress.properties.df)])
# Heatmap summarizing all the comparisons
comparisonsHeatmapAnnotated(propertiesStressData = stress.properties.df,
properties = properties.toPlot,
columnDep = "salt",
random = 0,
sourceMolTable = prop.names.format,
outWidth = 4.5,
outHeight = 6,
outTable = "results/S_cerevisiae/medianValues_features_stressGroups.tab")
```
### Ridges plot of selected features
```{r, eval = TRUE}
# Filter unnecesary columns
stress.properties.df.f = stress.properties.df %>%
dplyr::select(-c(ensembl_gene_id, external_gene_name, hog1DepSaltExt))
stress.properties.df.f$salt = Hmisc::capitalize(stress.properties.df.f$salt)
stress.properties.melt = melt(stress.properties.df.f)
stress.properties.melt$feature = prop.names.format[match(stress.properties.melt$variable, prop.names.format$Property), "Property_formatted_short"]
stress.properties.melt$salt = factor(stress.properties.melt$salt, levels = c("Upregulated", "Downregulated", "Unresponsive"))
# Select features to be displayed in main figure
selectedFeatures = c("Broad conservation", "Number of transcription factors")
stress.properties.melt.sel = stress.properties.melt %>%
dplyr::filter(feature %in% selectedFeatures)
ggplot(stress.properties.melt.sel, aes(x = value, fill = salt, y = salt), color = salt) +
geom_density_ridges(alpha = 0.8) +
facet_wrap(~feature, nrow = 2, scales = "free") +
theme_bw(base_family = "Arial", base_size = 9) + theme(strip.text = element_text(face = "bold", size = 9),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
strip.background = element_blank(),
panel.border = element_rect(colour = "black"),
legend.position = "none",
axis.title=element_text(size=9),
axis.text = element_text(colour = "black", size = 9),
axis.text.y = element_blank(),
axis.ticks.y = element_blank(),
plot.title = element_text(hjust = 0.5, face = "bold", size = 9),
panel.spacing = unit(16, "pt")) +
scale_fill_manual(values = rev(c("gray", "#6CA699", "#D75565"))) +
xlab("") +
ylab("")
ggsave("figures/ridgesPlot_selectedProperties_salt.pdf", width = 2.2, height = 4, device = cairo_pdf)
```
## Figure S2
### Ridges plot of rest features
```{r, eval = TRUE}
# Plot rest figures in Supplement
stress.properties.melt.rest = stress.properties.melt %>%
dplyr::filter(!feature %in% selectedFeatures)
ggplot(stress.properties.melt.rest, aes(x = value, fill = salt, y = salt)) +
geom_density_ridges(alpha = 0.8) +
facet_wrap(~feature, ncol = 4, scales = "free") +
theme_bw(base_family = "Arial", base_size = 9) + theme(strip.text = element_text(face = "bold", size = 9),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
strip.background = element_blank(),
panel.border = element_rect(colour = "black"),
axis.title=element_text(size=9),
axis.text = element_text(colour = "black", size = 9),
axis.text.y = element_blank(),
axis.ticks.y = element_blank(),
plot.title = element_text(hjust = 0.5, face = "bold", size = 9),
legend.position = "none",
panel.spacing = unit(16, "pt"),
plot.margin = unit(c(5.1, 6.1, 5.1, 5.1), units = "points")) +
scale_fill_manual(values = rev(c("gray", "#6CA699", "#D75565"))) +
xlab("") +
ylab("")
ggsave("figures/ridgesPlot_restProperties_salt.pdf", width = 7.5, height = 10.5, device = cairo_pdf)
```
## Figure 3
### Multinomial logistic regression: ROC curve
```{r, eval = TRUE}
load("data/rdata/roc_curve_multinomial_scerevisiae_random_0.rda", verbose = TRUE)
# Specify the different classes
classes <- c("unresponsive", "downregulated", "upregulated")
pretty_colours <- c("gray", "#6CA699", "#D75565")
# Store auc
auc <- c()
cairo_pdf(paste0("figures/roc_multinomialLogistic_train_valid_salt_random_0.pdf"), width = 2.5, height = 2.5, family = "Arial")
par(mar = c(2.1, 2.1, 1.1, 1.1), ps = 9, tcl = -0.1, mgp = c(1, 0.1, 0))
# For each class
for (i in 1:length(classes)){
# Define which observations belong to class[i]
true_values <- ifelse(ValidSet[,"salt_2"]==classes[i],1,0)
# Assess the performance of classifier for class[i]
pred <- prediction(prediction_for_roc_curve[,classes[i]],true_values)
perf <- performance(pred, "tpr", "fpr")
if (i==1)
{
plot(perf,col=pretty_colours[i])
}
else
{
plot(perf,col=pretty_colours[i],add=TRUE)
}
# Calculate the AUC and print it to screen
auc.perf <- performance(pred, measure = "auc")
auc <- c(auc, unlist(auc.perf@y.values))
}
abline(coef = c(0, 1), lty = 2)
