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functions.R
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pcScree <- function (object, pc.genes = NULL, num.pcs = 15, cumulative = FALSE)
{
data.use = [email protected]
if (is.null(pc.genes)){
pc.genes = [email protected]
} else{
pc.genes = unique(pc.genes[pc.genes %in% rownames(data.use)])
}
pc.genes.var = apply(data.use[pc.genes, ], 1, var)
pc.data = data.use[pc.genes[pc.genes.var > 0], ]
pca.obj = prcomp(pc.data)
pve = 100*pca.obj$sdev[1:num.pcs]^2/sum(pca.obj$sdev^2)
if (cumulative){
plot(cumsum(pve), type="o", ylab="Cumulative PVE", xlab="Principal Component", col="red")
} else {
plot(pve,type = "s", ylab = "PVE",
xlab="Principal Component", col="blue")
}
}
RegressOut<-function(object,latent.vars,genes.regress=NULL,do.scale=TRUE,do.spline=FALSE,
spline.span=0.75,do.plot=FALSE,nCol=2,num.genes=10,...)
{
library(pbapply)
set.ifnull=function(x,y) {
if(is.null(x)) x=y
return(x)}
genes.regress=set.ifnull(genes.regress,rownames([email protected]))
genes.regress=ainb(genes.regress,rownames([email protected]))
latent.data=fetch.data(object,latent.vars)
exp.data=t(object@data[genes.regress,])
regression.mat=cbind(latent.data,exp.data)
new.data=t(pbsapply(genes.regress, function(x) {
regression.mat.2=latent.data
regression.mat.2[,"GENE"] = regression.mat[,x];
fmla=as.formula(paste("GENE ", " ~ ", paste(latent.vars,collapse="+"),sep=""));
return(lm(fmla,data = regression.mat.2)$residuals)
}))
if(do.spline==TRUE){
new.data=t(pbsapply(genes.regress, function(x) {
regression.mat.2=latent.data
regression.mat.2[,"GENE"] = regression.mat[,x];
fmla=as.formula(paste("GENE ", " ~ ", paste(latent.vars,collapse="+"),sep=""));
return(loess(fmla,span = spline.span, data = regression.mat.2)$residuals)
}))
}
if (do.plot==TRUE){
PCs=unique(as.numeric(unlist(strsplit(gsub("[^0-9]", "", unlist(latent.vars)), ""))))
genes.plot=pcTopGenes(object,PCs,num.genes)
nCol=nCol
par(mfrow=c(nCol,length(genes.plot)/nCol))
for(gene in genes.plot){
fm=as.formula(paste("`",gene,"`","~", paste(latent.vars,collapse="+"),sep = ""))
plot(regression.mat[,latent.vars[1]],regression.mat[,gene],xlab = latent.vars[1],ylab=as.character(gene))
if(do.spline==TRUE){
loess(fm,span = spline.span, data = regression.mat)
}else{abline(lm(fm,data = regression.mat),col="red",lwd=2)}
}
par(mfrow=c(1,1))
}
if (do.scale==TRUE) {
new.data=t(scale(t(new.data)))
}
new.data[is.na(new.data)]=0
[email protected]=new.data
return(object)
}
feature.plot.pseudo <- function (object, features.plot, dim.1 = 1, dim.2 = 2, cells.use = NULL,
pt.size = 1, cols.use = heat.colors(10), pch.use = 16, reduction.use = "tsne",
use.imputed = FALSE, nCol = NULL,name.x=NULL,name.y=NULL,yaxt=NULL,plot.legend=FALSE,...)
{
set.ifnull=function(x,y) {
if(is.null(x)) x=y
return(x)
}
translate.dim.code <- function (reduction.use)
{
return.code = "PC"
if (reduction.use == "ica")
return.code = "IC"
if (reduction.use == "tsne")
return.code = "tSNE_"
if (reduction.use == "mds")
return.code = "MDS"
return(return.code)
}
cells.use = set.ifnull(cells.use, colnames(object@data))
dim.code = "PC"
if (is.null(nCol)) {
nCol = 2
if (length(features.plot) > 6)
nCol = 3
if (length(features.plot) > 9)
nCol = 4
}
num.row = floor(length(features.plot)/nCol - 1e-05) + 1
par(mfrow = c(num.row, nCol))
dim.code = translate.dim.code(reduction.use)
dim.codes = paste(dim.code, c(dim.1, dim.2), sep = "")
data.plot = fetch.data(object, dim.codes)
x1 = paste(dim.code, dim.1, sep = "")
x2 = paste(dim.code, dim.2, sep = "")
data.plot$x = data.plot[, x1]
data.plot$y = data.plot[, x2]
data.plot$pt.size = pt.size
data.use = data.frame(t(fetch.data(object, features.plot,
cells.use = cells.use, use.imputed = use.imputed)))
for (i in features.plot) {
data.gene = na.omit(data.frame(data.use[i, ]))
data.cut = as.numeric(as.factor(cut(as.numeric(data.gene),
breaks = length(cols.use))))
data.col = rev(cols.use)[data.cut]
name.x=set.ifnull(name.x,"tSNE_1")
name.y=set.ifnull(name.y,"tSNE_2")
plot(data.plot$x, data.plot$y, col = data.col, cex = pt.size,
pch = pch.use, main = i, xlab = name.x, ylab = name.y,yaxt=yaxt,...)
