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heatmap_trans_order_by_atac.R
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library(tidyverse)
library(openxlsx)
library(Seurat)
library(ggplot2)
library(ggfortify)
library(jcolors)
require(gridExtra)
library(grid)
library(tidytext)
## For parallel calculations
num.cores <- detectCores(all.tests = FALSE, logical = TRUE)
getCurvePeakLoc <- function(t, y, prob = 0.8){
## Fitting the estimated kernel with smooth splines
spline.fit <- smooth.spline(x = t, y = y)
## Compute the derivatives of the fitted splines
s.0 <- predict(spline.fit, spline.fit$x, deriv=0)
s.1 <- predict(spline.fit, spline.fit$x, deriv=1)
s.derv <- data.frame(s0=s.0$y, s1=s.1$y)
## Get the location of the extrema
locs <- rle(den.sign <- sign(s.derv$s1))
## Maxima
inc.ind <- which(locs$values == 1)
if(length(inc.ind) > 1){
maxima.ind = {}
for(i in inc.ind){
maxima.ind = c(maxima.ind, sum(locs$lengths[1:i]))
}
## Interpolate a point between the location where derivative changes sign
maxima = (spline.fit$x[maxima.ind] + spline.fit$x[(maxima.ind + 1)]) / 2
maxima = maxima[!is.na(maxima)]
## Get the maximum values
maxval = predict(spline.fit, maxima)
## Get the outliers
maxima.outliers = which(maxval$y >= quantile(maxval$y, prob = prob))
## Peaks for entities of interest
entity.x = maxval$x[maxima.outliers]
entity.y = maxval$y[maxima.outliers]
}else{
entity.x <- spline.fit$x[which.max(spline.fit$y)]
entity.y <- spline.fit$y[which.max(spline.fit$y)]
}
return(entity.x)
}
sc.rna.spline.fits <- readRDS('../Input/toxo_cdc/rds_ME49_59/sc_rna_spline_fits_all_genes.rds')
sc.atac.spline.fits <- readRDS('../Input/toxo_cdc/rds_ME49_59/sc_atac_spline_fits_all_genes.rds')
# da_peaks_sig_genes_trans <- readRDS("../Input/toxo_cdc/rds_ME49_59/da_peaks_sig_genes.rds")
# ## remove repetition due to multiple peaks assigned to one gene
# da_peaks_sig_genes_trans <- da_peaks_sig_genes_trans %>% dplyr::select(V11, cluster) %>% distinct()
marker.genes <- readRDS('../Input/toxo_cdc/rds_ME49_59/Intra_markers_sig.rds')
atac.trans.marker.genes <- marker.genes
#atac.trans.marker.genes <- da_peaks_sig_genes_trans
atac.trans.marker.genes <- atac.trans.marker.genes %>% transmute(GeneID = gsub("_", "-", GeneID), phase = cluster) %>% distinct()
atac.trans.marker.genes %>% group_by(phase) %>% summarise(n())
## Filter to include transition markers only
sc.rna.spline.fits <- sc.rna.spline.fits %>% dplyr::filter(GeneID %in% atac.trans.marker.genes$GeneID)
sc.atac.spline.fits <- sc.atac.spline.fits %>% dplyr::filter(GeneID %in% atac.trans.marker.genes$GeneID)
## Turn the data into wide format (time by gene) and center & scale each gene
sc.rna.dtw.wide <- sc.rna.spline.fits %>%
pivot_wider(names_from = 'GeneID', values_from = 'y') %>%
mutate_at(vars(matches("TGME")), scale) %>%
as.data.frame()
na.ind <- which(apply(sc.rna.dtw.wide, 2, function(x) any(is.na(x))))
if(length(na.ind)){
sc.rna.dtw.wide <- sc.rna.dtw.wide[,-na.ind]
}
sc.atac.dtw.wide <- sc.atac.spline.fits %>%
pivot_wider(names_from = 'GeneID', values_from = 'y') %>%
mutate_at(vars(matches("TGME")), scale) %>%
as.data.frame()
na.ind <- which(apply(sc.atac.dtw.wide, 2, function(x) any(is.na(x))))
if(length(na.ind)){
sc.atac.dtw.wide <- sc.atac.dtw.wide[,-na.ind]
}
sc.rna.mu.scale <- sc.rna.dtw.wide %>%
pivot_longer(-x, names_to = 'GeneID', values_to = 'expr')
sc.atac.mu.scale <- sc.atac.dtw.wide %>%
pivot_longer(-x, names_to = 'GeneID', values_to = 'expr')
sc.rna.peak.order <- sc.rna.mu.scale %>% group_by(GeneID) %>% summarise(peak.ord = getCurvePeakLoc(x, expr))
sc.rna.mu.scale <- left_join(sc.rna.mu.scale, sc.rna.peak.order, by = 'GeneID')
sc.rna.mu.scale <- left_join(sc.rna.mu.scale, atac.trans.marker.genes, by = 'GeneID')
sc.atac.peak.order <- sc.atac.mu.scale %>% group_by(GeneID) %>% summarise(peak.ord = getCurvePeakLoc(x, expr))
sc.atac.mu.scale <- left_join(sc.atac.mu.scale, sc.atac.peak.order, by = 'GeneID')
sc.atac.mu.scale <- left_join(sc.atac.mu.scale, atac.trans.marker.genes, by = 'GeneID')
sc.atac.mu.scale$GeneID <- factor(sc.atac.mu.scale$GeneID,
levels = unique(sc.atac.mu.scale$GeneID[order(-sc.atac.mu.scale$peak.ord)]))
## ATAC peak order
sc.rna.mu.scale$peak.ord.atac <- sc.atac.mu.scale$peak.ord[match(sc.rna.mu.scale$GeneID, sc.atac.mu.scale$GeneID)]