#legend("bottomright", legend = paste0(capitalize(classes), "; AUC: ", round(auc, 2)), col = pretty_colours, lty = 1, bty = "n")
dev.off()
```
### Multinomial logistic regression: AIC, leave-one-out approach
Change to random_400 if we want to plot the graph with n = 400 unresponsive genes
```{r, eval = TRUE}
raw.AIC.df <- read.delim("results/S_cerevisiae/dataAIC_salt_random_0.tsv", stringsAsFactors = F)
# Full model as annotation in the graph
raw.AIC.full <- raw.AIC.df[raw.AIC.df$Property=="full", "AIC"]
# Remove full model for clarity
raw.AIC.df.tP = raw.AIC.df[raw.AIC.df$Property!="full",]
# Order from max to min
propOrder = as.character(raw.AIC.df.tP[order(raw.AIC.df.tP$AIC), "Property_Formatted"])
raw.AIC.df.tP$Property_Formatted = factor(raw.AIC.df.tP$Property_Formatted, levels = propOrder)
ggplot(raw.AIC.df.tP, aes(x = AIC, y = Property_Formatted)) +
geom_point() +
geom_vline(xintercept = raw.AIC.full, colour = "red4", linetype = "dashed") +
theme_bw(base_size = 9, base_family = "Arial") +
theme(axis.text = element_text(colour = "black", size = 9)) +
xlab("AIC (leave-one-out approach)") +
ylab("")
ggsave("figures/pointOrdered_AIC_salt_random_0.pdf", width = 4.25, height = 2.25, device = cairo_pdf)
```
### Multinomial logistic regression: Coefficients, radial plot
```{r}
# Change to 400 if wanted
colorsRadial <- c("#6CA699", "#D75565")
multinom.toPlot <- read.delim("results/S_cerevisiae/multinomialTestSummary_scaled_salt_random_0.tab", stringsAsFactors = F, row.names = 1)
multinom.toPlot.radar <- multinom.toPlot[c("upregulated", "downregulated"), -1]
# Change order of GC and length features
multinom.toPlot.radar<- multinom.toPlot.radar[, c(1:4, 7,5,6,8,11,9,10,12,13)]
multinom.toPlot.radar = tibble::rownames_to_column(multinom.toPlot.radar, var = "stressGroup2")
prop.x.axis <- prop.names.format[match(colnames(multinom.toPlot.radar)[2:ncol(multinom.toPlot.radar)], prop.names.format$Property), "Property_formatted"]
prop.x.axis <- sub("Protein-Protein Interaction", "PPI", prop.x.axis)
prop.x.axis <- sub("transcription factors", "TFs", prop.x.axis)
ggRadar(data = multinom.toPlot.radar, aes(color = stressGroup2, fill = stressGroup2), rescale = F, size = 0) +
theme_bw(base_size = 16, base_family = "Arial") +
scale_x_discrete(labels = prop.x.axis) +
scale_colour_manual(values = colorsRadial) +
scale_fill_manual(values = colorsRadial) +
theme(axis.text = element_blank(),
axis.ticks = element_blank()) +
theme(legend.position = "none", panel.border = element_blank(),panel.grid = element_line(color = "grey", size = 1.1)) +
ggsave("figures/radarPlot_multinomCoefficients_random_0_wo_labels.pdf", width = 5, height = 5, device = cairo_pdf)
```
## Figure S3
### Heatmap of pairwise correlations
```{r, eval = TRUE}
load("data/rdata/pairwise_pearson_allFeatures.rda")
M <- cor_all$r
row.names(M) <- prop.names.format[match(row.names(M), prop.names.format$Property), "Property_formatted_short"]
colnames(M) <- prop.names.format[match(colnames(M), prop.names.format$Property), "Property_formatted_short"]
p_mat <- cor_all$P
row.names(p_mat) <- prop.names.format[match(row.names(p_mat), prop.names.format$Property), "Property_formatted_short"]
colnames(p_mat) <- prop.names.format[match(colnames(p_mat), prop.names.format$Property), "Property_formatted_short"]
col2 <- colorRampPalette(c("#67001F", "#B2182B", "#D6604D", "#F4A582",
"#FDDBC7", "#FFFFFF", "#D1E5F0", "#92C5DE",
"#4393C3", "#2166AC", "#053061"))
cairo_pdf("figures/properties_all_pairwise_pearson.pdf", width = 7, height = 7, family = "Arial", pointsize = 9)
corrplot.mixed(M, tl.pos = "lt", order = "alphabet", number.cex = 0.70, cex.axis = 1, number.digits = 1, upper = "color", lower = "number",lower.col = "black", upper.col = rev(col2(200)), tl.col = "black")
dev.off()
```
### Multinomial logistic regression: AIC of an univariable model
```{r, eval = TRUE}
univariate.aic <- read.delim("results/S_cerevisiae/dataAIC_univariate_salt_random_0.tsv", stringsAsFactors = F)
# Order from max to min
propOrder = as.character(univariate.aic[order(univariate.aic$AIC, decreasing = T), "Property_Formatted"])
univariate.aic$Property_Formatted = factor(univariate.aic$Property_Formatted, levels = propOrder)
ggplot(univariate.aic, aes(x = AIC, y = Property_Formatted)) +
geom_point() +
theme_bw(base_size = 9, base_family = "Arial") +
theme(axis.text = element_text(colour = "black", size = 9)) +
xlab("AIC (univariable model)") +
ylab("")
