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---
title: "Fiigures"
output: html_document
date: "2022-08-27"
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
```{r load libs, echo=FALSE, warning=FALSE, message=FALSE}
library(openxlsx)
library(ggplot2)
library(ggfortify)
library(jcolors)
require(gridExtra)
library(grid)
library(matrixStats)
library(tidyverse)
library(tidytext)
library(RColorBrewer)
library(parallel)
library(ComplexHeatmap)
library(circlize)
library(MyEllipsefit)
library(princurve)
library(Seurat)
library(gam)
library(princurve)
library(parallel)
library(tidyverse)
library(MyEllipsefit)
library(sctransform)
library(openxlsx)
library(ggplot2)
library(ggfortify)
library(jcolors)
require(gridExtra)
library(grid)
library(matrixStats)
library(tidytext)
library(RColorBrewer)
library(parallel)
library(ComplexHeatmap)
library(circlize)
library(dtwclust)
library(ggVennDiagram)
source('./util_funcs.R')
```
## Figure 1 left panel.
```{r}
pcaMataData.alldata <- readRDS("./rds/pcaMataData.alldata.rds")
p1 <- ggplot(pcaMataData.alldata, aes(x= UMAP_1,y=UMAP_2)) +
geom_point(aes(
fill = spp,
color = spp,
),
alpha = 0.4,
shape=21, size = 1)+
scale_color_manual(values = c("BBig" = "firebrick","BBov" ="darkorchid3", 'BDiv_Cow' = 'darkslateblue',
'BDiv_Human' = 'darkolivegreen4')) +
scale_fill_manual(values = c("BBig" = "firebrick","BBov" ="darkorchid3", 'BDiv_Cow' = 'darkslateblue',
'BDiv_Human' = 'darkolivegreen4')) +
theme_bw(base_size = 14) +
theme(legend.position = "right") +
ylab('UMAP2') + xlab('UMAP1') +
theme(axis.text.x = element_text(angle = 0, hjust = 1, size = 12, face="bold")) +
theme(axis.text.y = element_text(angle = 0, hjust = 1, size = 12, face="bold")) +
theme(strip.background = element_rect(colour="black", fill="white",
size=0.5, linetype="solid")) +
theme(
plot.title = element_text(size=14, face = "bold.italic", color = 'red'),
axis.title.x = element_text(size=14, face="bold", hjust = 1),
axis.title.y = element_text(size=14, face="bold")
) +
theme(legend.position = c(0.15, 0.85),
legend.title = element_text(colour="black", size=12,
face="bold"),
legend.text = element_text(colour="black", size=12,
face="bold"))
plot(p1)
```
## Figure 1 right panel. (top, middle, bottom)
```{r}
pcaMataData.alldata <- readRDS("./rds/pcaMataData.alldata.rds")
p2 <- ggplot(pcaMataData.alldata, aes(x = PC_1, y = PC_2)) +
geom_point(aes(#fill = lable.prob,
fill = spp,
color = spp,
),
alpha = 0.4,
shape=21, size = 1)+
scale_color_manual(values = c("BBig" = "firebrick","BBov" ="darkorchid3", 'BDiv_Cow' = 'darkslateblue',
'BDiv_Human' = 'darkolivegreen4')) +
scale_fill_manual(values = c("BBig" = "firebrick","BBov" ="darkorchid3", 'BDiv_Cow' = 'darkslateblue',
'BDiv_Human' = 'darkolivegreen4')) +
theme_classic() +
ylab('PC2') + xlab('PC1') +
theme(axis.text.x = element_text(angle = 0, hjust = 1, size = 18, face="bold")) +
theme(axis.text.y = element_text(angle = 0, hjust = 1, size = 18, face="bold")) +
theme(strip.background = element_rect(colour="black", fill="white",
size=0.5, linetype="solid")) +
theme(
plot.title = element_text(size=14, face = "bold.italic", color = 'red'),
axis.title.x = element_text(size=20, face="bold", hjust = 1),
axis.title.y = element_text(size=20, face="bold")
) +
theme(legend.position = "none",
legend.title = element_text(colour="black", size=12,
face="bold"),
legend.text = element_text(colour="black", size=12,
face="bold"))
plot(p2)
p3 <- ggplot(pcaMataData.alldata, aes(x= PC_1,y=PC_3)) +
geom_point(aes(#fill = lable.prob,
fill = spp,
color = spp,
),
alpha = 0.4,
shape=21, size = 1)+
scale_color_manual(values = c("BBig" = "firebrick","BBov" ="darkorchid3", 'BDiv_Cow' = 'darkslateblue',
'BDiv_Human' = 'darkolivegreen4')) +
scale_fill_manual(values = c("BBig" = "firebrick","BBov" ="darkorchid3", 'BDiv_Cow' = 'darkslateblue',
'BDiv_Human' = 'darkolivegreen4')) +
theme_classic() +
theme(legend.position = "right") +
ylab('PC3') + xlab('PC1') +
theme(axis.text.x = element_text(angle = 0, hjust = 1, size = 18, face="bold")) +
theme(axis.text.y = element_text(angle = 0, hjust = 1, size = 18, face="bold")) +
theme(strip.background = element_rect(colour="black", fill="white",
size=0.5, linetype="solid")) +
theme(
plot.title = element_text(size=14, face = "bold.italic", color = 'red'),
axis.title.x = element_text(size=20, face="bold", hjust = 1),
