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util_funcs_YR.R
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## this function plots rna and atac profile of genes in a table of interest
plot_rna_atac <- function(sc.rna.sc.atac.joint.long.sub){
p <- ggplot(sc.rna.sc.atac.joint.long.sub, aes(x= time,y=normExpr)) +
geom_path(aes(color = GeneID),alpha = 0.8, size = 0.8)+
theme_bw() +
theme(legend.position = "right") +
ylab('normExpr') + xlab('Time') +
theme(axis.text.x = element_text(angle = 0, size = 20, face="bold", colour = "black")) +
theme(axis.text.y = element_text(angle = 0, size = 20, face="bold", colour = "black")) +
theme(strip.background = element_rect(colour="black", fill="white",
size=0.5, linetype="solid")) +
theme(strip.text = element_text(size = 20, face="bold", angle = 0)) +
coord_cartesian(xlim = c(0,6.5)) +
facet_grid(. ~ data, scales = 'free', space = 'free') +
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 = c(0.15, 0.85),
legend.position = 'none',
legend.title = element_text(colour="black", size=12,
face="bold"),
legend.text = element_text(colour="black", size=12,
face="bold"))
return(p)
}
## this function extracts the expression and accessibility profile of genes of interest
## need to give rna and atac splines and genes of interest as input
## set the scale T/F
get_rna_atac_profile <- function(rna.splines, atac.splines, genes.tab, scale = T) {
sc.rna.dtw.wide <- rna.splines %>%
pivot_wider(names_from = 'GeneID', values_from = 'y') %>%
mutate_at(vars(matches("TGME")), ~scale(., center = T, scale = scale)) %>%
as.data.frame()
sc.atac.dtw.wide <- atac.splines %>%
pivot_wider(names_from = 'GeneID', values_from = 'y') %>%
mutate_at(vars(matches("TGME")), ~scale(., center = T, scale = scale)) %>%
as.data.frame()
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')
## table of genes to plot their expression
tab.genes <- data.frame(TGME49 = gsub("_", "-", genes.tab$gene_name),
Name = genes.tab$ProductDescription)
sc.rna.long <- inner_join(sc.rna.mu.scale, tab.genes, by = c('GeneID' = 'TGME49'))
sc.rna.long <- sc.rna.long %>%
transmute(time = x, GeneID = GeneID, normExpr = expr, Name = Name) %>% distinct()
sc.atac.long <- inner_join(sc.atac.mu.scale, tab.genes, by = c('GeneID' = 'TGME49'))
sc.atac.long <- sc.atac.long %>%
transmute(time = x, GeneID = GeneID, normExpr = expr, Name = Name) %>% distinct()
sc.rna.sc.atac.joint <- inner_join(sc.rna.long, sc.atac.long,
by = c("time", "GeneID", "Name"))
colnames(sc.rna.sc.atac.joint) <- c("time", "GeneID", "scRNA", "Name", "scATAC")
sc.rna.sc.atac.joint.long <- sc.rna.sc.atac.joint %>%
pivot_longer(-c('time', "GeneID", "Name"),
names_to = 'data', values_to = 'normExpr')
return(sc.rna.sc.atac.joint.long)
}
## This function performs dynamic time warping clustering for both rna and atac profiles
## it gets a table of genes,
## the table should include the product description (gene ID and gene Name)
## you can specify the number of clusters you are looking for (cannot be less than 2)
clust.df <- function(tab , num.clust) {
k <- num.clust
sc.rna <- sc.rna.dtw.wide[,colnames(sc.rna.dtw.wide) %in% tab$gene_name ]
sc.atac <- sc.atac.dtw.wide[,colnames(sc.atac.dtw.wide) %in% tab$gene_name]
sc.atac<- sc.atac.dtw.wide[,colnames(sc.atac.dtw.wide) %in% colnames(sc.rna)]