ggsave("figures/pointOrdered_AIC_univariate_salt_random_0.pdf", width = 3.9, height = 1.9, device = cairo_pdf)
```
### Multinomial logistic regression: AIC, leave-one-out approach (coexpression degree)
```{r, eval = TRUE}
raw.AIC.df <- read.delim("results/S_cerevisiae/dataAIC_salt_random_0_coexpsDegree.tsv", stringsAsFactors = F)
raw.AIC.full <- raw.AIC.df[raw.AIC.df$Property=="full", "AIC"]
# Remove full model for clarity
raw.AIC.df.tP = raw.AIC.df[raw.AIC.df$Property!="full",]
# Order from max to min
propOrder = as.character(raw.AIC.df.tP[order(raw.AIC.df.tP$AIC), "Property_Formatted"])
raw.AIC.df.tP$Property_Formatted = factor(raw.AIC.df.tP$Property_Formatted, levels = propOrder)
ggplot(raw.AIC.df.tP, aes(x = AIC, y = Property_Formatted)) +
geom_point() +
geom_vline(xintercept = raw.AIC.full, colour = "red4", linetype = "dashed") +
theme_bw(base_size = 9, base_family = "Arial") +
theme(axis.text = element_text(colour = "black", size = 9)) +
xlab("AIC (leave-one-out approach)") +
ylab("")
ggsave("figures/pointOrdered_AIC_salt_random_0_coexpsDegree.pdf", width = 3.9, height = 1.9, device = cairo_pdf)
```
### Random forest: ROC curve
```{r, eval = TRUE}
load("data/rdata/roc_curve_randomForest_scerevisiae_random_0.rda", verbose = TRUE)
# Specify the different classes
classes <- c("unresponsive", "downregulated", "upregulated")
# Store auc
auc <- c()
cairo_pdf(paste0("figures/roc_randomForest_train_valid_salt_random_0.pdf"), width = 2.5, height = 2.5, family = "Arial")
par(mar = c(2.1, 2.1, 1.1, 1.1), ps = 9, tcl = -0.1, mgp = c(1, 0.1, 0))
# For each class
for (i in 1:length(classes)){
# Define which observations belong to class[i]
true_values <- ifelse(ValidSet[,"salt_2"]==classes[i],1,0)
# Assess the performance of classifier for class[i]
pred <- prediction(prediction_for_roc_curve[,classes[i]],true_values)
perf <- performance(pred, "tpr", "fpr")
if (i==1)
{
plot(perf,col=pretty_colours[i])
}
else
{
plot(perf,col=pretty_colours[i],add=TRUE)
}
# Calculate the AUC and print it to screen
auc.perf <- performance(pred, measure = "auc")
auc <- c(auc, unlist(auc.perf@y.values))
}
abline(coef = c(0, 1), lty = 2)
#legend("bottomright", legend = paste0(capitalize(classes), "; AUC: ", round(auc, 2)), inset = 0.05, col = pretty_colours, lty = 1, cex = 1.2)
dev.off()
```
### Random forest: Mean decrease in accuracy
```{r, eval = TRUE}
imp.df.m = read.delim("results/S_cerevisiae/modelImportance_randomForestsalt_random_0.tab", stringsAsFactors = F, row.names = 1)
imp.df.m$Property_Formatted <- paste0(" ", prop.names.format[match(row.names(imp.df.m), prop.names.format$Property), "Property_formatted_short"], " ")
# Order from max to min
propOrder = as.character(imp.df.m[order(imp.df.m$MeanDecreaseAccuracy), "Property_Formatted"])
imp.df.m$Property_Formatted = factor(imp.df.m$Property_Formatted, levels = propOrder)
ggplot(imp.df.m, aes(x = MeanDecreaseAccuracy, y = Property_Formatted)) +
geom_point() +
theme_bw(base_size = 9, base_family = "Arial") +
theme(axis.text = element_text(colour = "black", size = 9)) +
xlab("Mean decrease in accuracy") +
ylab("")
ggsave("figures/pointOrdered_Mean_Decrease_Accuracy_salt_random_0.pdf", width = 4.0, height = 2, device = cairo_pdf)
```
## Figure 4
### Hog1 dependency boxplot
```{r, eval=TRUE}
mutantResPlot = read.table("results/S_cerevisiae/hog1_dependency/hog1Dependency_boxplot_data.tsv",
stringsAsFactors = FALSE,
sep = "\t",
check.names = FALSE,
header =TRUE)
# Change name of stress responsive annotation
mutantResPlot$stressGene <- ifelse(mutantResPlot$stressGene == "Upregulated",
yes = "Upregulated WT\n(log2FC >= 1)",
no = "Downregulated WT\n(log2FC <= -1)")
# Annotation for sign genes and log2fc <= -1
down <- as.integer(table(mutantResPlot$mutantDep)["Less upregulated"])
totalInd <- as.numeric(table(mutantResPlot$stressGene)[2])
downPerc <- round(as.integer(table(mutantResPlot$mutantDep)["Less upregulated"])/as.numeric(table(mutantResPlot$stressGene)[2]) * 100,2)
downAnn <- paste0("Less upregulated:\n", down, " of ", totalInd," (", downPerc, "%)")
# Annotation for sign genes and log2fc >= 1
up <- as.integer(table(mutantResPlot$mutantDep)["Less downregulated"])
totalRep <- as.numeric(table(mutantResPlot$stressGene)[1])
upPerc <- round(as.integer(table(mutantResPlot$mutantDep)["Less downregulated"])/as.numeric(table(mutantResPlot$stressGene)[1]) * 100,2)
