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plots.R
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plots.R
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library(ggplot2)
library(cowplot)
library(grid)
library(gridExtra)
source('constants.R')
source('gene_mapping_utils.R')
source('data_utils.R')
source('prediction.R')
source('utilities.R')
source('intend.R')
source('benchmark.R')
# library(umap)
# library(ggrepel)
# library(RColorBrewer)
# library(batchelor)
# library(FNN)
# library(class)
# library(hrbrthemes)
# library(survminer)
# library(glue)
# source('tcga_assembler_preprocessing.R')
# source('survival.R')
# source('projection_functions.R')
####################################################################################################
# Common Utils
# The MMA-MA WFCI version and JLMA WFCI are not used in most figures
# Hence, they are referenced as additional algorithms
get.projections <- function(subtype, base.algorithms = T, additional.algorithms = F)
{
dimension <- DIMENSION.FOR.INTEGRATION.COMPARISON
base <- list()
if (base.algorithms) {
base <- list(
INTEND = get.intend.projections(subtype, dimension),
LIGER = get.liger.projections(subtype, dimension),
`Seurat v3` = get.seurat.projections(subtype, dimension),
`MMD-MA` = get.mmd.ma.projections(subtype, dimension, MMDMA.RESULTS.DIR)
)
}
additional <- list()
if (additional.algorithms) {
additional <- list(
`MMD-MA WFCI (500)` = get.mmd.ma.projections(subtype, dimension, file.path(MMDMA.WFCI.RESULTS.DIR, "500")),
`MMD-MA WFCI (2000)` = get.mmd.ma.projections(subtype, dimension, file.path(MMDMA.WFCI.RESULTS.DIR, "2000")),
`JLMA WFCI (500)` = get.manifold.alignment.projections(subtype, dimension, file.path(JLMA.RESULTS.DIR, "500")),
`JLMA WFCI (2000)` = get.manifold.alignment.projections(subtype, dimension, file.path(JLMA.RESULTS.DIR, "2000"))
)
}
return(c(base, additional))
}
####################################################################################################
# Supplementary Figures 1 + 2 + 3
plot.model.histograms <- function()
{
common.features <- get.common.features.for.all.subtypes()
gene.to.cg.site.mapping <- get.filtered.gene.to.cg.sites.mapping.all.subtypes(
common.features$expression,
common.features$methylation,
UPSTREAM.MARGIN,
DOWNSTREAM.MARGIN)
number.of.sites.per.gene <- sapply(gene.to.cg.site.mapping$cg_sites, length)
# Supplementary Figure 1
plot.hiatogram.of.methylation.sites.per.gene(number.of.sites.per.gene)
regression.output <- load.expression.methylation.regression.output(output.file.path = sprintf(
REGRESSION.OUTPUT.TRAIN.SUBTYPES.PATH.CV.LVO.FORMAT, "LUAD", UPSTREAM.MARGIN, DOWNSTREAM.MARGIN))
# The first coefficient is excluded as it is the intercept value. This is why `[c(-1),]` is used.
number.of.sites.per.gene.after.lasso.shrinkage <- sapply(
regression.output$fit.parameters, function(fit) sum(
coef(fit, s = "lambda.min")[c(-1),] != 0, na.rm = T))
# Supplementary Figure 2
plot.hiatogram.of.methylation.sites.per.gene(number.of.sites.per.gene.after.lasso.shrinkage)
r.squared.train <- regression.output$R.squared.train
r.squared.train.top.2000.genes <- sort(r.squared.train, decreasing = T)[1:2000]
# Supplementary Figure 3
plot.histogram.for.prediction.r.squared(r.squared.train)
plot.histogram.for.prediction.r.squared(r.squared.train.top.2000.genes)
}
plot.hiatogram.of.methylation.sites.per.gene <- function(number.of.sites.per.gene) {
df <- data.frame(number.of.sites = number.of.sites.per.gene)
df.lines <- data.frame(values = c(mean(df$number.of.sites), quantile(df$number.of.sites)[2:4]),
labels = c("Average", "Q1", "Median", "Q3"))
dev.new(noRStudioGD = T)
ggplot() +
geom_histogram(data = subset(df, number.of.sites <= 100),
mapping = aes(x = number.of.sites),
binwidth = 1,
color = "grey20",
fill = "grey95") +
geom_segment(data = df.lines,
aes(x = values, y = 0, xend = values, yend = Inf, color = labels),
size = 1.2,
show.legend = FALSE,
linetype = "dashed") +
xlab("Number of methylation probes") +
ylab("Number of genes") +
geom_text(data = df.lines,