if(plot.legend) legend("topright",legend = c("High",rep("",8),"Low"),border = F,fill = heat.colors(10),y.intersp = 0.2,bty="n",cex = 0.8)
}
rp()
}
jackStrawPlot.new<-function (object, PCs = 1:5, nCol = 3, score.thresh = 1e-05,
plot.x.lim = 0.1, plot.y.lim = 0.3)
{
pAll = [email protected]
pAll <- pAll[, PCs, drop = F]
pAll$Contig <- rownames(pAll)
pAll.l <- melt(pAll, id.vars = "Contig")
colnames(pAll.l) <- c("Contig", "PC", "Value")
qq.df <- NULL
score.df <- NULL
for (i in PCs) {
q <- qqplot(pAll[, i], runif(1000), plot.it = FALSE)
pc.score = prop.test(c(length(which(pAll[, i] <= score.thresh)),
floor(nrow(pAll) * score.thresh)), c(nrow(pAll),
nrow(pAll)))$p.val
if (length(which(pAll[, i] <= score.thresh)) == 0)
pc.score = 1
if (is.null(score.df))
score.df <- data.frame(PC = paste("PC", i, sep = ""),
Score = pc.score)
else score.df <- rbind(score.df, data.frame(PC = paste("PC",
i, sep = ""), Score = pc.score))
if (is.null(qq.df))
qq.df <- data.frame(x = q$x, y = q$y, PC = paste("PC",
i, sep = ""))
else qq.df <- rbind(qq.df, data.frame(x = q$x, y = q$y,
PC = paste("PC", i, sep = "")))
}
pAll.l$PC.Score <- paste(score.df$PC, sprintf("%1.3g", score.df$Score))
gp <- ggplot(pAll.l, aes(sample = Value)) + stat_qq(distribution = qunif) +
facet_wrap("PC.Score", ncol = nCol) + labs(x = "Theoretical [runif(1000)]",
y = "Empirical") + xlim(0, plot.y.lim) + ylim(0, plot.x.lim) +
coord_flip() + geom_abline(intercept = 0, slope = 1,
linetype = "dashed", na.rm = T) + theme_bw()
return(gp)
}
tsne.pseudo <- function (object, do.label = FALSE, do.col.ret=FALSE,label.pt.size = 1, label.cex.text = 1,
label.cols.use = NULL, name.x=NULL,name.y=NULL,font.use=NULL, ...)
{
set.ifnull <- function(x,y) {
if(is.null(x)) x=y
return(x)
}
.local <- function (object, do.label = FALSE, do.col.ret=FALSE,label.pt.size = 1,
label.cex.text = 1, label.cols.use = NULL, cells.use = NULL,
...)
{
cells.use = set.ifnull(cells.use, [email protected])
cells.use = ainb(cells.use, [email protected])
name.x = set.ifnull(name.x,"tSNE_1")
name.y = set.ifnull(name.y,"tSNE_2")
if (do.label == TRUE) {
label.cols.use = set.ifnull(label.cols.use, rainbow(length(levels(object@ident[cells.use]))))
set.seed(1)
label.cols.use = sample(label.cols.use)
if (do.col.ret == TRUE){
print(label.cols.use)
}
if (length(unique([email protected][,2]))==1){
plot([email protected][cells.use, 1], [email protected][cells.use,2],col = label.cols.use[as.integer(object@ident[cells.use])],
pch = 16, xlab = name.x, ylab = name.y, cex = label.pt.size,yaxt='n',cex.lab=1.2,font.lab=2,...)