upAnn <- paste0("Less downregulated:\n", up, " of ", totalRep," (", upPerc, "%)")
ggplot(data = mutantResPlot, aes(x = stressGene, y = lfc_interaction)) +
geom_quasirandom(aes(colour = mutantDep), alpha = 0.5) +
geom_boxplot(alpha = 0.5, colour = "#636363", outlier.shape = NA, width = 0.1) +
theme_classic(base_size = 9) +
theme(axis.text = element_text(colour = "black", size = 9),
axis.title = element_text(size = 9),
legend.position = "none") +
scale_color_manual(values = c("grey", "#3B9AB2", "#E1AF00")) +
annotate(geom = "text",
label = upAnn,
family= theme_get()$text[["family"]],
size= 9*0.35,
x = 1, y = min(mutantResPlot$lfc_interaction) + 10, hjust = .5) +
annotate(geom = "text",
label = downAnn,
family= theme_get()$text[["family"]],
size= 9*0.35,
x = 2.0, y = max(mutantResPlot$lfc_interaction) - 0.5, hjust = .3) +
geom_hline(yintercept = 0, colour = "black", linetype = "dashed") +
ylab(expression(paste(log[2]("Stress / Basal"), " HOG1 KO vs WT"))) +
xlab("")
ggsave(paste0("figures/boxplot_Hog1DependentGenes.pdf"), height = 2.5, width = 4)
```
### Logistic regression: AIC, leave-one-out approach
```{r, eval = TRUE}
raw.AIC.df <- read.delim("results/S_cerevisiae/dataAIC_upregulated_hog1DepSaltExt.tsv", stringsAsFactors = F)
# Store full model for annotation
raw.AIC.full <- raw.AIC.df[raw.AIC.df$Property == "full", "AIC"]
# Remove full model
raw.AIC.df.tP = raw.AIC.df[raw.AIC.df$Property!="full",]
# Order from max to min
propOrder = as.character(raw.AIC.df.tP[order(raw.AIC.df.tP$AIC), "Property_Formatted"])
raw.AIC.df.tP$Property_Formatted = factor(raw.AIC.df.tP$Property_Formatted, levels = propOrder)
ggplot(raw.AIC.df.tP, aes(x = AIC, y = Property_Formatted)) +
geom_point() +
geom_vline(xintercept = raw.AIC.full, colour = "red4", linetype = "dashed") +
theme_bw(base_size = 9, base_family = "Arial") +
theme(axis.text = element_text(colour = "black", size = 9)) +
xlab("AIC (leave-one-out approach)") +
ylab("")
ggsave("figures/pointOrdered_AIC_upregulated_hog1DepSaltExt.pdf", width = 4.25, height = 2.25, device = cairo_pdf)
```
### Logistic regression: Coefficients, volcano plot
```{r, eval = TRUE}
scaled.toPlot <- read.delim("results/S_cerevisiae/logisticTestSummary_scaled_upregulated_hog1DepSaltExt.tab", stringsAsFactors = F, row.names = 1)
scaled.toPlot <- scaled.toPlot[row.names(scaled.toPlot) != "(Intercept)",]
scaled.toPlot <- tibble::rownames_to_column(scaled.toPlot, var = "property")
colnames(scaled.toPlot)[5] <- "pvalue"
scaled.toPlot <- scaled.toPlot %>%
mutate(signif = ifelse(pvalue <= 0.05, yes = "Significant", no = "Non_significant"))
scaled.toPlot$Property_formatted <- prop.names.format[match(scaled.toPlot$property, prop.names.format$Property), "Property_formatted"]
scaled.toPlot$Molecule_of_property <- prop.names.format[match(scaled.toPlot$property, prop.names.format$Property), "Molecule"]
scaled.toPlot$Molecule_of_property <- factor(scaled.toPlot$Molecule_of_property, levels = c("DNA", "RNA", "Protein"))
ggplot(data = scaled.toPlot, aes(x = Estimate, y = -log10(pvalue))) +
geom_point(aes(fill = Molecule_of_property), shape = 21, size = 2) +
geom_hline(colour = "grey", linetype = "dashed", yintercept = -log10(0.05)) +
geom_vline(colour = "grey", linetype = "dashed", xintercept = 0) +
theme_bw(base_family = "Arial", base_size = 9) +
theme(legend.position = "bottom",
legend.title = element_blank(),
legend.text = element_text(size = 9, color = "black"),
axis.title = element_text(size = 9, color = "black"),
axis.text = element_text(size = 9, color = "black"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
strip.background = element_blank(),
panel.border = element_rect(colour = "black")) +
geom_text_repel(data = subset(scaled.toPlot, signif == "Significant"),
aes(label = Property_formatted),
size = 9*0.35,
box.padding = unit(1.5, "lines"),
point.padding = unit(0.3, "lines"),
segment.colour = "grey36",
segment.alpha = 0.5,
colour = "black",
force = 100) +
scale_fill_manual(values = c("dodgerblue4", "deepskyblue", "#B40F20")) +
ylab(expression(-log[10]("p-value"))) +
scale_y_continuous(trans = "log1p") +
xlab("Importance (Logistic regression coefficient)") +
ggsave("figures/logisticTestVolcanoPlot_upregulated_hog1DepSaltExt_scaled.pdf",
device = cairo_pdf,
width = 2.75,
height = 2.75)
```
## Figure S4
### Hog1 experiment PCA
```{r, eval = TRUE}
# Reading data
scores = read.delim("results/S_cerevisiae/hog1_dependency/hog1Dependency_pcagg_data.tsv",