mapping = aes(x = values, y = -30, label = labels, color = labels),
size = 5,
angle = 45,
show.legend = FALSE) +
theme_cowplot(font_size = 18)
}
plot.histogram.for.prediction.r.squared <- function(r.squared.train)
{
df <- data.frame(R.squared.train =r.squared.train)
df.lines <- data.frame(values = c(mean(df$R.squared.train)),
labels = c("Average"))
dev.new(noRStudioGD = T)
ggplot() +
geom_histogram(data = df,
mapping = aes(x = R.squared.train),
binwidth = 0.02,
color = "grey20",
fill = "grey95") +
geom_segment(data = df.lines,
aes(x = values, y = 0, xend = values, yend = Inf, color = labels),
size = 1.2,
show.legend = FALSE,
linetype = "dashed") +
xlab("R squared (training set)") +
ylab("Number of genes") +
geom_text(data = df.lines,
mapping = aes(x = values, y = -12, label = sprintf("%s\n%.02f", labels, values), color = labels),
size = 4.8,
show.legend = FALSE) +
scale_x_continuous(breaks = seq(0, 1, by = 0.1)) +
theme_cowplot(font_size = 16)
}
####################################################################################################
# Supplementary Figures 5
# scores.data: the result of compute.projection.scores.for.integration.algorithms (utils.R)
# for a specified subtype
plot.legend.of.score.vs.dimensions <- function(scores.data)
{
dev.new(width = 1600, height = 500, unit = "px", noRStudioGD = T)
plot(NULL ,xaxt = 'n',yaxt = 'n',bty = 'n',ylab = '',xlab = '', xlim = 0:1, ylim = 0:1)
projection.scores.list <- scores.data$projection.scores.list
legend.text <- names(projection.scores.list)
legend(
"topleft",
legend = legend.text,
col = 1:length(projection.scores.list),
ncol = 4,
pch = 19,
cex = 1.2,
bg = "#f9f9f9")
}
plot.projection.scores.vs.dimensions <- function
(scores.data,
show.legend = F,
show.best.score = F,
pos = 1,
new.window = T)
{
if (new.window) {
dev.new(width = 1600, height = 1000, unit = "px", noRStudioGD = T)
}
projection.scores.list <- scores.data$projection.scores.list
min.d <- scores.data$min.d
max.d <- scores.data$max.d
subtype <- scores.data$subtype
if (show.legend) {
margines = c(5, 5, 4, 15)
par(mar = margines)
}
plot(NULL,
type = "n",
xlab = "Dimension",
ylab = "FOSCTTM (%)",
main = subtype,
xlim = c(min.d, max.d),
ylim = c(0,60),
cex.lab = 1.3,
cex.main = 1.5,
pch = 19)
for (i in 1:length(projection.scores.list)) {
scores <- projection.scores.list[[i]]
dimensions <- as.integer(names(scores))
indices.of.relevant.dimensions <- (dimensions >= min.d & dimensions <= max.d)
points(dimensions[indices.of.relevant.dimensions],
scores[indices.of.relevant.dimensions] * 100,
col = i,
pch = 19,
cex = 0.8)
}
par(xpd = FALSE)
grid()
if (show.legend) {
par(mar = margines, xpd = TRUE)
if (show.best.score) {
legend.text <- paste(
names(projection.scores.list),
lapply(projection.scores.list, function(x) {
min.score <- min(x, na.rm = TRUE)
d.for.min.score <- names(x)[which(x == min.score)]
sprintf("\n(%#.2f)", min.score*100)
}))
}
else {
legend.text <- names(projection.scores.list)
}
legend(
"right",
inset = c(-0.25, 0),
legend = legend.text,
col = 1:length(projection.scores.list),
pch = 19,
cex = 1.2,
y.intersp = 2,
bg = "#f9f9f9")
}
}
####################################################################################################
# Figure 2 + Supplementary Figure 17
plot.projection.score.per.sample.for.all.algorithms <- function(
subtype,
dimension,
show.axis.titles = F,
show.legend = F,
additional.algorithms = T
)
{
projections.list <- get.projections(subtype, dimension, additional.algorithms = additional.algorithms)
scores.per.sample <- lapply(projections.list, function(projections)
get_projection_cross_manifold_similarity_score_per_sample(
X_projected = projections$expression,
Y_projected = projections$methylation
)
)
projections.algorithm <- unlist(lapply(1:length(scores.per.sample), function(i)
rep(names(scores.per.sample)[i], length(scores.per.sample[[i]]))))
scores.aggregated <- unlist(scores.per.sample) * 100
projections.algorithm <- factor(projections.algorithm, levels = names(scores.per.sample))