axis.title.y = element_text(size=20, face="bold")
) +
theme(legend.position = "none",
legend.title = element_text(colour="black", size=12,
face="bold"),
legend.text = element_text(colour="black", size=12,
face="bold"))
plot(p3)
p4 <- ggplot(pcaMataData.alldata, aes(x= PC_2,y=PC_3)) +
geom_point(aes(#fill = lable.prob,
fill = spp,
color = spp,
),
alpha = 0.4,
shape=21, size = 1)+
scale_color_manual(values = c("BBig" = "firebrick","BBov" ="darkorchid3", 'BDiv_Cow' = 'darkslateblue',
'BDiv_Human' = 'darkolivegreen4')) +
scale_fill_manual(values = c("BBig" = "firebrick","BBov" ="darkorchid3", 'BDiv_Cow' = 'darkslateblue',
'BDiv_Human' = 'darkolivegreen4')) +
theme_classic() +
theme(legend.position = "right") +
ylab('PC3') + xlab('PC2') +
theme(axis.text.x = element_text(angle = 0, hjust = 1, size = 18, face="bold")) +
theme(axis.text.y = element_text(angle = 0, hjust = 1, size = 18, face="bold")) +
theme(strip.background = element_rect(colour="black", fill="white",
size=0.5, linetype="solid")) +
theme(
plot.title = element_text(size=14, face = "bold.italic", color = 'red'),
axis.title.x = element_text(size=20, face="bold", hjust = 1),
axis.title.y = element_text(size=20, face="bold")
) +
theme(legend.position = "none",
legend.title = element_text(colour="black", size=12,
face="bold"),
legend.text = element_text(colour="black", size=12,
face="bold"))
plot(p4)
```
## Figute 2A
```{r, echo=FALSE}
S.O.itegrated <- readRDS('./rds/S.O.integrated.list.pstime.GAM.intersect.rds')
## cells projected to eliiptic curve
L <- wiskerPlot(S.O.itegrated$bdiv_human)
plot(x = -15:15, y = -15:15, type = 'n', xlab = 'PC1', ylab = 'PC2', cex.lab = 1.3)
whiskers(as.matrix(L$pc[,c(1,2)]), L$fit$s, col = "gray")
```
## Figute 2B
```{r, echo=FALSE}
L2 <- readRDS('./rds/all_pstime_fits_L2.rds')
## Lag time distribution
dd <- density(L2$bdiv_human$dd$lag,bw = 2.8)
dd <- data.frame(lag = dd$x, density = dd$y)
p1 <- ggplot(dd, aes(x=lag, y = density)) +
theme_bw() +
theme(axis.text.x = element_text(face="bold", size=12),
axis.text.y = element_text(face="bold", size = 12),
axis.title = element_text(face="bold", size = 12)) +
geom_vline(xintercept = dd$lag[which.max(dd$density)], linetype='dashed', color='red') +
geom_line(colour="black")
plot(p1)
```
## Figure 2c
```{r echo=FALSE}
bdiv_human_meta_data <- readRDS("./rds/Bdiv_human_pca_fitter_curv_start_point.rds")
p <- ggplot(bdiv_human_meta_data, aes(x=PC_1,y=PC_2)) +
geom_point(aes(fill = phase, color = phase), shape=21, size = 1)+
scale_color_manual(values=c("G" = "#1b5878", "SM" = "#c44237", "MC" = "#ad8842","C" = "#e99093"))+
scale_fill_manual(values=c("G" = "#1b5878", "SM" = "#c44237", "MC" = "#ad8842","C" = "#e99093"))+
geom_path(aes(x=sc1[cell.ord],y=sc2[cell.ord]), col = 'black', size = 1.5,
arrow = arrow(type = "open", angle = 30, ends = "first",length = unit(0.25, "inches"))) +
geom_point(aes(x=sc1[order(adj.time)][1],y=sc2[order(adj.time)][1]), col = 'black',fill = "black",
shape=21, size = 7, stroke = 1.5)+
theme_bw(base_size = 14) +
theme(axis.text.y = element_text( size = 16, face="bold", color = "black"),
axis.text.x = element_text(size = 16, face="bold", color = "black"),
plot.title = element_text(size=18, face = "bold.italic", color = 'black', hjust = 0.5),
axis.title.x = element_text(size=18, face="bold"),
axis.title.y = element_text(size=18, face="bold"),
legend.title = element_text(size = 18, face = "bold"),
legend.text = element_text(size = "16", face = "bold", color = "black"),
strip.background = element_rect(colour="black", fill="white",size=0.5, linetype="solid")) +
ylab('PC2') + xlab('PC1') +
ggtitle("Bdiv_human")
plot(p)
```
## Figure 3
```{r}
sync.sc <- readRDS('../Input/compScBabesia/RData_new/sync.sc.rds')
# plot func
overlaySyncScPlot <- function (d, xts = NULL, yts = NULL, offset = 0, main = main, xlab = xlab, ylab = ylab,
match.indices = NULL, match.col = "gray40", match.lty = 4)
{
if (is.null(xts) || is.null(yts)) {
xts <- d$query
yts <- d$reference
}
if (is.null(xts) || is.null(yts))
stop("Original timeseries are required")
ytso <- yts + offset
maxlen <- max(length(xts), length(ytso))
length(xts) <- maxlen
length(ytso) <- maxlen
def.par <- par(no.readonly = TRUE)
par(mar = c(5, 5, 4, 4) + 0.1)
if (offset != 0) {
par(mar = c(5, 4, 4, 4) + 0.1)
}