sc.rna.markers.hc_dtw <- dtwClustCurves(sc.rna, nclust = k)
sc.atac.markers.hc_dtw <- dtwClustCurves(sc.atac, nclust = k)
tab <- tab[tab$gene_name %in% colnames(sc.rna),]
sc.rna.long <- inner_join(sc.rna.mu.scale, tab, by = c('GeneID' = 'gene_name'))
sc.rna.long <- sc.rna.long %>%
transmute(time = x, GeneID = GeneID, normExpr = expr, Name = ProductDescription) %>% distinct()
sc.atac.long <- inner_join(sc.atac.mu.scale, tab, by = c('GeneID' = 'gene_name'))
sc.atac.long <- sc.atac.long %>%
transmute(time = x, GeneID = GeneID, normExpr = expr, Name = ProductDescription) %>% distinct()
sc.rna.clust.info <- data.frame(GeneID = colnames(sc.rna), cluster = cutree(sc.rna.markers.hc_dtw, k = k))
sc.atac.clust.info <- data.frame(GeneID = colnames(sc.atac), cluster = cutree(sc.atac.markers.hc_dtw, k = k))
sc.rna.long.clust <- inner_join(sc.rna.long, sc.rna.clust.info, by = 'GeneID')
sc.atac.long.clust <- inner_join(sc.atac.long, sc.atac.clust.info, by = 'GeneID')
sc.rna.sc.atac.joint <- inner_join(sc.rna.long.clust, sc.atac.long.clust,
by = c("time", "GeneID", "Name"))
colnames(sc.rna.sc.atac.joint) <- c("time", "GeneID", "scRNA", "Name", "cluster.RNA", "scATAC", "cluster.ATAC")
sc.rna.sc.atac.joint.long <- sc.rna.sc.atac.joint %>%
pivot_longer(-c('time', "GeneID", "Name", "cluster.RNA", "cluster.ATAC"),
names_to = 'data', values_to = 'normExpr')
sc.rna.sc.atac.joint.long$cluster.RNA <- paste('C', sc.rna.sc.atac.joint.long$cluster.RNA)
sc.rna.sc.atac.joint.long$cluster.ATAC <- paste('C', sc.rna.sc.atac.joint.long$cluster.ATAC)
return(sc.rna.sc.atac.joint.long)
}
clust.atac.df <- function(tab, num.clust = num.clust){
tab <- tab
k <- num.clust
sc.atac <- sc.atac.dtw.wide[,colnames(sc.atac.dtw.wide) %in% tab$gene_name]
sc.atac.markers.hc_dtw <- dtwClustCurves(sc.atac, nclust = k)
tab <- tab[tab$gene_name %in% colnames(sc.atac),]
sc.atac.long <- inner_join(sc.atac.mu.scale, tab, by = c('GeneID' = 'gene_name'))
sc.atac.long <- sc.atac.long %>%
transmute(time = x, GeneID = GeneID, normExpr = expr, group = group, trans.cluster.rna) %>%
distinct()
sc.atac.clust.info <- data.frame(GeneID = colnames(sc.atac), cluster = cutree(sc.atac.markers.hc_dtw, k = k))
sc.atac.long.clust <- inner_join(sc.atac.long, sc.atac.clust.info, by = 'GeneID')
colnames(sc.atac.long.clust) <- c("time", "GeneID", "normExpr", "group", "trans.cluster.rna", "cluster.ATAC")
sc.atac.long.clust$cluster.ATAC <- paste('C', sc.atac.long.clust$cluster.ATAC)
return(sc.atac.long.clust)
}
plot_atac_trand <- function(sc.atac.long.clust){
p <- ggplot(sc.atac.long.clust, aes(x= time,y=normExpr)) +
geom_path(aes(color = GeneID),alpha = 0.8, size = 0.8)+
theme_bw(base_size = 14) +
theme(legend.position = "right") +
ylab('normExpr') + xlab('Time') +
theme(axis.text.x = element_text(angle = 0, hjust = 1, size = 15, face="bold", colour = "black")) +
theme(axis.text.y = element_text(angle = 0, hjust = 1, size = 15, face="bold", colour = "black")) +
theme(strip.background = element_rect(colour="black", fill="white",size=0.5, linetype="solid")) +
theme(strip.text = element_text(size = 18, face="bold", angle = 0)) +
coord_cartesian(xlim = c(0,6.5)) +
facet_grid(cluster.ATAC ~ ., scales = 'free', space = 'free') +
theme(
plot.title = element_text(size=14, face = "bold.italic", color = 'red'),