scores.per.sample.df <- data.frame(algorithm = projections.algorithm, scores = scores.aggregated)
boxplots <- ggplot(scores.per.sample.df, aes(x = algorithm, y = scores, color = algorithm, fill = algorithm)
) +
geom_boxplot() +
ylab("FOSCTTM (%)") +
xlab("Algorithm") +
theme(
text = element_text(size = 15),
axis.text.x = element_blank(),
plot.title = element_text(size = 16, hjust = 0.5),
legend.title = element_blank(),
legend.position = ifelse(test = show.legend, yes = "right", no = "none")
) + scale_fill_brewer(palette = "Pastel2") + scale_color_brewer(palette = "Dark2")
if (!show.axis.titles) {
boxplots <- boxplots + theme(axis.title.x = element_blank(), axis.title.y = element_blank())
}
return(boxplots)
}
# Figure 2
plot.projection.score.per.sample.for.all.subtypes <- function()
{
plot.list <- lapply(
TCGA.SUBTYPES,
function(subtype) plot.projection.score.per.sample.for.all.algorithms(
subtype,
DIMENSION.FOR.INTEGRATION.COMPARISON
) + ggtitle(subtype)
)
plot <- plot_grid(plotlist = plot.list, nrow = 4)
y.grob <- textGrob("FOSCTTM (%)",
gp = gpar(fontsize = 15), rot = 90)
x.grob <- textGrob("Algorithm",
gp = gpar(fontsize = 18))
dev.new(noRStudioGD = T)
grid.arrange(arrangeGrob(plot, left = y.grob, bottom = x.grob))
}
# Supplementary Figure 17
plot.projection.score.per.sample.for.multi.subtype.example <- function() {
multi.subtype.name <- get.multi.subtypes.name(SUBTYPES.FOR.MULTI.SUBTYPE.ANALYSIS)
plot <- plot.projection.score.per.sample.for.all.algorithms(
multi.subtype.name,
DIMENSION.FOR.INTEGRATION.COMPARISON,
show.axis.titles = T,
show.legend = T,
additional.algorithms = F
)
dev.new(noRStudioGD = T)
plot(plot)
}
####################################################################################################
# Supplementary Figure 4
plot.expression.methylation.correlations.per.genomic.region <-
function(correlations.per.region.for.all.subtypes)
{
keys <- unique(unlist(lapply(correlations.per.region.for.all.subtypes, names)))
correlations.per.region <- setNames(
do.call(mapply,c(FUN = c, lapply(correlations.per.region.for.all.subtypes, `[`, keys))),
keys
)
# Remove NA values
correlations.per.region <- lapply(correlations.per.region, function(cor) cor[!is.na(cor)])
correlations.concatenated <- unlist(correlations.per.region)
regions.attribution <- rep(names(correlations.per.region), times = sapply(correlations.per.region, length))
regions.attribution <- factor(regions.attribution, levels = names(correlations.per.region))
df <- data.frame(regions = regions.attribution, correlations = correlations.concatenated)
dev.new(noRStudioGD = T)
boxplots <- ggplot(df, aes(x = regions, y = correlations, color = regions)) +
geom_violin(show.legend = FALSE) +
geom_hline(yintercept = 0, size = 1.2, linetype = "dashed") +
stat_summary(fun = mean,
geom = "crossbar",
width = 0.75,
mapping = aes(color = regions),
show.legend = FALSE) +
geom_point(data = subset(df, FALSE), size = 5, shape = 15) + # Hack for legend key control
guides(color = guide_legend(title = NULL, label.theme = element_text(size = 16))) +
xlab("Gene Region") +
ylab("Expression-Methylation Correlations per Gene") +
theme(
text = element_text(size = 18),
panel.grid.minor = element_line(colour = "white", size = 0.5),
panel.grid.major = element_line(colour = "white", size = 0.8)
) +
scale_y_continuous(minor_breaks = seq(-1 , 1, 0.05), breaks = seq(-1, 1, 0.1)) +
scale_fill_brewer(palette = "Set1")
plot(boxplots)
}
plot.expression.methylation.correlations.per.genomic.region.for.all.subtypes.in.plot.grid <-
function(correlations.per.region.for.all.subtypes)
{
plot.list <- lapply(1:length(TCGA.SUBTYPES), function(i)
{
plot.expression.methylation.correlations.per.genomic.region.for.subtype(
correlations.per.region.for.all.subtypes[[i]],
TCGA.SUBTYPES[i])
}
)
plot <- plot_grid(plotlist = plot.list)
y.grob <- textGrob("Expression-Methylation Correlations per Gene",
gp = gpar(fontsize = 18), rot = 90)
x.grob <- textGrob("Gene Region",
gp = gpar(fontsize = 15))
dev.new(noRStudioGD = T)
grid.arrange(arrangeGrob(plot, left = y.grob, bottom = x.grob))