stringsAsFactors = F,
row.names = 1)
# Fixing labels
scores$group = gsub("basal", "Basal", scores$group)
scores$group = gsub("salt", "Stress", scores$group)
scores$group = gsub("WT", " Wild-type", scores$group)
scores$group = gsub("HOG1_KO", " *HOG1* KO", scores$group)
scores$group = factor(scores$group, levels = c("Basal: Wild-type", "Basal: *HOG1* KO", "Stress: Wild-type", "Stress: *HOG1* KO"))
# x and y are 1st and 2nd component label is the sample name and colored by group
ggPca <- ggplot(data = scores, aes(x = PC1, y = PC2, colour = group)) +
theme_classic(base_size = 9, base_family = "Arial") +
geom_hline(yintercept = 0, colour = "gray65") +
geom_vline(xintercept = 0, colour = "gray65") +
geom_point(size = 2) +
# Scaling axes to take into account the min and max values of PC
scale_x_continuous(limits = c(min(scores$PC1 - 2), max(scores$PC1 + 2))) +
scale_y_continuous(limits = c(min(scores$PC2 - 2), max(scores$PC2 + 2))) +
scale_color_npg() +
# Eliminating legend and setting up appearence of x and y axis
theme(legend.position = "none",
legend.title = element_blank(),
axis.text = element_text(size = 9, colour = "black"),
axis.line.x = element_line(colour = "black"),
axis.line.y = element_line(colour = "black"),
axis.title = element_text(size = 9, colour = "black"))
# Putting PC1Var and PC2Var manually. This is computed in Hog1Dep genes analysis.
PC1Var = 85
PC2Var = 12
ggPca <- ggPca + xlab(paste0("PC1: ", PC1Var, "% variance")) + ylab(paste0("PC2: ", PC2Var, "% variance"))
ggsave(ggPca, filename = "figures/pca_Hog1WT_Hog1KO_basal_salt_pcagg.pdf", width = 3.5, height = 2.5, device = cairo_pdf)
```
### Hog1 basal volcano plot
```{r, eval=TRUE}
resBasal = read.delim("results/S_cerevisiae/hog1_dependency/results_basal_HOG1WT_vs_HOG1KO.tab", stringsAsFactors = F, sep = "\t")
# Add -log10 padj
resBasal$lpadj = -log10(resBasal$padj)
# Remove rows that have NA values
resBasal <- na.omit(resBasal)
# Modify dataset to add new column with significant genes
resBasal <- resBasal %>%
mutate(threshold = ifelse(abs(resBasal$log2FoldChange) >= 1 & resBasal$padj <= 0.05, yes = "Significantly changed", no = "Non significantly changed"))
# Annotation for sign genes and log2fc <= -1
#down <- sum(resBasal$padj <= 0.05 & resBasal$log2FoldChange <= -1, na.rm = TRUE)
#downAnn <- paste(paste("Downregulated:\n", down), "genes")
# Annotation for sign genes and log2fc >= 1
#up <- sum(resBasal$padj <= 0.05 & resBasal$log2FoldChange >= 1, na.rm = TRUE)
#upAnn <- paste(paste("Upregulated:\n", up), "gene")
genesInterest <- c("HOG1", "CWP1", "SED1", "PGU1")
p <- ggplot(resBasal, aes(x = log2FoldChange, y = lpadj)) +
geom_point(aes(color = factor(threshold)), size = 1.75, alpha = 0.8, na.rm = T) +
theme_bw(base_size = 9, base_family = "Arial") +
theme(legend.position = "none") +
theme(legend.title = element_blank()) +
theme(axis.title=element_text(size=9),
axis.text = element_text(colour = "black", size = 9)) +
xlab(expression(paste(log[2], "(", italic("HOG1"), "KO / ", italic("HOG1"), "WT)"))) +
xlim(c(min(resBasal$log2FoldChange), 3)) +
ylab(expression(-log[10]("adjusted p-value"))) +
geom_vline(xintercept = 1, colour = "grey42", linetype = "dashed") +
geom_vline(xintercept = -1, colour = "grey42", linetype = "dashed") +
geom_hline(yintercept = 1.3, colour = "grey42", linetype = "dotdash") +
#annotate(geom = "text",
# label = downAnn,
# family = theme_get()$text[["family"]],
# x = -5, y = min(resBasal$lpadj) + 0.5, hjust = .5,
# size = 9*0.35) +
#annotate(geom = "text",
# label = upAnn,
# family = theme_get()$text[["family"]],
# x = 3/1.7, y = min(resBasal$lpadj) + 0.5, hjust = .3,
# size = 9*0.35) +
geom_text_repel(
data = subset(resBasal, comName %in% genesInterest),
aes(label = comName),
size = 9*0.35,
fontface = 'italic',
box.padding = unit(0.35, "lines"),
point.padding = unit(0.3, "lines")) +
scale_color_manual(values = c("Significantly changed" = "#E64B35",
"Non significantly changed" = "#636363"))
# Plot and scale y-axis with log1p function for visualize better taking into account high values
p + scale_y_continuous(trans = "log1p")
ggsave("figures/volcanoPlot_Basal_HOG1WT_vs_HOG1KO.pdf", width = 2.5, height = 2.5, device = cairo_pdf)
```
### Logistic regression: AIC of univariable model
```{r, eval = TRUE}
univariate.aic <- read.delim("results/S_cerevisiae/dataAIC_univariate_upregulated_hog1DepSaltExt.tsv", stringsAsFactors = F)