k.centers = t(sapply(levels(object@ident), function(x) apply([email protected][which.cells(object,
x), ], 2, median)))
points(k.centers[, 1], k.centers[, 2],cex = 1.3,
col = "white", pch = 16)
text(k.centers[, 1], k.centers[, 2],levels(object@ident),
cex = label.cex.text,font=font.use)
} else {
plot([email protected][cells.use, 1], [email protected][cells.use,
2], col = label.cols.use[as.integer(object@ident[cells.use])],
pch = 16, xlab = name.x, ylab = name.y, cex = label.pt.size,cex.lab=1.2, font.lab=2,...)
k.centers = t(sapply(levels(object@ident), function(x) apply([email protected][which.cells(object,
x), ], 2, median)))
points(k.centers[, 1], k.centers[, 2], cex = 1.3,
col = "white", pch = 16)
text(k.centers[, 1], k.centers[, 2], levels(object@ident),
cex = label.cex.text,font=font.use)
}
}
else {
if (length(unique([email protected][,2]))==1){
plot([email protected][cells.use, 1], [email protected][cells.use,2],col = label.cols.use[as.integer(object@ident[cells.use])],
pch = 16, xlab = name.x, ylab = name.y, cex = label.pt.size,yaxt='n',cex.lab=1.2,font.lab=2,...)
} else {
plot([email protected][cells.use, 1], [email protected][cells.use,
2], col = label.cols.use[as.integer(object@ident[cells.use])],
pch = 16, xlab = name.x, ylab = name.y, cex = label.pt.size,cex.lab=1.2, font.lab=2,...)
}
}
}
.local(object, do.label, do.col.ret, label.pt.size, label.cex.text, label.cols.use,
...)
}
genes.plot.pseudo <- function (object,genes.use, cell.ids = NULL, pseudo="pseudo.time",pch.use = 16, cex.use = 1.5, do.induct=TRUE,
do.scale=FALSE,use.imputed = FALSE, method = "logit", spline.span = 0.75,name.x=NULL,do.line=FALSE,col.use=NULL, bin = 25, nState = 2,
do.smooth=FALSE,knn=10,inset=c(0,0),ylim=NULL,conf.int=FALSE,do.label=F,do.ret.mat=F,lwd=1,do.ret.pt=F,use.loess=F,...)
{
set.ifnull=function(x,y) {
if(is.null(x)) x=y
return(x)
}
nn.count.add <- function(mat, k, dmat) {
dmat[dmat == 0] <- Inf
knn <- t(apply(dmat, 1, function(x) rank(x) <= k))
mat <- mat + mat %*% t(knn)
return(mat)
}
cell.ids = set.ifnull(cell.ids, [email protected])
ident.use = as.factor(object@ident[cell.ids])
xlab=paste(name.x,"Pseudotime")
ret.mat=c()
turn.mat=c()
turn.pt=list(on=c(),off=c())
if(do.induct & method == "hmm") {
library(RHmm)
object.pseudo = pseudoBin(object, bin.size = bin)}
if (do.smooth){
pt.ncc=fetch.data(object,pseudo)
dmat = as.matrix(dist(pt.ncc))
count.mat = [email protected]
[email protected] = nn.count.add(count.mat, knn, dmat)
data.use = data.frame(t(fetch.data(object, c(pseudo, genes.use),
cells.use = cell.ids, use.imputed = use.imputed,use.scaled = TRUE)))
}
if(do.scale){
data.use = data.frame(t(fetch.data(object, c(pseudo, genes.use),
cells.use = cell.ids, use.imputed = use.imputed,use.scaled = TRUE)))
}else{
data.use = data.frame(t(fetch.data(object, c(pseudo, genes.use),
cells.use = cell.ids, use.imputed = use.imputed,use.scaled = FALSE)))
}
pseudo.order=order(data.use[pseudo,cell.ids])
pseudo.data=unlist(data.use[pseudo,cell.ids][pseudo.order])
[email protected][email protected][,pseudo.order]
ylim=set.ifnull(ylim,range(data.use[genes.use,cell.ids]))
plot(0,0,xlim = range(pseudo.data),ylim = ylim,type = "l",xlab=xlab,ylab="log(FPKM)",
cex = cex.use, pch = pch.use, font.lab=2,cex.lab=1.2,bty="l",...)