}
plot.expression.methylation.correlations.per.genomic.region.for.subtype <-
function(correlations.per.region, subtype)
{
# Remove NA values
correlations.per.region <- lapply(correlations.per.region, function(cor) cor[!is.na(cor)])
correlations.concatenated <- unlist(correlations.per.region)
regions.attribution <- rep(names(correlations.per.region), times = sapply(correlations.per.region, length))
regions.attribution <- factor(regions.attribution, levels = names(correlations.per.region))
df <- data.frame(regions = regions.attribution, correlations = correlations.concatenated)
boxplots <- ggplot(df, aes(x = regions, y = correlations, color = regions)) +
geom_violin(show.legend = FALSE) +
geom_hline(yintercept = 0, size = 1, linetype = "dashed") +
stat_summary(fun = mean,
geom = "crossbar",
width = 0.75,
mapping = aes(color = regions),
show.legend = FALSE) +
ggtitle(subtype) +
theme(
text = element_text(size = 12),
axis.title.x = element_blank(),
axis.title.y = element_blank(),
axis.text.x = element_blank(),
plot.title = element_text(size = 15),
panel.grid.minor = element_line(colour = "white", size = 0.5),
panel.grid.major = element_line(colour = "white", size = 0.8)
) +
scale_y_continuous(minor_breaks = seq(-1 , 1, 0.1), breaks = seq(-1, 1, 0.5)) +
scale_fill_brewer(palette = "Set1")
return(boxplots)
}
####################################################################################################
create.original.datasets.data.for.plot <- function(
expression.reduced,
methylation.reduced,
reduction.method,
multi.subtypes.attribution.expression = NULL,
multi.subtypes.attribution.methylation = multi.subtypes.attribution.expression
)
{
rownames(expression.reduced) <- paste(rownames(expression.reduced), "1", sep = "_")
rownames(methylation.reduced) <- paste(rownames(methylation.reduced), "2", sep = "_")
modalities <- c(rep(EXPRESSION.GROUP, nrow(expression.reduced)),
rep(METHYLATION.GROUP, nrow(methylation.reduced)))
sample.num <- c(1:nrow(expression.reduced), 1:nrow(methylation.reduced))
data.for.plot <- list(
reduced.data = rbind(expression.reduced, methylation.reduced),
reduction.method = reduction.method,
sample.num = sample.num
)
data.for.plot[[BY.MODALITY]] = modalities
if (!is.null(multi.subtypes.attribution.expression)) {
stopifnot(length(multi.subtypes.attribution.expression) == nrow(expression.reduced) &&
length(multi.subtypes.attribution.methylation) == nrow(methylation.reduced))
data.for.plot[[BY.SUBTYPE]] = c(
multi.subtypes.attribution.expression,
multi.subtypes.attribution.methylation
)
}
return(data.for.plot)
}
get.inetgrated.results.for.plot <- function(
projections,
multi.subtypes.attribution.expression = NULL,
multi.subtypes.attribution.methylation = multi.subtypes.attribution.expression
)
{
projected.expression <- projections$expression
projected.methylation <- projections$methylation
colnames(projected.expression) <- paste(colnames(projected.expression), "1", sep = "_")
colnames(projected.methylation) <- paste(colnames(projected.methylation), "2", sep = "_")
integrated.data <- cbind(projected.expression, projected.methylation)
modalities <- c(rep(EXPRESSION.GROUP, ncol(projected.expression)),
rep(METHYLATION.GROUP, ncol(projected.methylation)))
sample.num <- c(1:ncol(projected.expression), 1:ncol(projected.methylation))
if (!is.null(multi.subtypes.attribution.expression)) {
stopifnot(length(multi.subtypes.attribution.expression) == ncol(projected.expression) &&
length(multi.subtypes.attribution.methylation) == ncol(projected.methylation))
}
return(list(integrated.data = integrated.data,
modalities = modalities,
sample.num = sample.num,
subtype.attribution = c(
multi.subtypes.attribution.expression,
multi.subtypes.attribution.methylation
)
))
}
preprocess.plot.data <- function(plot.data, dims, color.by, shape.by = NA)
{
df <- data.frame(row.names = rownames(plot.data$reduced.data))
c1 <- glue("{plot.data$reduction.method}_1")
c2 <- glue("{plot.data$reduction.method}_2")
df[[c1]] <- plot.data$reduced.data[, dims[1]]