matplot(cbind(xts, ytso), type = 'l', pch = 21, xlab = xlab,
ylab = ylab, axes = FALSE, main = main, lwd = 4, cex.main = 2.2, cex.lab = 1.8,
font = 2, font.lab = 2)
axis(1, cex.axis = 1.6, font = 2)
axis(2, at = pretty(xts), cex.axis = 1.6, font = 2)
if (offset != 0) {
rightTicks <- pretty(yts)
axis(4, at = rightTicks + offset, labels = rightTicks)
}
if (is.null(match.indices)) {
ml <- length(d$index1)
idx <- 1:ml
}
else if (length(match.indices) == 1) {
idx <- seq(from = 1, to = length(d$index1), length.out = match.indices)
}
else {
idx <- match.indices
}
segments(d$index1[idx], xts[d$index1[idx]], d$index2[idx],
ytso[d$index2[idx]], col = match.col, lty = match.lty, lwd = 4)
par(def.par)
}
gene.peak.good <- 'Bdiv_033720'
gen.peak.poor <- 'Bdiv_024320'
gene.dip.good <- 'Bdiv_040000'
gene.dip.poor <- 'Bdiv_019820c'
my.genes <- c(gene.peak.good, gen.peak.poor, gene.dip.good, gene.dip.poor)
##
my.gene.expr <- sync.sc %>% dplyr::filter(GeneID == my.genes[1])
dd <- dtw(my.gene.expr$scaled.expr.sc, my.gene.expr$scaled.expr.sync, k = TRUE, step.pattern = symmetric2)
overlaySyncScPlot(dd, offset = 0, match.indices = 20, main = my.genes[1], xlab = '', ylab = '')
##
my.gene.expr <- sync.sc %>% dplyr::filter(GeneID == my.genes[2])
dd <- dtw(my.gene.expr$scaled.expr.sc, my.gene.expr$scaled.expr.sync, k = TRUE, step.pattern = symmetric2)
overlaySyncScPlot(dd, offset = 0, match.indices = 20, main = my.genes[2], xlab = '', ylab = '')
##
my.gene.expr <- sync.sc %>% dplyr::filter(GeneID == my.genes[3])
dd <- dtw(my.gene.expr$scaled.expr.sc, my.gene.expr$scaled.expr.sync, k = TRUE, step.pattern = symmetric2)
overlaySyncScPlot(dd, offset = 0, match.indices = 20, main = my.genes[3], xlab = '', ylab = '')
##
my.gene.expr <- sync.sc %>% dplyr::filter(GeneID == my.genes[4])
dd <- dtw(my.gene.expr$scaled.expr.sc, my.gene.expr$scaled.expr.sync, k = TRUE, step.pattern = symmetric2)
overlaySyncScPlot(dd, offset = 0, match.indices = 20, main = my.genes[4], xlab = '', ylab = '')
legend(x = "bottomright", # Position
legend = c("scRNA-Seq", "Bulk"), # Legend texts
lty = c(1, 2), # Line types
col = c("black", "red"), # Line colors
lwd = 2, cex = 2.1)
```
## Figure 3B
```{r echo=FALSE}
sync.sc <- readRDS('../Input/compScBabesia/RData_new/sync.sc.rds')
peaks <- readRDS('../Input/compScBabesia/RData_new/peaks.rds')
dwt.dist <- readRDS('../Input/compScBabesia/RData_new/dwt.dist.rds')
sync.sc.means <- sync.sc %>% dplyr::select(GeneID, mean.expr.sc, mean.expr.sync) %>% distinct()
sm.peaks <- peaks %>% dplyr::filter(Phase %in% c('SM', 'MC'))
non.sm.peaks <- peaks %>% dplyr::filter(!(Phase %in% c('SM', 'MC')))
sm.boundary <- c(2, 11)
peaks <- peaks %>% mutate(smc.peak = ifelse(Phase %in% c('SM', 'MC') &
peak.sc > sm.boundary[1] &
peak.sc < sm.boundary[2], 'Yes', 'No'),
smc.dip = ifelse(!(Phase %in% c('SM', 'MC')) &
dip.sc > sm.boundary[1] &
dip.sc < sm.boundary[2], 'Yes', 'No'))
dist.genes <- dwt.dist$GeneID[dwt.dist$d >= quantile(dwt.dist$d, probs = 0.8)]
close.genes <- dwt.dist$GeneID[dwt.dist$d <= quantile(dwt.dist$d, probs = 0.2)]
peaks$Alignment <- 'Mid'
peaks$Alignment[peaks$GeneID %in% dist.genes] <- 'Low'
peaks$Alignment[peaks$GeneID %in% close.genes] <- 'High'
peaks$Alignment <- factor(peaks$Alignment, levels = c('Low', 'Mid', 'High'))
stats.align <- peaks %>% group_by(Phase, Alignment) %>% summarise(total = n(), percent = n()) %>%
group_by(Phase) %>% mutate(percent=percent/sum(percent)*100,
ylab_pos = ceiling((105 - cumsum(percent))))
ind <- which(stats.align$total < 3)
stats.align$total <- as.character(stats.align$total)
stats.align$total[ind] <- ''
p <- ggplot(stats.align, aes(fill=Alignment, y=percent, x=Phase)) +
geom_bar(position="stack", stat="identity") +
geom_text(aes(y=ylab_pos, label=total), vjust=0.5, color="black", size=8, fontface = 'bold') +
theme_minimal() +
theme(
axis.text = element_text(size=18, face="bold", color = 'black'),
#plot.title = element_text(size=16, face = "bold.italic", color = 'black'),
axis.title.x = element_text(size=22, face="bold"),
axis.title.y = element_text(size=22, face="bold")) +
theme(
legend.title=element_text(size=18, face="bold"),
legend.text=element_text(size=18, face="bold"))
plot(p)
peaks$Phase <- as.character(peaks$Phase)
peaks$Phase[is.na(peaks$Phase)] <- 'NoPeak'
peaks$Phase <- factor(peaks$Phase, levels = c('G', 'SM', 'MC', 'C', 'NoPeak'))