# Order from max to min
propOrder = as.character(univariate.aic[order(univariate.aic$AIC, decreasing = T), "Property_Formatted"])
univariate.aic$Property_Formatted = factor(univariate.aic$Property_Formatted, levels = propOrder)
ggplot(univariate.aic, aes(x = AIC, y = Property_Formatted)) +
geom_point() +
theme_bw(base_size = 9, base_family = "Arial") +
theme(axis.text = element_text(colour = "black", size = 9)) +
xlab("AIC (univariable model)") +
ylab("")
ggsave("figures/pointOrdered_AIC_univariate_upregulated_hog1DepSaltExt.pdf", width = 3.75, height = 2, device = cairo_pdf)
```
### Random forest: Hog1 dependent vs independent mean decrease in accuracy
```{r, eval = TRUE}
imp.df <- read.delim(file = "results/S_cerevisiae/modelImportance_randomForest_hog1DepSaltExt_upregulated.tab", stringsAsFactors = F, row.names = 1)
imp.df.m$Property_Formatted <- paste0(" ", prop.names.format[match(row.names(imp.df.m), prop.names.format$Property), "Property_formatted_short"], " ")
# Order from max to min
propOrder = as.character(imp.df.m[order(imp.df.m$MeanDecreaseAccuracy), "Property_Formatted"])
imp.df.m$Property_Formatted = factor(imp.df.m$Property_Formatted, levels = propOrder)
ggplot(imp.df.m, aes(x = MeanDecreaseAccuracy, y = Property_Formatted)) +
geom_point() +
theme_bw(base_size = 9, base_family = "Arial") +
theme(axis.text = element_text(colour = "black", size = 9)) +
xlab("Mean Decrease in Accuracy") +
ylab("")
ggsave("figures/pointOrdered_Mean_Decrease_Accuracy_Hog1DepSaltExt_upregulated.pdf", width = 3.75, height = 2, device = cairo_pdf)
```
## Figure 5
### Heatmap of features
Individual heatmaps for HeLa and selected yeast features
```{r}
# Read data
stress.properties.df.human <- read.delim("results/human/humanStressConsensus_Properties_Full_Table.tsv", stringsAsFactors = F)
stress.properties.df.yeast <- read.delim("results/S_cerevisiae/stressConsensus_Properties_Full_Table.tsv", stringsAsFactors = F)
# Change name to salt column to differentiate from heatmap with all feature
colnames(stress.properties.df.yeast)[3] <- "salt_yeast_selected"
conditions <- c("salt_HeLa:unresponsive,upregulated,downregulated",
"salt:unresponsive,upregulated,downregulated")
properties.toPlot <- c("Broad_conservation", "cpb", "Disorder", "DM", "gc_UTR5", "gc_CDS","gc_UTR3",
"halfLife", "length_UTR5", "length_CDS", "length_UTR3", "nTf", "PPI_degree")
comparisonsHeatmapAnnotated(propertiesStressData = stress.properties.df.human,
properties = properties.toPlot,
columnDep = "salt_HeLa",
random = 0,
sourceMolTable = prop.names.format,
outWidth = 4.5,
outHeight = 2.5,
outTable = "results/human/medianValues_features_human_stressGroups.tab")
comparisonsHeatmapAnnotated(propertiesStressData = stress.properties.df.yeast,
properties = properties.toPlot,
columnDep = "salt_yeast_selected",
random = 0,
outWidth = 4.5,
outHeight = 2.5,
outTable = "results/human/medianValues_features_yeast_stressGroups.tab",
sourceMolTable = prop.names.format)
```
### Ridges plot of selected features
```{r, eval = TRUE}
# Filter unnecesary columns
stress.properties.df.f = stress.properties.df.human %>%
dplyr::select(c(salt_HeLa, gc_CDS, halfLife))
stress.properties.df.f$salt_HeLa = Hmisc::capitalize(stress.properties.df.f$salt_HeLa)
stress.properties.melt = melt(stress.properties.df.f)
stress.properties.melt$feature = prop.names.format[match(stress.properties.melt$variable, prop.names.format$Property), "Property_formatted"]
stress.properties.melt$salt_HeLa = factor(stress.properties.melt$salt_HeLa, levels = c("Upregulated", "Downregulated", "Unresponsive"))
# Remove NAs to avoid displaying NA as a group
stress.properties.melt <- na.omit(stress.properties.melt)
ggplot(stress.properties.melt, aes(x = value, y = salt_HeLa, fill = salt_HeLa), color = salt_HeLa) +
geom_density_ridges(alpha = 0.8) +
facet_wrap(~feature, nrow = 2, scales = "free") +
theme_bw(base_family = "Arial", base_size = 9) + theme(strip.text = element_text(face = "bold", size = 9),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
strip.background = element_blank(),
panel.border = element_rect(colour = "black"),
legend.position = "none",
axis.title=element_text(size=9),
axis.text = element_text(colour = "black", size = 9),
axis.text.y = element_blank(),
axis.ticks.y = element_blank(),
plot.title = element_text(hjust = 0.5, face = "bold", size = 9),