df[[c2]] <- plot.data$reduced.data[, dims[2]]
df[[color.by]] <- plot.data[[color.by]]
if (!is.na(shape.by)) {
df$shape <- as.factor(plot.data[[shape.by]])
}
else {
df$shape <- as.factor(rep(19, times = nrow(df)))
}
df$sample.num <- plot.data$sample.num
return(list(df = df, x = c1, y = c2, color.by = color.by))
}
preprocess.plot.data.with.facets <- function(plot.data, dims, color.by, facets.group.by)
{
data <- preprocess.plot.data(plot.data, dims, color.by)
data$df[[facets.group.by]] <- plot.data[[facets.group.by]]
return(data)
}
plot.as.seperate.datasets.2d <- function(
subtype,
plot.data,
dims = c(1,2),
color.by = BY.MODALITY,
facets.group.by = BY.MODALITY,
new.window = FALSE,
continuous.scale = FALSE)
{
if (new.window) {
dev.new(height = 500, width = 1000, unit = "px",noRStudioGD = TRUE)
}
data <- preprocess.plot.data.with.facets(plot.data, dims, color.by, facets.group.by)
plot <-
ggplot(data = data$df, mapping = aes_string(x = data$x, y = data$y, color = data$color.by)) +
facet_wrap(as.formula(paste("~", facets.group.by)), scales = "free") +
geom_point(
size = 1,
# shape = ".",
show.legend = F) +
theme_cowplot() +
panel_border() +
theme(
text = element_text(size = 10),
axis.text = element_text(size = 10),
strip.text.x = element_text(size = (14))
)
if (continuous.scale) {
plot <- plot + scale_colour_gradient2(mid = "yellow", high = "red", na.value = "grey80")
}
if (new.window) {
plot(plot)
} else {
return(plot)
}
}
plot.as.seperate.datasets.2d.with.labeled.samples <- function(
subtype,
plot.data,
dims = c(1,2),
color.by = BY.MODALITY,
facets.group.by = BY.MODALITY,
new.window = FALSE)
{
if (new.window) {
dev.new(height = 500, width = 1000, unit = "px",noRStudioGD = TRUE)
}
data <- preprocess.plot.data.with.facets(plot.data, dims, color.by, facets.group.by)
set.seed(123456) # For the reproduction of the data points sampling
sampled.points <- sample(unique(data$df$sample.num), 10)
plot <-
ggplot(data = data$df, mapping = aes_string(x = data$x, y = data$y, color = data$color.by)) +
facet_wrap(as.formula(paste("~", facets.group.by)), scales = "free") +
geom_point(color = WEAK.GRAY, show.legend = FALSE) +
geom_point(
data = subset(data$df, sample.num %in% sampled.points),
show.legend = FALSE) +
geom_text_repel(
# data = data$df,
# mapping = aes(label = sample.num),
# size = 2.5,
data = subset(data$df, sample.num %in% sampled.points),
mapping = aes(label = rep(1:length(sampled.points), 2)),
size = 5,
show.legend = FALSE) +
theme_cowplot() +
panel_border() +
theme(
text = element_text(size = 10),
axis.text = element_text(size = 10),
strip.text.x = element_text(size = (14))
)
if (new.window) {
plot(plot)
} else {
return(plot)
}
}
plot.as.integrated.dataset.2d <- function(
algorithm,
plot.data,
dims = c(1,2),
color.by = BY.MODALITY,
shape.by = NA,
new.window = FALSE,
show.legend = FALSE,
show.title = TRUE,
continuous.scale = FALSE)
{
if (new.window) {
dev.new(height = 500, width = 600, unit = "px",noRStudioGD = TRUE)
}
data <- preprocess.plot.data(plot.data = plot.data, dims = dims, color.by = color.by, shape.by = shape.by)
plot <-
ggplot(data = data$df, aes_string(x = data$x, y = data$y, color = data$color.by)) +
geom_point(data = subset(data$df, FALSE), size = 5, shape = 15) + # Hack for legend key control
guides(color = guide_legend(title = NULL, label.theme = element_text(size = 16))) +
geom_point(
size = 1,
show.legend = FALSE,
aes(shape = shape)) +
scale_shape_manual(values = c(19, 0, 3, 15)) +
theme_cowplot() +
theme(
plot.title = element_text(size = 16, hjust = 0.5),
text = element_text(size = 10),
axis.text = element_text(size = 10),
)
if (continuous.scale) {
plot <- plot + scale_colour_gradient2(mid = "yellow", high = "red", na.value = "grey80")
}
if (show.title) {
plot <- plot + ggtitle(algorithm)
}
if (!show.legend) {
plot <- plot + theme(legend.position = "none")
}
if (new.window) {
plot(plot)
} else {
return(plot)
}
}
plot.as.integrated.dataset.2d.with.labeled.samples <- function(
algorithm,
plot.data,
dims = c(1,2),
color.by = BY.MODALITY,
new.window = FALSE,
show.legend = FALSE)