col.use=set.ifnull(col.use,rainbow(length(genes.use)))
if(do.line){
for (i in 1:length(genes.use)){
g2 = as.numeric(data.use[genes.use[i], cell.ids][pseudo.order])
points(pseudo.data, g2,col=col.use[i],pch=20)
lines(pseudo.data, g2,col=col.use[i],lwd=lwd)
}
}else{
for (i in 1:length(genes.use)){
g2 = as.numeric(data.use[genes.use[i], cell.ids][pseudo.order])
loess.fit = loess(g2 ~ pseudo.data, span = spline.span)
lines(pseudo.data, loess.fit$fitted,type="l",col=col.use[i],lwd=lwd)
ret.mat=rbind(ret.mat,loess.fit$fitted)
if(conf.int){
prid = predict(loess.fit,se=T)
lines(pseudo.data[order(pseudo.data)], (prid$fit - qt(0.975,prid$df) * prid$se)[order(pseudo.data)],col=col.use[i],lty=2)
lines(pseudo.data[order(pseudo.data)], (prid$fit + qt(0.975,prid$df) * prid$se)[order(pseudo.data)],col=col.use[i],lty=2)
}
if(do.induct & method == "logit"){
if(do.smooth){
gene.norm=apply(matrix([email protected][genes.use[i],cell.ids]),2, FUN = function(X) (X - min(X))/diff(range(X)))
}
if(use.loess){
logit.use <- loess.fit$fitted
gene.norm=(logit.use - min(logit.use))/diff(range(logit.use))}
if(!use.loess & !do.smooth){
gene.norm=apply(object@data[genes.use[i],cell.ids],1, FUN = function(X) (X - min(X))/diff(range(X)))
}
gene.norm[which(gene.norm>0.5)]=1
gene.norm[which(gene.norm<0.5)]=0
model <- glm(gene.norm[pseudo.order]~pseudo.data,family=binomial(link='logit'))
model.fit=abs(fitted(model)-0.5)
turn=min(model.fit)
turn.point=pseudo.data[which(model.fit==turn)]
turn.mat=c(turn.mat,turn.point)
abline(v=turn.point,lwd=2,col=col.use[i],lty="longdash")
}
if(method == "hmm"){
if(use.loess){hmm.use <- loess.fit$fitted} else {hmm.use <- unlist(object@data[genes.use[i],])}
if(max(hmm.use) <= 2){
turn=1
turn.point=pseudo.data[turn]
names(turn.point)=genes.use[i]
abline(v=turn.point,lwd=2,col=col.use[i],lty="longdash")
turn.pt$off=c(turn.pt$off,turn.point)
} else {
hmm.fit=HMMFit(hmm.use,nStates = nState)
hmm.vit=viterbi(hmm.fit,hmm.use)
if(length(unique(hmm.vit$states)) == 1){
turn=1
turn.point=pseudo.data[turn]
names(turn.point)=genes.use[i]
abline(v=turn.point,lwd=2,col=col.use[i],lty="longdash")
if(loess.fit$fitted[turn] >=1){
turn.pt$on=c(turn.pt$on,turn.point)
} else{
turn.pt$off=c(turn.pt$off,turn.point)
}
} else {
counter = 1
turn = NULL
for (j in 1:(length(hmm.use)-1)){
if(hmm.vit$states[j] - hmm.vit$states[j+1] != 0){
turn.point=pseudo.data[j]
names(turn.point)=genes.use[i]
abline(v=turn.point,lwd=2,col=col.use[i],lty="longdash")
if(hmm.use[j]-hmm.use[j+1]>0){
turn.pt$off = c(turn.pt$off, turn.point)
} else {turn.pt$on=c(turn.pt$on, turn.point)}
}
}
}
}
}
}
}
if(do.label){
legend("topright",legend = genes.use,
lty=1, col=col.use, cex=.75,xpd=T,inset = inset)
}
if(do.ret.mat){
rownames(ret.mat)=genes.use
colnames(ret.mat)=cell.ids[pseudo.order]
return(ret.mat)}
if(do.ret.pt){
if(method=="hmm") {return(turn.pt)} else {
names(turn.mat)=genes.use
return(turn.mat)
}
}
}
genePlot.pseudo <- function (object,gene,pseudo="pseudo.time", cell.ids = NULL, col.use = NULL,
pch.use = 16, cex.use = 1.5, use.imputed = FALSE, do.ident = FALSE,
do.spline = FALSE,do.line=FALSE,spline.span = 0.75,name.x=NULL, do.logit=FALSE,
do.smooth=FALSE,knn=10,use.scale=FALSE,do.return=FALSE,conf.int=FALSE,...)