axis.title.x = element_text(size=18, face="bold", hjust = 1),
axis.title.y = element_text(size=18, face="bold")
) +
theme(#legend.position = c(0.15, 0.85),
legend.position = 'none',
legend.title = element_text(colour="black", size=12,
face="bold"),
legend.text = element_text(colour="black", size=12,
face="bold")) +
theme(axis.ticks = element_blank())
return(p)
}
clust.atac.df <- function(tab, num.clust = num.clust){
tab <- tab
k <- num.clust
sc.atac <- sc.atac.dtw.wide[,colnames(sc.atac.dtw.wide) %in% tab$gene_name]
sc.atac.markers.hc_dtw <- dtwClustCurves(sc.atac, nclust = k)
tab <- tab[tab$gene_name %in% colnames(sc.atac),]
sc.atac.long <- inner_join(sc.atac.mu.scale, tab, by = c('GeneID' = 'gene_name'))
sc.atac.long <- sc.atac.long %>%
transmute(time = x, GeneID = GeneID, normExpr = expr, group = group, trans.cluster.rna) %>%
distinct()
sc.atac.clust.info <- data.frame(GeneID = colnames(sc.atac), cluster = cutree(sc.atac.markers.hc_dtw, k = k))
sc.atac.long.clust <- inner_join(sc.atac.long, sc.atac.clust.info, by = 'GeneID')
colnames(sc.atac.long.clust) <- c("time", "GeneID", "normExpr", "group", "trans.cluster.rna", "cluster.ATAC")
sc.atac.long.clust$cluster.ATAC <- paste('C', sc.atac.long.clust$cluster.ATAC)
return(sc.atac.long.clust)
}
plot_atac_trand <- function(sc.atac.long.clust){
p <- ggplot(sc.atac.long.clust, aes(x= time,y=normExpr)) +
geom_path(aes(color = GeneID),alpha = 0.8, size = 0.8)+
theme_bw(base_size = 14) +
theme(legend.position = "right") +
ylab('normExpr') + xlab('Time') +
theme(axis.text.x = element_text(angle = 0, hjust = 1, size = 15, face="bold", colour = "black")) +
theme(axis.text.y = element_text(angle = 0, hjust = 1, size = 15, face="bold", colour = "black")) +
theme(strip.background = element_rect(colour="black", fill="white",size=0.5, linetype="solid")) +
theme(strip.text = element_text(size = 18, face="bold", angle = 0)) +
coord_cartesian(xlim = c(0,6.5)) +
facet_grid(cluster.ATAC ~ ., scales = 'free', space = 'free') +
theme(
plot.title = element_text(size=14, face = "bold.italic", color = 'red'),
axis.title.x = element_text(size=18, face="bold", hjust = 1),
axis.title.y = element_text(size=18, face="bold")
) +
theme(#legend.position = c(0.15, 0.85),
legend.position = 'none',
legend.title = element_text(colour="black", size=12,
face="bold"),
legend.text = element_text(colour="black", size=12,
face="bold")) +
theme(axis.ticks = element_blank())
return(p)
}
plot_rna_atac_trends.ord <- function(sc.rna.sc.atac.joint.long.sub){
p <- ggplot(sc.rna.sc.atac.joint.long.sub, aes(x= time,y=normExpr)) +
geom_path(aes(color = GeneID),alpha = 0.8, size = 0.8)+
theme_bw() +
#theme_bw(base_size = 16) +
theme(legend.position = "right") +
ylab('normExpr') + xlab('Time') +
theme(axis.text.x = element_text(angle = 0, hjust = 1, size = 22, face="bold", colour = "black")) +
theme(axis.text.y = element_text(angle = 0, hjust = 1, size = 18, face="bold", colour = "black")) +
theme(strip.background = element_rect(colour="black", fill="white",size=0.5, linetype="solid")) +
theme(strip.text = element_text(size = 22, face="bold", angle = 0)) +
coord_cartesian(xlim = c(0,6.5)) +
facet_grid(cluster.RNA.ordered ~ data, scales = 'free', space = 'free') +
theme(