sc.rna.mu.scale$GeneID <- factor(sc.rna.mu.scale$GeneID,
levels = unique(sc.rna.mu.scale$GeneID[order(-sc.atac.mu.scale$peak.ord)]))
sc.atac.mu.scale <- sc.atac.mu.scale %>% dplyr::filter(GeneID %in% atac.trans.marker.genes$GeneID)
p1 <- ggplot(sc.atac.mu.scale, aes(x = x, y = GeneID, fill = expr)) +
geom_tile() +
# facet_grid(phase~., scales = "free", space='free',
# labeller=label_wrap_gen(multi_line = TRUE))+
ylab("Genes") + xlab("time/cells") + ggtitle('scATAC') +
#scale_fill_gradientn(colours = hm.palette(10)) +
scale_fill_gradientn(colours = viridis::inferno(10)) +
theme(panel.spacing = unit(0.1, "lines")) +
theme(
strip.background = element_rect(fill = "white", color = "black"),
strip.text = element_text(size = 20, face = "bold"),
axis.text.x = element_blank(),
axis.ticks = element_blank(),
axis.text.y = element_blank(),
plot.title = element_text(size=20, face="bold"),
axis.title.x = element_text(size=20, face="bold"),
axis.title.y = element_text(size=20, face="bold"),
legend.position = "none")
#theme(plot.margin=unit(c(2.5,2.5,2.5,2.5),"cm"))
plot(p1)
ggsave(filename="../Output/toxo_cdc/ME49_59/figures_paper/scATAC_heatmap_atac_ord.pdf",
plot=p1,
width = 6, height = 7,
units = "in", # other options are "in", "cm", "mm"
dpi = 300
)
sc.rna.mu.scale <- sc.rna.mu.scale %>% dplyr::filter(GeneID %in% atac.trans.marker.genes$GeneID)
p2 <- ggplot(sc.rna.mu.scale, aes(x = x, y = GeneID, fill = expr)) +
geom_tile() +
# facet_grid(phase~., scales = "free", space='free',
# labeller=label_wrap_gen(multi_line = TRUE))+
ylab("Genes") + xlab("time/cells") + ggtitle('scRNA') +
#scale_fill_gradientn(colours = hm.palette(10)) +
scale_fill_gradientn(colours = viridis::inferno(10)) +
theme(panel.spacing = unit(0.1, "lines")) +
theme(
strip.background = element_rect(fill = "white", color = "black"),
strip.text = element_text(size = 20, face = "bold"),
axis.text.x = element_blank(),
axis.ticks = element_blank(),
axis.text.y = element_blank(),
plot.title = element_text(size=20, face="bold"),
axis.title.x = element_text(size=20, face="bold"),
axis.title.y = element_text(size=20, face="bold"),
legend.position = "none")+ ylab("")
plot(p2)
ggsave(filename="../Output/toxo_cdc/ME49_59/figures_paper/scRNA_heatmap_atac_ord.pdf",
plot=p2,
width = 6, height = 7,
units = "in", # other options are "in", "cm", "mm"
dpi = 300
)
pp <- p1 | p2
pp
ggsave(filename="../Output/toxo_cdc/ME49_59/figures_paper/cyclic_genes_heatmaps_rna_atac_ord_by_peak_access.png",
plot=pp,
width = 6, height = 8,
units = "in", # other options are "in", "cm", "mm"
dpi = 300
)
saveRDS(sc.rna.mu.scale, '../Input/toxo_cdc/rds_ME49_59/sc_rna_spline_mu_scale_rna_trans_ord_by_atac.rds')
saveRDS(sc.atac.mu.scale, '../Input/toxo_cdc/rds_ME49_59/sc_atac_spline_mu_scale_atac_trans_ord_by_atac.rds')
# new version
sc.rna.mu.scale$data <- "scRNA"
#sc.rna.mu.scale$peak.ord.rna <- sc.rna.mu.scale$peak.ord
sc.atac.mu.scale$data <- "scATAC"
sc.atac.mu.scale$peak.ord.atac <- sc.atac.mu.scale$peak.ord
df <- rbind(sc.rna.mu.scale, sc.atac.mu.scale)
df$data <- factor(df$data, levels = c( "scATAC", "scRNA"))
p2 <- ggplot(df, aes(x = x, y = GeneID, fill = expr)) +
geom_tile() +
# facet_grid(phase~., scales = "free", space='free',
# labeller=label_wrap_gen(multi_line = TRUE))+
ylab("Genes") + xlab("time/cells") +
#scale_fill_gradientn(colours = hm.palette(10)) +
facet_grid(. ~ data, scales = "free", space='free', labeller=label_wrap_gen(multi_line = TRUE))+
scale_fill_gradientn(colours = viridis::inferno(10)) +
theme(panel.spacing = unit(0.01, "lines")) +
theme(
strip.background = element_rect(fill = "white", color = "white"),
panel.spacing = unit(0.01, "lines"),
strip.text = element_text(size = 20, face = "bold"),
axis.text.x = element_blank(),
axis.ticks = element_blank(),
axis.text.y = element_blank(),
plot.title = element_text(size=20, face="bold"),
axis.title.x = element_text(size=20, face="bold"),
axis.title.y = element_text(size=20, face="bold"),
legend.position = "none") + ylab('Genes')
plot(p2)
ggsave(filename="../Output/toxo_cdc/ME49_59/figures_paper/cyclic_genes_heatmaps_atac_ord_facet.png",
plot=p2,
width = 6, height = 8,
units = "in", # other options are "in", "cm", "mm"
dpi = 300
)