peaks.filt <- peaks %>% dplyr::filter(smc.peak == 'Yes' & peak.sync > 0 & peak.sync < 12)
cor.txt <- cor(peaks.filt$peak.sc, peaks.filt$peak.sync)
p2 <- ggplot(peaks.filt, aes(x = peak.sc, y = peak.sync)) +
geom_point(aes(
fill = mean.expr.sc,
#color = aligns
), #color = 'blue',
shape=21, size = 2)+
geom_smooth(method='lm') +
ylab("Bulk") + xlab("Single Cell") + theme_minimal() +
# annotate(cor.txt,
# size = 4,
# fontface="bold",
# hjust = 1.05,
# vjust = 1.5
# ) +
#facet_wrap(.~Phase)+
#ggtitle("Peak time correlation") +
scale_fill_continuous(type = "viridis") +
annotate(geom="text", x=6.5, y=11.5, label=paste(round(cor.txt, 3)), color="black",size=8, fontface = "bold.italic") +
#annotate(geom="text", x=1.4, y=11.1, label="P-Value < 0.0001", color="black",size=4, fontface = "bold.italic") +
theme(axis.text = element_text(size = 18, face = "bold", color = 'black')) +
theme(
#plot.title = element_text(size=16, face = "bold.italic", color = 'black'),
axis.title.x = element_text(size=22, face="bold", color = 'black'),
axis.title.y = element_text(size=22, face="bold", color = 'black')
) +
labs(fill='expr') +
theme(#legend.position="top",
legend.position = c(0.07, 0.83),
legend.title=element_text(size=18, face="bold"),
legend.text=element_text(size=18, face="bold"))
plot(p2)
cutoff <- quantile(peaks$d, probs = c(0.2, 0.8))
hist.y <- density(peaks$d, from = 0, to = 1)
hist.y <- data.frame(x = hist.y$x, y = hist.y$y) %>%
#mutate(aligarea1 = x <= cutoff[1], area2 = (x>=cutoff[1] & x <= cutoff[2]), area3 = x >= cutoff[2]) %>%
mutate(aligns = ifelse(x <= cutoff[1], 'well', ifelse(x>=cutoff[1] & x <= cutoff[2], 'mid',' poor')))
p3 <- ggplot(data = hist.y, aes(x = x, ymin = -0.1, ymax = y, fill = aligns)) +
geom_ribbon(alpha = 0.9) +
geom_line(aes(y = y), size = 1.2) +
geom_vline(aes(xintercept=cutoff[1]),
color="black", linetype="dashed", size=0.8) +
geom_vline(aes(xintercept=cutoff[2]),
color="black", linetype="dashed", size=0.8) +
ylab("density") + xlab("dtw distance") + theme_minimal() +
theme(
axis.text = element_text(size = 18, face = "bold", color = 'black'),
#plot.title = element_text(size=16, face = "bold.italic", color = 'black'),
axis.title.x = element_text(size=22, face="bold"),
axis.title.y = element_text(size=22, face="bold")
) + theme(legend.position="none")
plot(p3)
```
## Figure 4A
```{r echo=FALSE}
#all.markers.sig.cell.cycle.phase.rds
markers.sig <- readRDS('./rds/all.markers.sig.cell.cycle.phase.rds')
clusters <- c('G', 'SM', 'MC', 'C')
spps <- c('Bbig', 'Bbov', 'Bdiv_cow', 'Bdiv_human')
titles <- c("B. big", "B. bov", "B. div (cow)", "B. div (human)")
for(i in 1:length(titles)){
markers.sig[[i]]$markers.sig$spp <- spps[i]
}
all.markers.sig <- do.call(Map, c(f = rbind, markers.sig))
all.markers.sig <- all.markers.sig$markers.sig
all.markers.sig$spp <- factor(all.markers.sig$spp, levels = c('Bbig', 'Bbov', 'Bdiv_cow', 'Bdiv_human'))
names(all.markers.sig) <- gsub("cluster", "phase", names(all.markers.sig))
all.markers.sig$phase <- factor(all.markers.sig$phase, levels = c('G', 'SM', 'MC', 'C'))
all.markers.sig.stat <- all.markers.sig %>% group_by(spp, phase) %>% summarise(num.deg = n())
p <- ggplot(all.markers.sig.stat, aes(x=phase, y=num.deg, fill = phase, color = phase)) +
geom_bar(stat="identity",size = 2, width = 0.75, aes(fill = phase))+
scale_color_manual(values = c('G' = "#1b5878","SM" ="#c44237",'MC' = '#ad8842', 'C' = '#e99093'))+
scale_fill_manual(values = c('G' = "#758FA2","SM" = "#D4807F",'MC' = "#C3AE85", 'C' = "#EAADAF"))+
geom_text(aes(label=num.deg), vjust=1.15, color="black", size=5, fontface = 'bold')+
facet_grid(. ~ spp, scales = "free", space='free',labeller=label_wrap_gen(multi_line = TRUE))+
theme_bw()+
theme(
axis.text.x = element_blank(),
#axis.text.y = element_text(size = 10),
axis.text.y = element_blank(),
axis.ticks = element_blank(),
axis.title.x = element_text(size = 14, face = "bold"),
axis.title.y = element_text(size = 14, face = "bold"))+
theme(strip.background=element_rect(fill='white', color = 'black'),
panel.spacing = unit(1.5, "lines"),
strip.text.x=element_text(angle=0, hjust=0.5,vjust=0.5, size = 14,face = 'bold'),
plot.title = element_text(size=16, face = "bold.italic", color = 'black'),
axis.title.x = element_text(size=12, face="bold"),
axis.title.y = element_text(size=12, face="bold"))+