{
set.ifnull=function(x,y) {
if(is.null(x)) x=y
return(x)
}
nn.count.add <- function(mat, k, dmat) {
dmat[dmat == 0] <- Inf
knn <- t(apply(dmat, 1, function(x) rank(x) <= k))
mat <- mat + mat %*% t(knn)
return(mat)
}
if (do.smooth){
pt.ncc=fetch.data(object,pseudo)
dmat = as.matrix(dist(pt.ncc))
count.mat = [email protected]
[email protected] = nn.count.add(count.mat, knn, dmat)
data.use = data.frame(t(fetch.data(object, c(pseudo, gene),
cells.use = cell.ids, use.imputed = use.imputed,use.scaled = TRUE)))
}else{
data.use = data.frame(t(fetch.data(object, c(pseudo, gene),
cells.use = cell.ids, use.imputed = use.imputed,use.scaled = use.scale)))
}
cell.ids = set.ifnull(cell.ids, [email protected])
name.x = set.ifnull(name.x, pseudo)
pseudo.order=order(data.use[pseudo,cell.ids])
g1 = as.numeric(data.use[pseudo, cell.ids])
g2 = as.numeric(data.use[gene, cell.ids])
ident.use = as.factor(object@ident[cell.ids])
pseudo.data=as.matrix(fetch.data(object,pseudo))
if(do.logit){
if(do.smooth){
gene.norm=apply(matrix([email protected][gene,colnames([email protected])]),2, FUN = function(X) (X - min(X))/diff(range(X)))
}
if(do.spline){
loess.fit = loess(g2 ~ g1, span = spline.span)
gene.norm = (loess.fit$fitted - min(loess.fit$fitted))/diff(range(loess.fit$fitted))
}
if(!do.smooth & !do.spline){
gene.norm=apply(object@data[gene,colnames(object@data)],1, FUN = function(X) (X - min(X))/diff(range(X)))
}
gene.norm[which(gene.norm>0.5)]=1
gene.norm[which(gene.norm<0.5)]=0
model <- glm(gene.norm[pseudo.order]~g1,family=binomial(link='logit'))
model.fit=abs(fitted(model)-0.5)
turn=min(model.fit)
turn.point=pseudo.data[which(model.fit==turn),1]
}
if (length(col.use) > 1) {
col.use = col.use[as.numeric(ident.use)]
}
else {
col.use = set.ifnull(col.use, as.numeric(ident.use))
}
g1 = as.numeric(data.use[pseudo, cell.ids])
g2 = unlist(object@data[gene, cell.ids])
gene.cor = round(cor(g1, g2), 2)
plot(g1, g2, xlab = name.x, ylab = gene, col = col.use, cex = cex.use,
main = "", pch = pch.use, font.lab=2,cex.lab=1.2, ...)
if (do.logit){
abline(v=turn.point,lwd=4,col="purple",lty="longdash")
}
if (do.spline) {
loess.fit = loess(g2 ~ g1, span = spline.span)
lines(g1[order(pseudo.data)], loess.fit$fitted[order(pseudo.data)],col = "black",lwd=4)
if(conf.int){
prid = predict(loess.fit,se=T)
lines(g1[order(g1)], (prid$fit - qt(0.975,prid$df) * prid$se)[order(g1)],col = "black",lwd=1.2,lty=2)
lines(g1[order(g1)], (prid$fit + qt(0.975,prid$df) * prid$se)[order(g1)],col = "black",lwd=1.2,lty=2)
}
}
if(do.line){
lines(g1[order(pseudo.data)], g2[order(pseudo.data)],col = "purple",lwd=3)
}
if (do.ident) {
return(identify(g1, g2, labels = cell.ids))
}
if (do.return){
if (do.spline){
return(data.frame(g1,loess.fit$fitted))}else{
return(data.frame(g1,g2))
}
}
}
vlnPlot.FPKM <- function (object, features.plot, name=NULL, nCol = NULL, ylab.max = 12,
do.ret = FALSE, do.sort = FALSE, size.x.use = 16, size.y.use = 20,
size.title.use = 20, use.imputed = FALSE, adjust.use = 1, do.scale=FALSE,
size.use = 1, cols.use = NULL, group.by = NULL,name.y=NULL,ratio.plot=1,name.x=NULL,x.axis=TRUE)
{
set.ifnull=function(x,y) {
if(is.null(x)) x=y
return(x)
}
plt.Vln.FPKM<-function (gene, data, cell.ident, ylab.max = 12, do.ret = FALSE,
do.sort = FALSE, size.x.use = 16, size.y.use = 16, size.title.use = 20,
adjust.use = 1, size.use = 1, cols.use = NULL,x.axis=TRUE)
{
data$gene = as.character(rownames(data))
data.use = data.frame(data[gene, ])
if (length(gene) == 1) {
data.melt = data.frame(rep(gene, length(cell.ident)))
colnames(data.melt)[1] = "gene"
data.melt$value = as.numeric(data[1, 1:length(cell.ident)])
data.melt$id = names(data)[1:length(cell.ident)]
}
if (length(gene) > 1)
data.melt = melt(data.use, id = "gene")
data.melt$ident = cell.ident
noise <- rnorm(length(data.melt$value))/1e+05