panel.spacing = unit(16, "pt")) +
scale_fill_manual(values = rev(c("gray", "#6CA699", "#D75565"))) +
xlab("") +
ylab("")
ggsave("figures/ridgesPlot_selectedProperties_salt_HeLa.pdf", width = 1.75, height =3, device = cairo_pdf)
```
### Multinomial logistic regression: ROC curve
```{r}
load("data/rdata/roc_curve_multinomial_human_random_0.rda", verbose = TRUE)
# Specify the different classes
classes <- c("unresponsive", "downregulated", "upregulated")
pretty_colours <- c("gray", "#6CA699", "#D75565")
# Store auc
auc <- c()
cairo_pdf(paste0("figures/roc_multinomialLogistic_train_valid_salt_HeLa_random_0.pdf"), width = 2.5, height = 2.5, family = "Arial")
par(mar = c(2.1, 2.1, 1.1, 1.1), ps = 9, tcl = -0.1, mgp = c(1, 0.1, 0))
for (i in 1:length(classes))
{
# Define which observations belong to class[i]
true_values <- ifelse(ValidSet[,"salt_HeLa_2"]==classes[i],1,0)
# Assess the performance of classifier for class[i]
pred <- prediction(prediction_for_roc_curve[,i],true_values)
perf <- performance(pred, "tpr", "fpr")
if (i==1)
{
plot(perf,col=pretty_colours[i])
}
else
{
plot(perf,col=pretty_colours[i],add=TRUE)
}
# Calculate the AUC and print it to screen
auc.perf <- performance(pred, measure = "auc")
auc <- c(auc, unlist(auc.perf@y.values))
}
abline(coef = c(0, 1), lty = 2)
#legend("bottomright", legend = paste0(c("Unresponsive HeLa", "Downregulated HeLa", "Upregulated HeLa"), "; AUC: ", round(auc, 2)), inset = 0.05, col = pretty_colours, lty = c(1,1,1,4,4,4), cex = 1.2)
dev.off()
```
### Multinomial logistic regression: AIC, leave-one-out approach
```{r, eval = TRUE}
raw.AIC.df <- read.delim("results/human/dataAIC_salt_HeLa_random_0.tsv", stringsAsFactors = F)
# Full model as annotation in the graph
raw.AIC.full <- raw.AIC.df[raw.AIC.df$Property=="full", "AIC"]
# Remove full model for clarity
raw.AIC.df.tP = raw.AIC.df[raw.AIC.df$Property!="full",]
# Order from max to min
propOrder = as.character(raw.AIC.df.tP[order(raw.AIC.df.tP$AIC), "Property_Formatted"])
raw.AIC.df.tP$Property_Formatted = factor(raw.AIC.df.tP$Property_Formatted, levels = propOrder)
ggplot(raw.AIC.df.tP, aes(x = AIC, y = Property_Formatted)) +
geom_point() +
geom_vline(xintercept = raw.AIC.full, colour = "red4", linetype = "dashed") +
theme_bw(base_size = 9, base_family = "Arial") +
theme(axis.text = element_text(colour = "black", size = 9)) +
xlab("AIC (leave-one-out approach)") +
ylab("")
ggsave("figures/pointOrdered_AIC_salt_HeLa_random_0.pdf", width = 4.25, height = 2.25, device = cairo_pdf)
```
### Multinomial logistic regression: Coefficients, radial plot
```{r}
# Change to 400 if wanted
multinom.toPlot <- read.delim("results/human/multinomialTestSummary_scaled_Properties_salt_HeLa_random_0.tab", stringsAsFactors = F, row.names = 1)
multinom.toPlot.radar <- multinom.toPlot[c("upregulated", "downregulated"), -1]
multinom.toPlot.radar = tibble::rownames_to_column(multinom.toPlot.radar, var = "stressGroup2")
prop.x.axis <- prop.names.format[match(colnames(multinom.toPlot.radar)[2:ncol(multinom.toPlot.radar)], prop.names.format$Property), "Property_formatted"]
prop.x.axis <- sub("Protein-Protein Interaction", "PPI", prop.x.axis)
prop.x.axis <- sub("transcription factors", "TFs", prop.x.axis)
ggRadar(data = multinom.toPlot.radar, aes(color = stressGroup2, fill = stressGroup2), rescale = F, size = 0) +
theme_bw(base_size = 16, base_family = "Arial") +
scale_x_discrete(labels = prop.x.axis) +
scale_colour_manual(values = colorsRadial) +
scale_fill_manual(values = colorsRadial) +
theme(legend.title = element_blank(),
legend.position = "none", panel.border = element_blank(), panel.grid = element_line(color = "grey", size = 1.1),
axis.text = element_blank(),
axis.ticks = element_blank()) +
ggsave("figures/radarPlot_multinomCoefficients_salt_HeLa_random_0_wo_labels.pdf", width = 5, height = 5, device = cairo_pdf)
```
## Figure S5
### Ridges plot of rest features
```{r, eval = TRUE}
# Read data
stress.properties.df <- read.delim("results/human/humanStressConsensus_Properties_Full_Table.tsv", stringsAsFactors = F)
# Filter unnecesary columns (i.e retain only salt HeLa) and already plotted features
stress.properties.df.f = stress.properties.df %>%
dplyr::select(c(3:ncol(stress.properties.df), -gc_CDS, -halfLife))
stress.properties.df.f$salt_HeLa = Hmisc::capitalize(stress.properties.df.f$salt_HeLa)