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
#panel.border = element_blank(),
panel.spacing = unit(0.5, "lines"),
strip.text = element_text(face = "bold", size = 24, angle = 0),
strip.placement = "outside") +
theme(legend.text = element_text(face = "bold", size = 10),
legend.title = element_text(face = "bold", size = 14))+
theme(panel.spacing = unit(1.5, "lines"))
#theme(legend.position = "none")
p
```
## Figure 4B
```{r}
markers.sig <- readRDS('./rds/all.markers.sig.cell.cycle.phase.rds')
clusters <- c('G', 'SM', 'MC', 'C')
spps <- c('Bbig', 'Bbov', 'Bdiv_cow', 'Bdiv_human')
titles <- c("B. big", "B. bov", "B. div (cow)", "B. div (human)")
for(i in 1:length(titles)){
markers.sig[[i]]$markers.sig$spp <- spps[i]
}
all.markers.sig <- do.call(Map, c(f = rbind, markers.sig))
all.markers.sig <- all.markers.sig$markers.sig
all.markers.sig$spp <- factor(all.markers.sig$spp, levels = c('Bbig', 'Bbov', 'Bdiv_cow', 'Bdiv_human'))
names(all.markers.sig) <- gsub("cluster", "phase", names(all.markers.sig))
all.markers.sig$phase <- factor(all.markers.sig$phase, levels = c('G', 'SM', 'MC', 'C'))
all.markers.sig.stat <- all.markers.sig %>% group_by(spp, phase) %>% summarise(num.deg = n())
all.markers.stacked <- all.markers.sig.stat %>% group_by(spp) %>% mutate(num.spp.total.markers = sum(num.deg))
all.markers.stacked <- all.markers.stacked %>% rowwise() %>% mutate(prop.phase.spp = num.deg / num.spp.total.markers)
p <- ggplot(all.markers.stacked, aes(x=phase, y=prop.phase.spp, fill = spp)) +
geom_bar(stat="identity", position = "stack", size = 1, width = 0.75, aes(fill = spp, color = spp))+
scale_fill_manual(values =c("Bbig" = "#E8B5B4","Bbov" ="#D09FE9",
'Bdiv_cow' = "#BFB9D6", 'Bdiv_human' = "#CED5BD"))+
scale_color_manual(values = c("Bbig" = "firebrick","Bbov" ="darkorchid3",
'Bdiv_cow' = 'darkslateblue', 'Bdiv_human' = 'darkolivegreen4'))+
geom_text(aes(label= round(prop.phase.spp, digits = 2)),
position = position_stack(vjust = 0.5), color="black", size=6, fontface = 'bold')+
theme_bw()+
theme(
axis.text.x = element_text(size = 10, face = "bold", color = "black"),
axis.text.y = element_text(size = 10, face = "bold", color = "black"),
axis.ticks = element_blank(),
axis.title.x = element_text(size = 14, face = "bold"),
axis.title.y = element_text(size = 14, face = "bold"),
plot.title = element_text(size=14, face = "bold.italic", color = 'black')) +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
#panel.border = element_blank()
) +
theme(legend.text = element_text(face = "bold", size = 14),
legend.title = element_text(face = "bold", size = 14),
#legend.justification = c(0.9, 0.9), legend.position = c(0.9, 0.9)
legend.position = "none") +
ylab("proportion")
plot(p)
```
## Figure 4c
```{r}
cons.markers.phases.sig.cc <- readRDS("./rds/conserved_markers_cross_spp_phase_based.rds")
cons.markers.phases.sig.cc.stat <- cons.markers.phases.sig.cc %>% group_by(phase) %>%
summarise(genes = list(GeneID), num.deg = n())
cons.markers.phases.sig.cc.stat$phase <- factor(cons.markers.phases.sig.cc.stat$phase, levels = c('C', 'MC', 'SM', 'G'))
p <- ggplot(cons.markers.phases.sig.cc.stat, aes(x=num.deg, y=phase, fill = phase, color = phase)) +
geom_bar(stat="identity", size = 2, width = 0.75, aes(fill = phase))+
scale_color_manual(values = c('G' = "#1b5878","SM" ="#c44237",
'MC' = '#ad8842', 'C' = '#e99093'))+
scale_fill_manual(values = c('G' = "#758FA2","SM" = "#D4807F",
'MC' = "#C3AE85", 'C' = "#EAADAF"))+
geom_text(aes(label=num.deg), hjust=1.15, color="black", size=12, fontface = 'bold')+
theme_bw()+
theme(axis.text.x = element_text(face="bold", size=30, color = "black",angle=0, hjust = 1),
axis.text.y = element_text(face="bold", size=30, color = "black", angle=0),
axis.title.y = element_text(size=28, face="bold"),
axis.title.x = element_text(size=28, face="bold"))+
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
panel.spacing = unit(0.5, "lines"))+
theme(axis.line = element_line(color = 'black'),
axis.ticks = element_blank())+
theme(legend.text = element_text(face = "bold", size = 18),
legend.title = element_text(face = "bold", size = 20))+
theme(legend.justification = "top")+
scale_x_continuous(expand = c(0,0))
p
```
## Fiigure 4D
```{r echo=FALSE}
spp.markers <- readRDS("./rds/species_specific_sig_markers_phase_based_stat.rds")