plot.title = element_text(size=20, face = "bold.italic", color = 'red'),
axis.title.x = element_text(size=22, face="bold", hjust = 1),
axis.title.y = element_text(size=22, face="bold")
) +
theme(#legend.position = c(0.15, 0.85),
legend.position = 'none',
legend.title = element_text(colour="black", size=12,
face="bold"),
legend.text = element_text(colour="black", size=12,
face="bold"))
return(p)
}
getPcaMetaData <- function(S.O){
pc <- S.O@[email protected]
pc <- data.frame(pc) %>% dplyr::mutate(Sample = rownames(pc)) %>%
transmute(Sample = Sample, PC_1 = PC_1, PC_2 = PC_2, PC_3 = PC_3)
umap <- S.O@[email protected]
umap <- data.frame(umap) %>% dplyr::mutate(Sample = rownames(umap)) %>%
transmute(Sample = Sample, UMAP_1 = UMAP_1, UMAP_2 = UMAP_2)
meta.data <- data.frame(Sample = rownames([email protected]),
spp = [email protected]$spp2, phase = [email protected]$phase)
meta.data <- left_join(meta.data,
pc, by = 'Sample')
meta.data <- left_join(meta.data, umap, by = 'Sample')
return(meta.data)
}
getPcaMetaData.trans <- function(S.O){
pc <- S.O@[email protected]
pc <- data.frame(pc) %>% dplyr::mutate(Sample = rownames(pc)) %>%
transmute(Sample = Sample, PC_1 = PC_1, PC_2 = PC_2, PC_3 = PC_3)
umap <- S.O@[email protected]
umap <- data.frame(umap) %>% dplyr::mutate(Sample = rownames(umap)) %>%
transmute(Sample = Sample, UMAP_1 = UMAP_1, UMAP_2 = UMAP_2)
meta.data <- data.frame(Sample = rownames([email protected]),
spp = [email protected]$spp2, phase = [email protected]$phase,
transition.rna = [email protected]$transition.rna,
transition.atac = [email protected]$transition.atac)
meta.data <- left_join(meta.data,
pc, by = 'Sample')
meta.data <- left_join(meta.data, umap, by = 'Sample')
return(meta.data)
}
getPcaMetaData.atac.trans <- function(S.O){
pc <- S.O@[email protected]
pc <- data.frame(pc) %>% dplyr::mutate(Sample = rownames(pc)) %>%
transmute(Sample = Sample, PC_1 = PC_1, PC_2 = PC_2, PC_3 = PC_3)
umap <- S.O@[email protected]
umap <- data.frame(umap) %>% dplyr::mutate(Sample = rownames(umap)) %>%
transmute(Sample = Sample, UMAP_1 = UMAP_1, UMAP_2 = UMAP_2)
meta.data <- data.frame(Sample = rownames([email protected]),
spp = [email protected]$spp2,
phase = [email protected]$phase,
#transition.rna = [email protected]$transition.rna,
transition.atac = [email protected]$transition.atac)
meta.data <- left_join(meta.data,
pc, by = 'Sample')
meta.data <- left_join(meta.data, umap, by = 'Sample')
return(meta.data)
}
num.cores <- detectCores(all.tests = FALSE, logical = TRUE)
picewise_scale_V2 <- function(sds.data){
# Scale the pt based on know biology: Radke et. al 2000
G <- c(0, 3) # 3h
S <- c(3, 4.7) # 1.7h
M <- c(4.7, 5) # ~20 min
C <- c(5, 6) # 1h
ind.G1.a <- which(sds.data$phase == 'G1.a')
ind.G1.b <- which(sds.data$phase == 'G1.b')
ind.S <- which(sds.data$phase == 'S')
ind.M <- which(sds.data$phase == 'M')
ind.C <- which(sds.data$phase == 'C')
#t1 <- quantile(t[ind.G1.b], prob=0.25) ## Start of G1.b
t1 <- (quantile(sds.data$pt[ind.G1.a], prob=0.75) +
quantile(sds.data$pt[ind.G1.b], prob=0.25))/2 ## Start of G1.b
t2 <- (quantile(sds.data$pt[ind.G1.b], prob=0.75) +
quantile(sds.data$pt[ind.S], prob=0.25)) / 2 ## End of G1.b, Start of S
t3 <- (quantile(sds.data$pt[ind.S], prob=0.75) +
quantile(sds.data$pt[ind.M], prob=0.25)) / 2## End of S, Start of M