{
if (new.window) {
dev.new(height = 500, width = 600, unit = "px",noRStudioGD = TRUE)
}
data <- preprocess.plot.data(plot.data = plot.data, dims = dims, color.by = color.by)
set.seed(123456) # For the reproduction of the data points sampling
sampled.points <- sample(unique(data$df$sample.num), 10)
plot <-
ggplot(data = data$df, aes_string(x = data$x, y = data$y, color = data$color.by)) +
geom_point(data = subset(data$df, FALSE), size = 5, shape = 15) + # Hack for legend key control
guides(color = guide_legend(title = NULL, label.theme = element_text(size = 16))) +
geom_point(color = WEAK.GRAY, show.legend = FALSE) +
geom_point(
data = subset(data$df, sample.num %in% sampled.points),
show.legend = FALSE) +
geom_text_repel(
# data = data$df,
# mapping = aes(label = sample.num),
# size = 2.5,
data = subset(data$df, sample.num %in% sampled.points),
mapping = aes(label = rep(1:length(sampled.points), 2)),
size = 5,
show.legend = FALSE) +
theme_cowplot() +
theme(
text = element_text(size = 10),
axis.text = element_text(size = 10),
)
if (!show.legend) {
plot <- plot + theme(legend.position = "none")
}
if (new.window) {
plot(plot)
} else {
return(plot)
}
}
get.filtered.indices.with.survival.data <- function(
survival.data,
exp.met.data,
surv.plot.data.orig,
surv.plot.data.integrated
)
{
expression.samples <- colnames(exp.met.data$expression)
methylation.samples <- colnames(exp.met.data$methylation)
# Filter only primary tumor samples, to avoid more than one sample for the same patient
filtered.exp.samples <- filter.only.tumor.samples(expression.samples)
filtered.met.samples <- filter.only.tumor.samples(methylation.samples)
filtered.exp.patients <- get.short.tcga.samples.names(filtered.exp.samples)
filtered.met.patients <- get.short.tcga.samples.names(filtered.met.samples)
# Verify that the filtered patients list does not contain duplicates
stopifnot(anyDuplicated(filtered.exp.patients) == 0)
stopifnot(anyDuplicated(filtered.met.patients) == 0)
TCGA.PATIENT.LENGTH <- 12
filtered.exp.indices <- which(rownames(surv.plot.data.orig$reduced.data) %in% filtered.exp.samples)
filtered.exp.indices.2 <- which(rownames(surv.plot.data.integrated$reduced.data) %in% filtered.exp.samples)
stopifnot(identical(filtered.exp.indices, filtered.exp.indices.2))
filtered.met.indices <- which(rownames(surv.plot.data.orig$reduced.data) %in% filtered.met.samples)
filtered.met.indices.2 <- which(rownames(surv.plot.data.integrated$reduced.data) %in% filtered.met.samples)
stopifnot(identical(filtered.met.indices, filtered.met.indices.2))
filtered.exp.indices <- filtered.exp.indices[substr(rownames(surv.plot.data.orig$reduced.data)[
filtered.exp.indices], 1, TCGA.PATIENT.LENGTH) %in% names(survival.data)]
filtered.met.indices <- filtered.met.indices[substr(rownames(surv.plot.data.orig$reduced.data)[
filtered.met.indices], 1, TCGA.PATIENT.LENGTH) %in% names(survival.data)]
stopifnot(length(intersect(filtered.exp.indices, filtered.met.indices)) == 0)
return(list(
exp = filtered.exp.indices,
met = filtered.met.indices - length(expression.samples),
both = c(filtered.exp.indices, filtered.met.indices)
))
}
filter.plot.data <- function(plot.data, indices, additional.1d.array.fields = NULL)
{
stopifnot(length(plot.data) == 4 + length(additional.1d.array.fields))
plot.data$reduced.data <- plot.data$reduced.data[indices, ]
plot.data$sample.num <- plot.data$sample.num[indices]
plot.data[[BY.MODALITY]] <- plot.data[[BY.MODALITY]][indices]
for (field in additional.1d.array.fields)
{
plot.data[[field]] <- plot.data[[field]][indices]
}
return(plot.data)
}
get.subtype.classification.with.survival.analysis.for.skcm <- function()
{
repetitions <- 30
p.val <- matrix(nrow = 11, ncol = repetitions)
p.val.full <- list()
best.num.clusters <- matrix(nrow = 11, ncol = repetitions)
num.samples.with.survivial <- matrix(nrow = 2, ncol = repetitions)
rownames(num.samples.with.survivial) <- c("exp", "met")
for (i in 1:repetitions) {
subtype <- sprintf("SKCM_SPLIT_%d", i)
algorithm <- "INTEND"