p <- ggplot(spp.markers, aes(x=num.deg, y = spp, fill = spp, color = spp)) +
geom_bar(stat="identity", size = 2, width = 0.75, aes(fill = spp)) +
scale_color_manual(values = c("Bbig" = "firebrick","Bbov" = "darkorchid3",
'Bdiv_cow' = 'darkslateblue', 'Bdiv_human' = 'darkolivegreen4'))+
scale_fill_manual(values =c("Bbig" = "#E8B5B4","Bbov" = "#D09FE9",
'Bdiv_cow' = "#BFB9D6", 'Bdiv_human' = "#CED5BD"))+
geom_text(aes(label=num.deg), hjust= 1, color="black", size = 6, fontface = 'bold')+
facet_grid(phase~., scales = "free",
space='free',switch = "y", labeller = label_wrap_gen(multi_line = TRUE)) +
theme_bw()+
theme(axis.text.y = element_blank(),
axis.text.x = element_text(face="bold", size = 20, hjust = 1, color = "black"),
axis.title.y = element_text(size = 24, face = "bold"),
axis.title.x = element_text(size = 24, face = "bold"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
panel.spacing = unit(0.5, "lines"),
strip.background = element_blank(),
strip.text = element_text(face = "bold", size = 28),
strip.text.y.left = element_text(angle = 0),
strip.placement = "outside",
axis.line = element_line(color = 'black'),
axis.ticks = element_blank(),
legend.text = element_text(face = "bold", size = 20),
legend.title = element_text(face = "bold", size = 18)
)+
scale_x_continuous(expand = c(0,0))
plot(p)
```
## Figure 5
```{r}
sc.tc.mus.scale.df <- readRDS('./rds/all_sc_tc_mus_scale_toxo_inferred_cell_cycle_phases_Marker_phases_Progression_heatmap.rds')
titles <- c("B. big", "B. bov", "B. div (cow)", "B. div (human)")
title.phases <- c("G1.L", "SM", "MC", "C", "G1.E")
names(title.phases) <- c('G1.L', 'SM', 'MC', 'C','G1.E')
ps <- lapply(1:length(titles), function(i){
p <- ggplot(sc.tc.mus.scale.df[[i]], aes(x = t, reorder_within(GeneID, -peak.ord, stage ), fill = y)) +
geom_tile() +
scale_x_discrete(expand=c(0,0)) +
ylab("Genes") + xlab("time/cells") +
scale_fill_gradientn(colours = viridis::inferno(10)) +
facet_grid(marker.phase ~ cell.cycle.phase , scales = 'free', space = 'free',
labeller = labeller(cell.cycle.phase = title.phases)) +
theme(
axis.text.x = element_blank(),
axis.text.y = element_blank(),
axis.ticks = element_blank(),
axis.title.y = element_blank(),
axis.title.x = element_text(size = 20, face = "bold"),
legend.position = "none",
panel.spacing = unit(0.1, "lines"),
strip.background=element_rect(fill='white', color = 'black'),
strip.text.x = element_text(size = 4, face = 'bold', color = "black"),
strip.text.y = element_text(size = 10, face = 'bold'),
#strip.text.x = element_text(margin = margin(1,0,1,0, "cm")),
plot.title = element_text(size=18, face = "bold.italic", color = 'black', hjust = 0.5),
) + ggtitle(titles[i])
})
p <- grid.arrange(ps[[1]], ps[[2]], ps[[3]], ps[[4]], ncol = 4)
```
## Figure 6 A
```{r}
Bdiv.hum.vs.cow.markers <- readRDS("./rds/bdiv_human_vs_bdiv_cow.rds")
Bdiv.hum.vs.cow.markers.stat <- Bdiv.hum.vs.cow.markers %>% group_by(ref.spp, phase) %>%
summarise(genes = list(GeneID), num.deg = n())
colnames(Bdiv.hum.vs.cow.markers.stat) <- gsub("ref.spp", "spp", colnames(Bdiv.hum.vs.cow.markers.stat))
Bdiv.hum.vs.cow.markers.stat$phase <- factor(Bdiv.hum.vs.cow.markers.stat$phase, levels = c('G', 'SM', 'MC', 'C'))
p <- ggplot(Bdiv.hum.vs.cow.markers.stat, aes(x=num.deg, y=spp, fill = spp, color = spp)) +
geom_bar(stat="identity", size = 2, width = 0.75, alpha = 0.4, aes(fill=spp))+
scale_color_manual(values = c('Bdiv_cow' = 'darkslateblue', 'Bdiv_human' = 'darkolivegreen4'))+
scale_fill_manual(values = c('Bdiv_cow' = 'darkslateblue', 'Bdiv_human' = 'darkolivegreen4'))+
geom_text(aes(label=num.deg), hjust= 1.75, color="black", size=8, fontface = 'bold')+
theme_bw()+
facet_grid(phase~., scales = "free", space='free',switch = "y",
labeller=label_wrap_gen(multi_line = TRUE))+
theme(#axis.text.x = element_blank(),
axis.text.x = element_text(face="bold", size=18, angle=0, hjust = 1, color = "black"),
axis.title.y = element_text(size=22, face="bold"),
axis.title.x = element_text(size=22, face="bold"),
#axis.text.y = element_text(face="bold", size=12, angle=0),
axis.text.y = element_blank())+
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
panel.spacing = unit(0.5, "lines"),
strip.background = element_blank(),
strip.text = element_text(face = "bold", size = 22, angle = 0),
strip.placement = "outside")+
theme(axis.line = element_line(color = 'black'),