t4 <- (quantile(sds.data$pt[ind.M], prob=0.75) +
quantile(sds.data$pt[ind.C], prob=0.25)) / 2## End of M, Start of C
t0 <- 0
t5 <- 6
# sds.data$pt.shift <- (sds.data$pt + t.shift) %% 6
# sds.data$pt.shift <- 6 * ((sds.data$pt.shift - min(sds.data$pt.shift)) /
# (max(sds.data$pt.shift) - min(sds.data$pt.shift)))
# plot(sds.data$phase, sds.data$pt.shift)
slp.g <- (G[2] - G[1]) / (t2 - t0)
inc.g <- c(t0, G[1])
slp.s <- (S[2] - S[1]) / (t3 - t2)
inc.s <- c(t2, S[1])
slp.m <- (M[2] - M[1]) / (t4 - t3)
inc.m <- c(t3, M[1])
slp.c <- (C[2] - C[1]) / (t5 - t4)
inc.c <- c(t4, C[1])
s.t <- data.frame(pt = seq(0, 6, 0.01))
s.t <- s.t %>% mutate(phase = case_when(pt >= t0 & pt < t1 ~ 'G1.a',
pt >= t1 & pt < t2 ~ 'G1.b',
pt >= t2 & pt < t3 ~ 'S',
pt >= t3 & pt < t4 ~ 'M',
pt >= t4 ~ 'C'),
t = case_when(pt >= t0 & pt < t2 ~ inc.g[2] + slp.g * (pt - inc.g[1]),
pt >= t2 & pt < t3 ~ inc.s[2] + slp.s * (pt - inc.s[1]),
pt >= t3 & pt < t4 ~ inc.m[2] + slp.m * (pt - inc.m[1]),
pt >= t4 ~ inc.c[2] + slp.c * (pt - inc.c[1])))
}
clust.df <- function(tab , num.clust) {
k <- num.clust
sc.rna <- sc.rna.dtw.wide[,colnames(sc.rna.dtw.wide) %in% tab$gene_name ]
sc.atac <- sc.atac.dtw.wide[,colnames(sc.atac.dtw.wide) %in% tab$gene_name]
sc.atac<- sc.atac.dtw.wide[,colnames(sc.atac.dtw.wide) %in% colnames(sc.rna)]
sc.rna.markers.hc_dtw <- dtwClustCurves(sc.rna, nclust = k)
sc.atac.markers.hc_dtw <- dtwClustCurves(sc.atac, nclust = k)
tab <- tab[tab$gene_name %in% colnames(sc.rna),]
sc.rna.long <- inner_join(sc.rna.mu.scale, tab, by = c('GeneID' = 'gene_name'))
sc.rna.long <- sc.rna.long %>%
transmute(time = x, GeneID = GeneID, normExpr = expr, Name = ProductDescription) %>% distinct()
sc.atac.long <- inner_join(sc.atac.mu.scale, tab, by = c('GeneID' = 'gene_name'))
sc.atac.long <- sc.atac.long %>%
transmute(time = x, GeneID = GeneID, normExpr = expr, Name = ProductDescription) %>% distinct()
sc.rna.clust.info <- data.frame(GeneID = colnames(sc.rna), cluster = cutree(sc.rna.markers.hc_dtw, k = k))
sc.atac.clust.info <- data.frame(GeneID = colnames(sc.atac), cluster = cutree(sc.atac.markers.hc_dtw, k = k))
sc.rna.long.clust <- inner_join(sc.rna.long, sc.rna.clust.info, by = 'GeneID')
sc.atac.long.clust <- inner_join(sc.atac.long, sc.atac.clust.info, by = 'GeneID')
sc.rna.sc.atac.joint <- inner_join(sc.rna.long.clust, sc.atac.long.clust,
by = c("time", "GeneID", "Name"))
colnames(sc.rna.sc.atac.joint) <- c("time", "GeneID", "scRNA", "Name", "cluster.RNA", "scATAC", "cluster.ATAC")
sc.rna.sc.atac.joint.long <- sc.rna.sc.atac.joint %>%
pivot_longer(-c('time', "GeneID", "Name", "cluster.RNA", "cluster.ATAC"),
names_to = 'data', values_to = 'normExpr')
sc.rna.sc.atac.joint.long$cluster.RNA <- paste('C', sc.rna.sc.atac.joint.long$cluster.RNA)
sc.rna.sc.atac.joint.long$cluster.ATAC <- paste('C', sc.rna.sc.atac.joint.long$cluster.ATAC)
return(sc.rna.sc.atac.joint.long)
}
plot_rna_atac_trends.ord <- function(sc.rna.sc.atac.joint.long.sub){
p <- ggplot(sc.rna.sc.atac.joint.long.sub, aes(x= time,y=normExpr)) +
geom_path(aes(color = GeneID),alpha = 0.8, size = 0.8)+
theme_bw() +
#theme_bw(base_size = 16) +
theme(legend.position = "right") +
ylab('normExpr') + xlab('Time') +
theme(axis.text.x = element_text(angle = 0, hjust = 1, size = 22, face="bold", colour = "black")) +