stress.properties.melt = melt(stress.properties.df.f)
# Remove rows with NA to avoid plotting problems
stress.properties.melt = na.omit(stress.properties.melt)
stress.properties.melt$feature = prop.names.format[match(stress.properties.melt$variable, prop.names.format$Property), "Property_formatted_short"]
stress.properties.melt$salt_HeLa = factor(stress.properties.melt$salt_HeLa, levels = c("Upregulated", "Downregulated", "Unresponsive"))
ggplot(stress.properties.melt, aes(x = value, y = salt_HeLa, fill = salt_HeLa)) +
geom_density_ridges(alpha = 0.8) +
facet_wrap(~feature, ncol = 3, scales = "free") +
theme_bw(base_family = "Arial", base_size = 9) + theme(strip.text = element_text(face = "bold", size = 9),
axis.text = element_text(colour = "black", size = 9),
axis.title = element_text(size = 9),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
strip.background = element_blank(),
plot.title = element_text(hjust = 0.5, face = "bold", size = 9),
panel.border = element_rect(colour = "black"),
axis.text.y = element_blank(),
axis.ticks.y = element_blank(),
legend.position = "none",
panel.spacing = unit(16, "pt"),
plot.margin = unit(c(5.1, 6.5, 5.1, 5.1), units = "points")) +
scale_fill_manual(values = rev(c("gray", "#6CA699", "#D75565"))) +
xlab("") +
ylab("")
ggsave("figures/ridgesPlot_RestProperties_salt_HeLa.pdf", width = 6.5, height = 6.1, device = cairo_pdf)
```
### Multinomial logistic regression: Univariable modelling
```{r, eval = TRUE}
univariate.aic <- read.delim("results/human/dataAIC_univariate_salt_HeLa_random_0.tsv", stringsAsFactors = F)
# Order from max to min
propOrder = as.character(univariate.aic[order(univariate.aic$AIC, decreasing = T), "Property_Formatted"])
univariate.aic$Property_Formatted = factor(univariate.aic$Property_Formatted, levels = propOrder)
ggplot(univariate.aic, aes(x = AIC, y = Property_Formatted)) +
geom_point() +
theme_bw(base_size = 9, base_family = "Arial") +
theme(axis.text = element_text(colour = "black", size =9)) +
xlab("AIC (univariable model)") +
ylab("")
ggsave("figures/pointOrdered_AIC_univariate_salt_HeLa_random_0.pdf", width = 3.75, height = 2.0, device = cairo_pdf)
```
### Random forest: ROC curve
```{r}
load("data/rdata/roc_curve_randomForest_human_random_0.rda", verbose = TRUE)
# Specify the different classes
classes <- c("unresponsive", "downregulated", "upregulated")
pretty_colours <- c("gray", "#6CA699", "#D75565")
# Store auc
auc <- c()
cairo_pdf(paste0("figures/roc_randomForest_train_valid_salt_HeLa_random_0.pdf"), width = 3, height = 3, family = "Arial")
par(mar = c(2.1, 2.1, 1.1, 1.1), ps = 9, tcl = -0.1, mgp = c(1, 0.1, 0))
# For each class (HeLa)
for (i in 1:length(classes))
{
# Define which observations belong to class[i]
true_values <- ifelse(ValidSet[,"salt_HeLa_2"]==classes[i],1,0)
# Assess the performance of classifier for class[i]
pred <- prediction(prediction_for_roc_curve[,i],true_values)
perf <- performance(pred, "tpr", "fpr")
if (i==1)
{
plot(perf,col=pretty_colours[i])
}
else
{
plot(perf,col=pretty_colours[i],add=TRUE)
}
# Calculate the AUC and print it to screen
auc.perf <- performance(pred, measure = "auc")
auc <- c(auc, unlist(auc.perf@y.values))
}
abline(coef = c(0, 1), lty = 2)
#legend("bottomright", legend = paste0(c("Unresponsive HeLa", "Downregulated HeLa", "Upregulated HeLa"), "; AUC: ", round(auc, 2)), inset = 0.05, col = pretty_colours, lty = c(1,1,1,4,4,4), cex = 1.2)
dev.off()
```
### Random forest: Mean decrease in accuracy
```{r, eval = TRUE}
imp.df.m = read.delim("results/human/modelImportance_randomForestsalt_HeLa_random_0.tab", stringsAsFactors = F, row.names = 1)
imp.df.m$Property_Formatted <- paste0(" ", prop.names.format[match(row.names(imp.df.m), prop.names.format$Property), "Property_formatted_short"], " ")
# Order from max to min
propOrder = as.character(imp.df.m[order(imp.df.m$MeanDecreaseAccuracy), "Property_Formatted"])
imp.df.m$Property_Formatted = factor(imp.df.m$Property_Formatted, levels = propOrder)
ggplot(imp.df.m, aes(x = MeanDecreaseAccuracy, y = Property_Formatted)) +
geom_point() +
theme_bw(base_size = 9, base_family = "Arial") +
theme(axis.text = element_text(colour = "black", size = 9)) +
xlab("Mean Decrease in Accuracy") +
ylab("")
ggsave("figures/pointOrdered_Mean_Decrease_Accuracy_salt_HeLa_random_0.pdf", width = 3.75, height = 2.0, device = cairo_pdf)
```
## Figure 6
### Heatmap TCGA RNASeq: HeLa