data.melt$value = as.numeric(as.character(data.melt$value)) +
noise
if (do.sort) {
data.melt$ident = factor(data.melt$ident, levels = names(rev(sort(tapply(data.melt$value,
data.melt$ident, mean)))))
}
name.y=set.ifnull(name.y,"Expression level (log FPKM)\n")
p = ggplot(data.melt, aes(factor(ident), value))
p2 = p + geom_violin(scale = "width", adjust = adjust.use,
trim = TRUE, aes(fill = factor(ident))) + ylab(name.y)
if (!is.null(cols.use)) {
p2 = p2 + scale_fill_manual(values = cols.use)
}
name=set.ifnull(name,gene)
name.x=set.ifnull(name.x,"\nCell Type")
p3 = p2 + theme(legend.position = "top") + guides(fill = guide_legend(title = NULL))
#geom_jitter(height = 0, size = size.use) + xlab(name.x)
if (!x.axis) {
p4 = p3 + theme(axis.title.x = element_text(face = "bold",
colour = "#990000", size = size.x.use), axis.text.x = element_text(angle = 90,
vjust = 0.5, size = 18)) + theme_bw() + theme(
plot.background = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()) + scale_x_continuous(labels = "")
} else {
p4 = p3 + theme(axis.title.x = element_text(face = "bold",
colour = "#990000", size = size.x.use), axis.text.x = element_text(angle = 90,
vjust = 0.5, size = 18)) + theme_bw() + theme(
plot.background = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank())}
gene=name
p5 = (p4 + theme(axis.title.y = element_text(face = "bold",
colour = "#990000", size = size.y.use,vjust=1.5), axis.text.y = element_text(angle = 90,
vjust = 0.5, size = 18)) + ggtitle(gene) + theme(plot.title = element_text(size = size.title.use,
face = "bold")))
p5 = p5 + theme(axis.text=element_text(size=18,vjust = 2),axis.title.x=element_text(size=18,face = "bold"))+theme(legend.position = "none") +
coord_fixed(ratio=ratio.plot)
if (do.ret == TRUE) {
return(p5)
}
else {
print(p5)
}
}
pltVln<-function (gene, data, cell.ident, ylab.max = 12, do.ret = FALSE,
do.sort = FALSE, size.x.use = 16, size.y.use = 16, size.title.use = 20,
adjust.use = 1, size.use = 1, cols.use = NULL)
{
data$gene = as.character(rownames(data))
data.use = data.frame(data[gene, ])
if (length(gene) == 1) {
data.melt = data.frame(rep(gene, length(cell.ident)))
colnames(data.melt)[1] = "gene"
data.melt$value = as.numeric(data[1, 1:length(cell.ident)])
data.melt$id = names(data)[1:length(cell.ident)]
}
if (length(gene) > 1)
data.melt = melt(data.use, id = "gene")
data.melt$ident = cell.ident
noise <- rnorm(length(data.melt$value))/1e+05
data.melt$value = as.numeric(as.character(data.melt$value)) +
noise
if (do.sort) {
data.melt$ident = factor(data.melt$ident, levels = names(rev(sort(tapply(data.melt$value,
data.melt$ident, mean)))))
}
p = ggplot(data.melt, aes(factor(ident), value))
p2 = p + geom_violin(scale = "width", adjust = adjust.use,
trim = F, show.legend = F, aes(fill = factor(ident))) + geom_boxplot(width=0.15)+ ylab("Expression level (logFPKM)")
if (!is.null(cols.use)) {
p2 = p2 + scale_fill_manual(values = cols.use)
}
p3 = p2 + ylim(2000,7000)
#geom_jitter(height = 0, size = size.use) + xlab(NULL)
p4 = p3 + theme(axis.title.x = element_text(face = "bold",
colour = "#990000", size = size.x.use), axis.text.x = element_text(angle = 90,
vjust = 0.5, size = 5)) + theme_bw() + theme(
plot.background = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank())
p5 = (p4 + theme(axis.title.y = element_text(face = "bold",
colour = "#990000", size = size.y.use), axis.text.y = element_text(angle = 90,
vjust = 0.5, size = 12)) + ggtitle(gene) + theme(plot.title = element_text(size = size.title.use,
face = "bold")))
if (do.ret == TRUE) {
return(p5)
}
else {
print(p5)
}
}
if (is.null(nCol)) {
nCol = 2
if (length(features.plot) > 6)
nCol = 3
if (length(features.plot) > 9)
nCol = 4
}
if(do.scale){
data.use = data.frame(t(fetch.data(object, features.plot,