theme(axis.text.y = element_text(angle = 0, hjust = 1, size = 18, face="bold", colour = "black")) +
theme(strip.background = element_rect(colour="black", fill="white",size=0.5, linetype="solid")) +
theme(strip.text = element_text(size = 22, face="bold", angle = 0)) +
coord_cartesian(xlim = c(0,6.5)) +
facet_grid(cluster.RNA.ordered ~ data, scales = 'free', space = 'free') +
theme(
plot.title = element_text(size=20, face = "bold.italic", color = 'red'),
axis.title.x = element_text(size=22, face="bold", hjust = 1),
axis.title.y = element_text(size=22, face="bold")
) +
theme(#legend.position = c(0.15, 0.85),
legend.position = 'none',
legend.title = element_text(colour="black", size=12,
face="bold"),
legend.text = element_text(colour="black", size=12,
face="bold"))
return(p)
}
#
## This function performs dynamic time warping clustering for both rna and atac profiles
## it gets a table of genes,
## the table should include the product description (gene ID and gene Name)
## you can specify the number of clusters you are looking for (cannot be less than 2)
clust.df <- function(tab , num.clust) {
k <- num.clust
sc.rna <- sc.rna.dtw.wide[,colnames(sc.rna.dtw.wide) %in% tab$gene_name ]
sc.atac <- sc.atac.dtw.wide[,colnames(sc.atac.dtw.wide) %in% tab$gene_name]
sc.atac<- sc.atac.dtw.wide[,colnames(sc.atac.dtw.wide) %in% colnames(sc.rna)]
sc.rna.markers.hc_dtw <- dtwClustCurves(sc.rna, nclust = k)
sc.atac.markers.hc_dtw <- dtwClustCurves(sc.atac, nclust = k)
tab <- tab[tab$gene_name %in% colnames(sc.rna),]
sc.rna.long <- inner_join(sc.rna.mu.scale, tab, by = c('GeneID' = 'gene_name'))
sc.rna.long <- sc.rna.long %>%
transmute(time = x, GeneID = GeneID, normExpr = expr, Name = ProductDescription) %>% distinct()
sc.atac.long <- inner_join(sc.atac.mu.scale, tab, by = c('GeneID' = 'gene_name'))
sc.atac.long <- sc.atac.long %>%
transmute(time = x, GeneID = GeneID, normExpr = expr, Name = ProductDescription) %>% distinct()
sc.rna.clust.info <- data.frame(GeneID = colnames(sc.rna), cluster = cutree(sc.rna.markers.hc_dtw, k = k))
sc.atac.clust.info <- data.frame(GeneID = colnames(sc.atac), cluster = cutree(sc.atac.markers.hc_dtw, k = k))
sc.rna.long.clust <- inner_join(sc.rna.long, sc.rna.clust.info, by = 'GeneID')
sc.atac.long.clust <- inner_join(sc.atac.long, sc.atac.clust.info, by = 'GeneID')
sc.rna.sc.atac.joint <- inner_join(sc.rna.long.clust, sc.atac.long.clust,
by = c("time", "GeneID", "Name"))
colnames(sc.rna.sc.atac.joint) <- c("time", "GeneID", "scRNA", "Name", "cluster.RNA", "scATAC", "cluster.ATAC")
sc.rna.sc.atac.joint.long <- sc.rna.sc.atac.joint %>%
pivot_longer(-c('time', "GeneID", "Name", "cluster.RNA", "cluster.ATAC"),
names_to = 'data', values_to = 'normExpr')
sc.rna.sc.atac.joint.long$cluster.RNA <- paste('C', sc.rna.sc.atac.joint.long$cluster.RNA)
sc.rna.sc.atac.joint.long$cluster.ATAC <- paste('C', sc.rna.sc.atac.joint.long$cluster.ATAC)
return(sc.rna.sc.atac.joint.long)
}
## plot the expression and accessibility of genes within each cluster
# facet by rna cluster
plot_rna_atac_trends <- function(sc.rna.sc.atac.joint.long.sub){
p <- ggplot(sc.rna.sc.atac.joint.long.sub, aes(x= time,y=normExpr)) +
geom_path(aes(color = GeneID),alpha = 0.8, size = 0.8)+
theme_bw(base_size = 14) +