axis.ticks = element_blank())+
theme(legend.text = element_text(face = "bold", size = 18),
legend.title = element_text(face = "bold", size = 20))+
theme(strip.text.y.left = element_text(angle = 0))
p
Bdiv.hum.vs.cow.markers <- readRDS("./rds/bdiv_human_vs_bdiv_cow.rds")
markers.int.local.bdivs.sig.top <- Bdiv.hum.vs.cow.markers %>% group_by(ref.spp) %>% slice_max(n = 1, order_by = avg_log2FC.x)
S.O.integrated <- readRDS('./rds/S.O.integrated.list.pstime.GAM.indiv.cell.cycle.phase_sinlge_obj.rds')
DefaultAssay(S.O.integrated) <- 'RNA'
Idents(S.O.integrated) <- 'spp'
S.O.int.bdivs <- subset(S.O.integrated, ident = c('Bdiv_human', 'Bdiv_cow'))
DefaultAssay(S.O.int.bdivs) <- 'RNA'
Idents(S.O.int.bdivs) <- 'phase.spp'
[email protected] <- [email protected] %>% mutate(host = ifelse( spp == "Bdiv_human", "Bdiv_human", "Bdiv_cow"))
S.O.int.bdivs[['pca']]@cell.embeddings[,2] = -S.O.int.bdivs[['pca']]@cell.embeddings[,2]
S.O.int.bdivs[['pca']]@cell.embeddings[,1] = -S.O.int.bdivs[['pca']]@cell.embeddings[,1]
p <- FeaturePlot(object = S.O.int.bdivs,
features = rev.default(markers.int.local.bdivs.sig.top$gene.x), label = F,repel = F,
#shape.by = 'spp',
split.by = 'host',
cols = c("grey", "blue"), reduction = "pca")
plot(p)
DefaultAssay(S.O.int.bdivs) <- 'RNA'
Idents(S.O.int.bdivs) <- 'spp'
[email protected]$spp <- factor([email protected]$spp, levels = c("Bdiv_cow", "Bdiv_human"))
top.markers <- markers.int.local.bdivs.sig.top$GeneID
p <- VlnPlot(S.O.int.bdivs, features = gsub('_', '-', top.markers))
pp <- lapply(1:2, function(i){
plt <- p[[i]] +
scale_fill_manual(values = c('Bdiv_cow' = 'darkslateblue', 'Bdiv_human' = 'darkolivegreen4'))+
theme(axis.text.x = element_text(face = "bold", size = 16, angle = 0, hjust = 0.5),
axis.text.y = element_text(face = "bold", size = 18),
axis.title.x = element_blank(),
axis.title.y = element_text(face = "bold", size = 20),
plot.title = element_text(face = "bold", size = 20)) +
ylab("expr")
})
p2 <- grid.arrange(pp[[2]], pp[[1]], ncol = 1)
```
## Figure 6 B
```{r}
host.markers.int.local.sig.specific.filt <- readRDS("./rds/human_vs_cow.rds")
host.markers.int.local.sig.stats.filt <- host.markers.int.local.sig.specific.filt %>%
group_by(phase, ref.host) %>%
summarise(genes = list(GeneID), num.deg = n())
host.markers.int.local.sig.stats.filt$phase <- factor(host.markers.int.local.sig.stats.filt$phase,
levels = c('G', 'SM', 'MC', 'C'))
host.markers.int.local.sig.stats.filt$ref.host <- factor(host.markers.int.local.sig.stats.filt$ref.host,
levels = c('cow', 'human'))
colnames(host.markers.int.local.sig.stats.filt) <- gsub("ref.host", "host",
colnames(host.markers.int.local.sig.stats.filt))
host.markers.int.local.filt.top <- host.markers.int.local.sig.specific.filt %>% group_by(ref.host) %>%
slice_max(n = 1, order_by = avg_log2FC.x)
p <- ggplot(host.markers.int.local.sig.stats.filt,
aes(x=num.deg, y=host, fill = host, color = host)) +
geom_bar(stat="identity", size = 2, width = 0.75, alpha = 0.4, aes(fill=host))+
scale_fill_manual(values = c('cow' = 'darkgoldenrod', 'human' = 'darkolivegreen4'))+
scale_color_manual(values = c('cow' = 'darkgoldenrod', 'human' = 'darkolivegreen4'))+
geom_text(aes(label=num.deg), hjust= 1.15, color="black", size=10, fontface = 'bold')+
theme_bw()+
facet_grid(phase~., scales = "free", space='free',switch = "y",labeller=label_wrap_gen(multi_line = TRUE))+
theme(#axis.text.x = element_blank(),
axis.text.x = element_text(face="bold", size = 20, color = "black", angle=0, hjust = 1),
axis.title = element_blank(),
#axis.title.y = element_text(size=24, face="bold"),
axis.title.x = element_text(size=24, face="bold"),
#axis.text.y = element_text(face="bold", size=12, angle=0),
axis.text.y = element_blank())+
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
panel.spacing = unit(0.5, "lines"),
strip.background = element_blank(),
strip.text = element_text(face = "bold", size = 24, angle = 0),
strip.placement = "outside")+
theme(axis.line = element_line(color = 'black'),
axis.ticks = element_blank())+
theme(legend.text = element_text(face = "bold", size = 20),
legend.title = element_text(face = "bold", size = 22))+
theme(strip.text.y.left = element_text(angle = 0))
p
S.O.integrated <- readRDS('./rds/S.O.integrated.list.pstime.GAM.indiv.cell.cycle.phase_sinlge_obj.rds')
DefaultAssay(S.O.integrated) <- "RNA"