use.imputed = use.imputed,use.scaled = T)))
}else{
data.use = data.frame(t(fetch.data(object, features.plot,
use.imputed = use.imputed)))
}
ident.use = object@ident
if (!is.null(group.by))
ident.use = as.factor(fetch.data(object, group.by)[,
1])
pList = lapply(features.plot, function(x) pltVln(x, data.use[x,
], ident.use, ylab.max, TRUE, do.sort, size.x.use, size.y.use,
size.title.use, adjust.use, size.use, cols.use))
if (do.ret) {
return(pList)
}
else {
multiplotList(pList, cols = nCol)
rp()
}
}
boxPlot.FPKM <- function (object, features.plot, name=NULL, nCol = NULL, ylab.max = 12,
do.ret = FALSE, do.sort = FALSE, size.x.use = 20, size.y.use = 20,
size.title.use = 20, use.imputed = FALSE, adjust.use = 1,
size.use = 1, cols.use = NULL, group.by = NULL,name.y=NULL,ratio.plot=1,name.x=NULL,do.jitter=FALSE)
{
set.ifnull=function(x,y) {
if(is.null(x)) x=y
return(x)
}
plt.Box.FPKM<-function (gene, data, cell.ident, ylab.max = 12, do.ret = FALSE,
do.sort = FALSE, size.x.use = 16, size.y.use = 16, size.title.use = 20,
adjust.use = 1, size.use = 1, cols.use = NULL)
{
data$gene = as.character(rownames(data))
data.use = data.frame(data[gene, ])
if (length(gene) == 1) {
data.melt = data.frame(rep(gene, length(cell.ident)))
colnames(data.melt)[1] = "gene"
data.melt$value = as.numeric(data[1, 1:length(cell.ident)])
data.melt$id = names(data)[1:length(cell.ident)]
}
if (length(gene) > 1)
data.melt = melt(data.use, id = "gene")
data.melt$ident = cell.ident
noise <- rnorm(length(data.melt$value))/1e+05
data.melt$value = as.numeric(as.character(data.melt$value)) +
noise
if (do.sort) {
data.melt$ident = factor(data.melt$ident, levels = names(rev(sort(tapply(data.melt$value,
data.melt$ident, mean)))))
}
name.y=set.ifnull(name.y,"Expression level (log FPKM)\n")
p = ggplot(data.melt, aes(factor(ident), value))
p2 = p + geom_boxplot(aes(fill = factor(ident))) + ylab(name.y) + xlab(name.x)
if (!is.null(cols.use)) {
p2 = p2 + scale_fill_manual(values = cols.use)
}
name=set.ifnull(name,gene)
name.x=set.ifnull(name.x,"\nCell Type")
if(do.jitter){
p3 = p2 + theme(legend.position = "top") + guides(fill = guide_legend(title = NULL)) + xlab(name.x) + geom_jitter(height = 0, size = size.use)
} else {
p3 = p2 + theme(legend.position = "top") + guides(fill = guide_legend(title = NULL)) + xlab(name.x)
}
p4 = p3 + theme(axis.title.x = element_text(face = "bold",
colour = "#990000", size = size.x.use), axis.text.x = element_text(angle = 90,
vjust = 0.5, size = 18)) + theme_bw() + theme(
plot.background = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank())
if(name==""){gene=NULL}
p5 = (p4 + theme(axis.title.y = element_text(face = "bold",
colour = "#990000", size = size.y.use,vjust=1.5), axis.text.y = element_text(angle = 90,
vjust = 0.5, size = 18)) + ggtitle(gene) + theme(plot.title = element_text(size = size.title.use,
face = "bold")))
p5 = p5 + theme(axis.text=element_text(size=18,vjust = 2),axis.title.x=element_text(size=18,face = "bold"))+theme(legend.position = "none") +
coord_fixed(ratio=ratio.plot)
if (do.ret == TRUE) {
return(p5)
}
else {
print(p5)
}
}
if (is.null(nCol)) {
nCol = 2
if (length(features.plot) > 6)
nCol = 3
if (length(features.plot) > 9)
nCol = 4
}
data.use = data.frame(t(fetch.data(object, features.plot,
use.imputed = use.imputed)))
ident.use = object@ident
if (!is.null(group.by))
ident.use = as.factor(fetch.data(object, group.by)[,
1])
pList = lapply(features.plot, function(x) plt.Box.FPKM(x, data.use[x,
], ident.use, ylab.max, TRUE, do.sort, size.x.use, size.y.use,
size.title.use, adjust.use, size.use, cols.use))
if (do.ret) {
return(pList)
}
else {
multiplotList(pList, cols = nCol)
rp()
}
}
pseudo.gene.cluster <- function (object, cell.ids=NULL, pseudo="pseudo.time",genes.use,do.smooth=F,do.spline=F,
spline.span = 0.75, knn=10,...)