utils.log(sprintf("Collecting data for subtype: %s", subtype))
exp.met.data <- get.exp.met.data.for.cluster.plot(subtype, expect.same.samples.for.exp.met = FALSE)
original.datasets.for.plot <- get.original.datasets.for.plot.with.umap(subtype)
intend.projections <- get.intend.projections(subtype, DIMENSION.FOR.INTEGRATION.COMPARISON)
colnames(intend.projections$expression) <-
gsub(pattern = "[.]", replacement = "-", x = colnames(intend.projections$expression))
colnames(intend.projections$methylation) <-
gsub(pattern = "[.]", replacement = "-", x = colnames(intend.projections$methylation))
integrated.data.for.plot <- get.integrated.data.for.plot.with.umap(intend.projections)
orig.data.plot <- plot.as.seperate.datasets.2d(subtype, original.datasets.for.plot)
integrated.plot <- plot.as.integrated.dataset.2d(
algorithm,
integrated.data.for.plot,
show.legend = T,
show.title = F
)
common.legend <- get_legend(integrated.plot + theme(legend.direction = "horizontal", legend.justification = "center"))
integrated.plot <- integrated.plot + theme(legend.position = "none")
clinical.data <- get.clinical.data("SKCM")
survival.data <- get.survival.data(clinical.data)
surv.plot.data.orig <- original.datasets.for.plot
surv.plot.data.integrated <- integrated.data.for.plot
filtered.indices <- get.filtered.indices.with.survival.data(
survival.data,
exp.met.data,
surv.plot.data.orig,
surv.plot.data.integrated)
num.samples.with.survivial["exp", i] <- length(filtered.indices$exp)
num.samples.with.survivial["met", i] <- length(filtered.indices$met)
exp.met.data.filtered <- list(expression = exp.met.data$expression[, filtered.indices$exp],
methylation = exp.met.data$methylation[, filtered.indices$met])
intend.projections.filtered <- list(expression = intend.projections$expression[, filtered.indices$exp],
methylation = intend.projections$methylation[, filtered.indices$met])
surv.plot.data.orig <- filter.plot.data(surv.plot.data.orig, filtered.indices$both)
surv.plot.data.integrated <- filter.plot.data(surv.plot.data.integrated, filtered.indices$both)
skcm.res <- get.clustering.res.for.subtype(
subtype,
surv.plot.data.orig,
surv.plot.data.integrated,
survival.data,
intend.projections.filtered,
exp.met.data.filtered)
best.num.clusters[, i] <- skcm.res$best.num.of.clusters
p.val.full[[i]] <- skcm.res$pvalues
p.val[, i] <- skcm.res$pvalues[cbind(seq_len(nrow(skcm.res$pvalues)), skcm.res$best.num.of.clusters - 1)]
rownames(best.num.clusters) = rownames(p.val) = rownames(skcm.res$pvalues)
cat(sapply(rownames(skcm.res$pvalues), function(x) sprintf(
"%s:\t%.03f (%d)\n",
x,
skcm.res$pvalues[x, as.character(skcm.res$best.num.of.clusters[x])],
skcm.res$best.num.of.clusters[x]
)), sep = "")
}
num.clusters.exp.orig <- skcm.res$best.num.of.clusters["kmeans.origin.exp"]
num.clusters.met.orig <- skcm.res$best.num.of.clusters["kmeans.origin.met"]
num.clusters.met.by.exp <- skcm.res$best.num.of.clusters["met.proj.by.exp.orig.umap.none"]
clusters.exp.orig <- skcm.res$clustering.results[[toString(num.clusters.exp.orig)]]$kmeans$kmeans.origin.exp$cluster
clusters.met.orig <- skcm.res$clustering.results[[toString(num.clusters.met.orig)]]$kmeans$kmeans.origin.met$cluster
clusters.met.by.exp <- skcm.res$clustering.results[[
toString(num.clusters.met.by.exp)]]$kmeans$met.proj.by.exp.orig.umap.none$cluster
TCGA.PATIENT.LENGTH <- 12
exp.orig.patient.names <- substr(names(clusters.exp.orig), 1, TCGA.PATIENT.LENGTH)
met.orig.patient.names <- substr(names(clusters.met.orig), 1, TCGA.PATIENT.LENGTH)
met.by.exp.patient.names <- substr(names(clusters.met.by.exp), 1, TCGA.PATIENT.LENGTH)
surv.vs.clusters.exp.orig <- data.frame(
surv = survival.data[exp.orig.patient.names], clusters = clusters.exp.orig)
surv.vs.clusters.met.orig <- data.frame(
surv = survival.data[met.orig.patient.names], clusters = clusters.met.orig)
surv.vs.clusters.met.by.exp <- data.frame(
surv = survival.data[met.by.exp.patient.names], clusters = clusters.met.by.exp)
surv.analysis.exp.orig <- get.empirical.surv(surv.vs.clusters.exp.orig$surv, surv.vs.clusters.exp.orig$clusters)