mutate(host = ifelse( spp == "Bdiv_human", "human", "cow"))
Idents(S.O.integrated) <- 'host'
host.markers.int.local.filt.top <- host.markers.int.local.sig.specific.filt %>% group_by(ref.host) %>%
slice_max(n = 1, order_by = avg_log2FC.x)
S.O.integrated[['pca']]@cell.embeddings[,2] = -S.O.integrated[['pca']]@cell.embeddings[,2]
S.O.integrated[['pca']]@cell.embeddings[,1] = -S.O.integrated[['pca']]@cell.embeddings[,1]
p <- FeaturePlot(object = S.O.integrated,
features = rev.default(host.markers.int.local.filt.top$gene.x), label = F,repel = F,
#shape.by = 'spp',
split.by = 'host',
cols = c("grey", "blue"), reduction = "pca")
p
DefaultAssay(S.O.integrated) <- "RNA"
[email protected] <- [email protected] %>% mutate(host = ifelse( spp == "Bdiv_human", "human", "cow"))
[email protected]$host.phase <- paste([email protected]$host, [email protected]$cell.cycle.phase, sep = ":")
unique([email protected]$host.phase)
Idents(S.O.integrated) <- 'host.phase'
top.markers <- host.markers.int.local.filt.top$GeneID
p <- VlnPlot(subset(S.O.integrated, idents = c('cow:C', 'human:C')), features = gsub('_', '-', top.markers))
pp <- lapply(1:length(unique(top.markers)), function(i){
plt <- p[[i]] +
scale_fill_manual(values = c('cow:C' = 'darkgoldenrod', 'human:C' = 'darkolivegreen4'))+
theme(axis.text.x = element_text(face = "bold", size = 16, angle = 0, hjust = 0.5),
axis.text.y = element_text(face = "bold", size = 18),
axis.title.x = element_blank(),
axis.title.y = element_text(face = "bold", size = 20),
plot.title = element_text(face = "bold", size = 20)) +
ylab("expr")
})
p2 <- grid.arrange(pp[[2]], pp[[1]], nrow = 2)
```
## figure 7
## Please check the web application (https://umbibio.math.umb.edu/babesiasc/) for the network figure 7C.
```{r echo=FALSE}
top.10.cons <- read.xlsx("./rds/top_10_conserved_genes_network_2_clusters.xlsx")
top.10.cons$GeneID <- gsub("Bdiv_", "", top.10.cons$GeneID)
df.plot <- top.10.cons
cluster.dtw <- paste("cluster", 1:2, sep = " ")
p <- lapply(unique(top.10.cons$cluster), function(i){
tmp <- df.plot %>% dplyr::filter(cluster == i)
ggplot(tmp, aes(x = t, y = y, color = GeneID)) +
facet_grid(spp ~ .)+
geom_line(size = 0.5) +
theme_bw() +
ggtitle(cluster.dtw[i]) +
theme(
strip.text = element_blank(),
strip.background = element_rect(fill = "white", colour = "black"),
strip.text.y = element_text(size = 10, face = 'bold', color = "black"),
plot.title = element_text(size = 14, face = "bold.italic", hjust = 0.5),
axis.title.y = element_text(size = 14, face = "bold"),
axis.title.x = element_text(size = 14, face = "bold"),
legend.title = element_text(size = 14, face = "bold"),
legend.text = element_text(size = 14, face = "bold"),
axis.text.x = element_text(size = 10, color = "black", angle = 90),
axis.text.y = element_text(size = 10, color = "black", angle = 0),
) +
theme(strip.text.y = element_text(margin = margin(2,1,2,1, "cm")),
panel.spacing = unit(0.75, "lines"))+
ylab("expr") + xlab("time")
})
p2 <- do.call(grid.arrange, c(p, nrow=1))
```
## Figure 8 A
## Please check web application (https://umbibio.math.umb.edu/babesiasc/) for the network figure 8C.
```{r}
## 8A
epi.reg <- read.xlsx("./rds/epigenetics_regulators_dtw_clusters.xlsx")
epi.reg$GeneID <- gsub("Bdiv_", "", epi.reg$GeneID)
cluster.dtw <- paste("cluster", c(1:4), sep = " ")
p <- lapply(unique(epi.reg$cluster), function(i){
tmp <- epi.reg %>% dplyr::filter(cluster == i)
ggplot(tmp, aes(x = t, y = y, color = GeneID)) +
facet_grid(spp ~ .)+
geom_line(size = 0.5) +
theme_bw() +
ggtitle(cluster.dtw[i]) +
theme(strip.background = element_rect(fill = "white", colour = "black"),
strip.text.y = element_text(size = 10, face = 'bold', color = "black"),
plot.title = element_text(size = 14, face = "bold.italic", hjust = 0.5),
axis.title.x = element_text(size = 14, face = "bold", color = "black"),
axis.title.y = element_text(size = 12, face = "bold", color = "black"),
legend.title = element_text(size = 12, face = "bold"),
legend.text = element_text(size = 11, face = "bold"),
axis.text.y = element_text(size = 8, face = "bold", color = "black"),
axis.text.x = element_text(size = 12, face = "bold", color = "black", angle = 90),
#axis.text.x = element_blank(),
legend.margin=margin(0,0,0,0))+
ylab("expr") + xlab("time")
})
grid.arrange(grobs = p, ncol = 2)
```
## Figure 8B