{
set.ifnull=function(x,y) {
if(is.null(x)) x=y
return(x)
}
nn.count.add <- function(mat, k, dmat) {
dmat[dmat == 0] <- Inf
knn <- t(apply(dmat, 1, function(x) rank(x) <= k))
mat <- mat + mat %*% t(knn)
return(mat)
}
if (do.smooth){
pt.ncc=fetch.data(object,pseudo)
dmat = as.matrix(dist(pt.ncc))
count.mat = [email protected]
[email protected] = nn.count.add(count.mat, knn, dmat)
}
pseudo.data=as.matrix(fetch.data(object,pseudo))
pseudo.order=order(pseudo.data)
pseudo.data=pseudo.data[pseudo.order]
cell.ids = set.ifnull(cell.ids, [email protected])
cluster=list(up=NULL,down=NULL)
turn.mat=c()
ret.mat=c()
for (gene in genes.use){
if(do.spline){
loess.fit = loess(unlist(object@data[gene, pseudo.order]) ~ pseudo.data, span = spline.span)
data.use=loess.fit$fitted
} else {
data.use=unlist(object@data[gene,])
}
gene.norm=(data.use - min(data.use))/diff(range(data.use))
gene.norm[which(gene.norm>0.5)]=1
gene.norm[which(gene.norm<0.5)]=0
model <- glm(gene.norm[pseudo.order]~pseudo.data,family=binomial(link='logit'))
model.fit=fitted(model)
turn=which.min(abs(model.fit-0.5))
turn.point=pseudo.data[turn]
attr(gene,"names")=turn.point
if(turn==1 | turn==length(gene.norm)){
if(turn==1){
if(model.fit[turn+1]>model.fit[turn]){cluster[[1]]=append(cluster[[1]],gene)}
if(model.fit[turn+1]<model.fit[turn]){cluster[[2]]=append(cluster[[2]],gene)}
}
if(turn==length(gene.norm)){
if(model.fit[turn]>model.fit[turn-1]){cluster[[1]]=append(cluster[[1]],gene)}
if(model.fit[turn]<model.fit[turn-1]){cluster[[2]]=append(cluster[[2]],gene)}
}
} else {
if (model.fit[turn]>model.fit[turn-1] || model.fit[turn+1]>model.fit[turn]){
cluster[[1]]=append(cluster[[1]],gene)
}else{
cluster[[2]]=append(cluster[[2]],gene)
}
}
}
cluster1=list(up=NULL,down=NULL)
cluster1$up=cluster[[1]][order(as.double(names(cluster[[1]])),decreasing = T)]
cluster1$down=cluster[[2]][order(as.double(names(cluster[[2]])),decreasing = F)]
return(cluster1)
}
scale.pseudo<-function(object,pseudo="pseudo.time",cell.ids=NULL,do.PC=FALSE,do.scale=FALSE)
{
set.ifnull=function(x,y) {
if(is.null(x)) x=y
return(x)
}
if(do.scale){
cell.ids=set.ifnull(cell.ids,colnames(object@data))
pseudo.data=matrix(t(fetch.data(object,pseudo,cells.use = cell.ids)))
pseudo.scale=apply(pseudo.data,2, FUN = function(X) (X - min(X))/diff(range(X)))
[email protected][,"pseudo.time"]=pseudo.scale
}
if(do.PC){
if(do.scale){[email protected][,1]=pseudo.scale}
else{[email protected][,1][email protected][,"pseudo.time"]}
}
return(object)
}