theme(legend.position = "right") +
ylab('normExpr') + xlab('Time') +
theme(axis.text.x = element_text(angle = 0, hjust = 1, size = 18, face="bold", colour = "black")) +
theme(axis.text.y = element_text(angle = 0, hjust = 1, size = 18, face="bold", colour = "black")) +
theme(strip.background = element_rect(colour="black", fill="white",size=0.5, linetype="solid")) +
theme(strip.text = element_text(size = 18, face="bold", angle = 0)) +
coord_cartesian(xlim = c(0,6.5)) +
facet_grid(cluster.RNA ~ data, scales = 'free', space = 'free') +
theme(
plot.title = element_text(size=20, face = "bold.italic", color = 'red'),
axis.title.x = element_text(size=18, face="bold", hjust = 1),
axis.title.y = element_text(size=18, face="bold")
) +
theme(#legend.position = c(0.15, 0.85),
legend.position = 'none',
legend.title = element_text(colour="black", size=12,
face="bold"),
legend.text = element_text(colour="black", size=12,
face="bold"))
return(p)
}
## plot the expression and accessibility of genes within each cluster
#facet by atac cluster
plot_atac_trends <- function(sc.rna.sc.atac.joint.long.sub){
p <- ggplot(sc.rna.sc.atac.joint.long.sub, aes(x= time,y=normExpr)) +
geom_path(aes(color = GeneID),alpha = 0.8, size = 0.8)+
theme_bw(base_size = 14) +
theme(legend.position = "right") +
ylab('normExpr') + xlab('Time') +
theme(axis.text.x = element_text(angle = 0, hjust = 1, size = 18, face="bold", colour = "black")) +
theme(axis.text.y = element_text(angle = 0, hjust = 1, size = 18, face="bold", colour = "black")) +
theme(strip.background = element_rect(colour="black", fill="white",size=0.5, linetype="solid")) +
theme(strip.text = element_text(size = 18, face="bold", angle = 0)) +
coord_cartesian(xlim = c(0,6.5)) +
facet_grid(cluster.ATAC ~ data, scales = 'free', space = 'free') +
theme(
plot.title = element_text(size=20, face = "bold.italic", color = 'red'),
axis.title.x = element_text(size=18, face="bold", hjust = 1),
axis.title.y = element_text(size=18, face="bold")
) +
theme(#legend.position = c(0.15, 0.85),
legend.position = 'none',
legend.title = element_text(colour="black", size=12,
face="bold"),
legend.text = element_text(colour="black", size=12,
face="bold"))
return(p)
}
# expression and atac profile of one gene at a time
plot_trends <- function(my.GeneID, sc.rna.spline.fits,sc.atac.spline.fits ){
## 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(., center = F, scale = F)) %>%
as.data.frame()
sc.atac.dtw.wide <- sc.atac.spline.fits %>%
pivot_wider(names_from = 'GeneID', values_from = 'y') %>%
mutate_at(vars(matches("TGME")), ~scale(., center = F, scale = F)) %>%
as.data.frame()
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')
my.rna <- sc.rna.mu.scale %>% dplyr::filter(GeneID == my.GeneID)
my.atac <- sc.atac.mu.scale %>% dplyr::filter(GeneID == my.GeneID)
p1 <- ggplot(my.rna , aes(x= x,y=expr)) +
geom_line(color = 'blue',alpha = 0.8, size = 0.8)+
theme_bw(base_size = 14) +
theme(legend.position = "right") +
ylab('normExpr') + xlab('Time') +
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")) +
ggtitle(paste('rna', my.GeneID)) +
theme(strip.text = element_text(size = 14, face="bold", angle = 0)) +
theme(
plot.title = element_text(size=14, face = "bold.italic", color = 'black'),
axis.title.x = element_text(size=14, face="bold", hjust = 1),