surv.analysis.met.orig <- get.empirical.surv(surv.vs.clusters.met.orig$surv, surv.vs.clusters.met.orig$clusters)
surv.analysis.met.by.exp <- get.empirical.surv(surv.vs.clusters.met.by.exp$surv, surv.vs.clusters.met.by.exp$clusters)
surv.analysis.exp.orig$pvalue
surv.analysis.met.orig$pvalue
surv.analysis.met.by.exp$pvalue
fit.exp.orig <- survfit(surv ~ clusters, data = surv.vs.clusters.exp.orig)
fit.met.orig <- survfit(surv ~ clusters, data = surv.vs.clusters.met.orig)
fit.met.by.exp <- survfit(surv ~ clusters, data = surv.vs.clusters.met.by.exp)
plot.data.clusters.exp.orig <-
skcm.res$clustering.results[[toString(num.clusters.exp.orig)]]$plot.data$plot.data.clustered.origin
exp.indices <- which(plot.data.clusters.exp.orig[[BY.MODALITY]] == EXPRESSION.GROUP)
plot.data.clusters.exp.orig <- filter.plot.data(plot.data.clusters.exp.orig, exp.indices, "cluster")
plot.data.clusters.met.orig <-
skcm.res$clustering.results[[toString(num.clusters.met.orig)]]$plot.data$plot.data.clustered.origin
met.indices <- which(plot.data.clusters.met.orig[[BY.MODALITY]] == METHYLATION.GROUP)
plot.data.clusters.met.orig <- filter.plot.data(plot.data.clusters.met.orig, met.indices, "cluster")
plot.data.clusters.met.by.exp <-
skcm.res$clustering.results[[
toString(num.clusters.met.by.exp)]]$plot.data$plot.data.met.proj.by.exp.orig.umap.none
color.by.for.plots <- "cluster"
shape.by <- NA
show.legend = FALSE
plot.clustered.exp.orig <-
plot.as.integrated.dataset.2d(
algorithm,
plot.data.clusters.exp.orig,
color.by = color.by.for.plots,
shape.by = shape.by,
show.legend = show.legend,
show.title = F)
plot.clustered.exp.orig <- plot.clustered.exp.orig + scale_color_brewer(palette = "Paired")
plot.clustered.met.orig <-
plot.as.integrated.dataset.2d(
algorithm,
plot.data.clusters.met.orig,
color.by = color.by.for.plots,
shape.by = shape.by,
show.legend = show.legend,
show.title = F)
plot.clustered.met.orig <- plot.clustered.met.orig + scale_color_brewer(palette = "Dark2")
plot.clustered.met.by.exp <-
plot.as.integrated.dataset.2d(
algorithm,
plot.data.clusters.met.by.exp,
color.by = color.by.for.plots,
shape.by = shape.by,
show.legend = show.legend,
show.title = F)
plot.clustered.met.by.exp <- plot.clustered.met.by.exp + scale_color_brewer(palette = "Paired")
plot.survival.exp.orig <- ggsurvplot(fit.exp.orig)$plot +
xlab("Time (days)") + theme(legend.position = "none") + scale_color_brewer(palette = "Paired")
plot.survival.met.orig <- ggsurvplot(fit.met.orig)$plot +
xlab("Time (days)") + theme(legend.position = "none") + scale_color_brewer(palette = "Dark2")
plot.survival.met.by.exp <- ggsurvplot(fit.met.by.exp)$plot +
xlab("Time (days)") + theme(legend.position = "none") + scale_color_brewer(palette = "Paired")
tot.withinss.exp.orig.df <- data.frame(tot.withinss = skcm.res$tot.withinss["kmeans.origin.met", ],
num.clusters = as.integer(colnames(skcm.res$tot.withinss)))
tot.withinss.met.orig.df <- data.frame(tot.withinss = skcm.res$tot.withinss["kmeans.origin.met", ],
num.clusters = as.integer(colnames(skcm.res$tot.withinss)))
highlighted.num.clusters.exp.orig <- tot.withinss.exp.orig.df %>% filter(num.clusters == num.clusters.exp.orig)
highlighted.num.clusters.met.orig <- tot.withinss.met.orig.df %>% filter(num.clusters == num.clusters.met.orig)
tot.withinss.exp.orig.plot <- ggplot(tot.withinss.exp.orig.df, mapping = aes(num.clusters, tot.withinss)) +
geom_point() +
geom_point(data = highlighted.num.clusters.exp.orig, color = 'red', size = 3) +
ylab("Total within-cluster sum of squares") +
xlab("Number of clusters") +
theme_bw()
tot.withinss.met.orig.plot <- ggplot(tot.withinss.met.orig.df, mapping = aes(num.clusters, tot.withinss)) +
geom_point() +
geom_point(data = highlighted.num.clusters.met.orig, color = 'red', size = 3) +
ylab("Total within-cluster sum of squares") +
xlab("Number of clusters") +
theme_bw()
plot.row.1 <- plot_grid(
orig.data.plot,
integrated.plot,
labels = c('A','B'),
nrow = 1,
rel_widths = c(2, 1)
)
plot.row.2 <- plot_grid(
plot.clustered.met.orig,
plot.clustered.exp.orig,