axis.title.y = element_text(size=14, face="bold")
)
p2 <- ggplot(my.atac , aes(x= x,y=expr)) +
geom_line(color = 'red',alpha = 0.8, size = 0.8)+
theme_bw(base_size = 14) +
theme(legend.position = "right") +
ylab('normExpr') + xlab('Time') +
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")) +
ggtitle(paste('atac', my.GeneID)) +
theme(strip.text = element_text(size = 14, face="bold", angle = 0)) +
theme(
plot.title = element_text(size=14, face = "bold.italic", color = 'black'),
axis.title.x = element_text(size=14, face="bold", hjust = 1),
axis.title.y = element_text(size=14, face="bold")
)
p <- grid.arrange(p1, p2)
return(p)
}
## this function plots rna and atac profile of genes in a table of interest
plot_rna_atac <- function(sc.rna.sc.atac.joint.long.sub){
p <- ggplot(sc.rna.sc.atac.joint.long.sub, aes(x= time,y=normExpr)) +
geom_path(aes(color = GeneID),alpha = 0.8, size = 0.8)+
theme_bw() +
theme(legend.position = "right") +
ylab('normExpr') + xlab('Time') +
theme(axis.text.x = element_text(angle = 0, size = 20, face="bold", colour = "black")) +
theme(axis.text.y = element_text(angle = 0, size = 20, face="bold", colour = "black")) +
theme(strip.background = element_rect(colour="black", fill="white",
size=0.5, linetype="solid")) +
theme(strip.text = element_text(size = 20, face="bold", angle = 0)) +
coord_cartesian(xlim = c(0,6.5)) +
facet_grid(. ~ data, scales = 'free', space = 'free') +
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 = c(0.15, 0.85),
legend.position = 'none',
legend.title = element_text(colour="black", size=12,
face="bold"),
legend.text = element_text(colour="black", size=12,
face="bold"))
return(p)
}
## this function extracts the expression and accessibility profile of genes of interest
## need to give rna and atac splines and genes of interest as input
## set the scale T/F
get_rna_atac_profile <- function(rna.splines, atac.splines, genes.tab, scale = T) {
sc.rna.dtw.wide <- rna.splines %>%
pivot_wider(names_from = 'GeneID', values_from = 'y') %>%
mutate_at(vars(matches("TGME")), ~scale(., center = T, scale = scale)) %>%
as.data.frame()
sc.atac.dtw.wide <- atac.splines %>%
pivot_wider(names_from = 'GeneID', values_from = 'y') %>%
mutate_at(vars(matches("TGME")), ~scale(., center = T, scale = scale)) %>%
as.data.frame()
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')
## table of genes to plot their expression
tab.genes <- data.frame(TGME49 = gsub("_", "-", genes.tab$gene_name),
Name = HC.peaks$ProductDescription)
sc.rna.long <- inner_join(sc.rna.mu.scale, tab.genes, by = c('GeneID' = 'TGME49'))
sc.rna.long <- sc.rna.long %>%
transmute(time = x, GeneID = GeneID, normExpr = expr, Name = Name) %>% distinct()
sc.atac.long <- inner_join(sc.atac.mu.scale, tab.genes, by = c('GeneID' = 'TGME49'))
sc.atac.long <- sc.atac.long %>%
transmute(time = x, GeneID = GeneID, normExpr = expr, Name = Name) %>% distinct()
sc.rna.sc.atac.joint <- inner_join(sc.rna.long, sc.atac.long,
by = c("time", "GeneID", "Name"))
colnames(sc.rna.sc.atac.joint) <- c("time", "GeneID", "scRNA", "Name", "scATAC")
sc.rna.sc.atac.joint.long <- sc.rna.sc.atac.joint %>%
pivot_longer(-c('time', "GeneID", "Name"),
names_to = 'data', values_to = 'normExpr')
return(sc.rna.sc.atac.joint.long)
}