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continuousCCprediction_figures4paper.Rmd
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continuousCCprediction_figures4paper.Rmd
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---
title: "Figures for paper"
date: "2024-05-16"
output: html_document
---
```{r setup, include=FALSE}
# knitr::opts_knit$set(root.dir = "~/Projects/PMO/MeasuringFitnessPerClone/discussion/Bioinformatics/Imaging_Sequencing_Integration_NCIN87/code/data4paper")
knitr::opts_knit$set(root.dir = "C:/Users/80027908/Desktop/CC_project/PMO_MeasuringFitnessperClone-master/3D_Imaging/R/data4paper")
library(xlsx)
library(dyno)
library(gplots)
library(caret)
library(xtable)
library(tidyverse)
library(matlab)
library(RColorBrewer)
library(flexclust)
library(ggplot2)
library(e1071)
library(slingshot)
library(umap)
library(patchwork)
library(grid)
library(gridExtra)
library(dendextend)
library(DiagrammeR)
library(glue)
# devtools::source_url("https://github.com/noemiandor/Utils/master/grpstats.R?raw=TRUE")
OUTLINKED="A06_multiSignals_Linked"
OUTPSEUDOTIME="A08_Pseudotime"
OUTSTATS="A07_LinkedSignals_Stats"
RAWDATA="A01_rawData"
OUTFIGS = "../figs4paper"
xydim = 255
pixelsize_xy = 0.232 # um
z_interval = 0.29 # um
xyz=c("x","y","z")
zslice=35
```
Define local functions and read image coordinates and features used in multiple Figures:
```{r}
plotPNGorTIF<-function(figname){
if(fileparts(figname)$ext!=".png"){
la=tiff::readTIFF(figname)
}else{
la=png::readPNG(figname,native = F)
}
plot(NA, xlim = c(0, ncol(la)/nrow(la)), ylim = c(0, 1), type = "n", xaxt = "n", yaxt = "n", xlab = "", ylab = "")
rasterImage(la, 0, 0, ncol(la)/nrow(la),1)
return(la)
}
# Convert matrix to a data frame with row names, column names, and matrix entry
convert_matrix_to_df <- function(mat, pval) {
# Create a data frame with all combinations of row and column names
grid <- expand.grid(Pathway = rownames(mat), "Cell.Feature" = colnames(mat))
# Add the matrix values as a new column
grid$r <- as.vector(mat)
grid$pval <- as.vector(pval)
return(grid)
}
# Function to truncate strings to a specified length
truncate_string <- function(x, max_length) {
if (nchar(x) > max_length) {
return(paste0(substr(x, 1, max_length), "..."))
} else {
return(x)
}
}
getZLIM<- function(coord__){
tmp = quantile(coord__$z,c(0,1))
space=tmp[2]-tmp[1]
zlim=c(tmp[1]-space/2, tmp[2]+space/2)
return(zlim)
}
readOrganelleCoordinates<-function(signals_per_id, signals, IN){
coord=NULL;
for(cell in signals_per_id$x){
for(s in signals){
x=paste0(s,"_cell_",cell,"_coordinates.csv")
a=read.csv(paste0(IN,filesep,x))
# pixel to um conversion: @TODO
a$z=a$z*z_interval
a$x=a$x*pixelsize_xy
a$y=a$y*pixelsize_xy
# id=strsplit(fileparts(x)$name,"_")[[1]]
a$organelle = a[,ncol(a)]
a$intensity = a$signal
a$signal=s
# id=id[length(id)-1]
a$id=cell
# add missing columns
if(!is.null(coord)){
for(mc in setdiff(colnames(a), colnames(coord))){
coord[,mc]=NA
}
tmp=matrix(NA,nrow(a), ncol(coord))
colnames(tmp) = colnames(coord)
coord=rbind(tmp, coord)
coord[1:nrow(a),colnames(a)] = a
}else{
coord=a
}
# a[,c("y", "x", "z", "organelle", "id","signal")]
}
}
coord$id=as.numeric(coord$id)
return(coord)
}
grpstats <- function(x,g,statscols,q1=0.5){
allOut=list()
o=matrix(NA, length(unique(g)),ncol(x) )
rownames(o)=unique(g); colnames(o)=colnames(x);
for(col in statscols){
for(m in unique(g)){
ii=which(g==m);
v=NA;
if(col=='mean'){
v=apply(x[ii,,drop=F],2,mean,na.rm=T)
}else if(col=='sum'){
v=apply(x[ii,,drop=F],2,sum,na.rm=T)
}else if(col=='var'){
v=apply(x[ii,,drop=F],2,var)
}else if(col=='max'){
v=apply(x[ii,,drop=F],2,max,na.rm=T)
}else if(col=='min'){
v=apply(x[ii,,drop=F],2,min,na.rm=T)
}else if(col=='quantile'){
v=apply(x[ii,,drop=F],2,quantile,q1,na.rm=T)
}else if(col=='median'){
v=apply(x[ii,,drop=F],2,median,na.rm=T)
}else if(col=='numel+'){##Count elements >0
v=apply(x[ii,,drop=F]>0,2,sum,na.rm=T)
}else if(col=='numel_u'){##Count unique elements
v=apply(x[ii,,drop=F],2, function(k) length(unique(k)))
}else if(col=='fraction+'){##Fraction of elements> 0 out of all finite elements
v1=apply(!is.na(x[ii,,drop=F]),2,sum,na.rm=T)
v=apply(x[ii,,drop=F]>0,2,sum,na.rm=T)/v1
}else if(col=='maxcount'){##Most frequent value
v1=plyr::count(x[ii])
v=v1$x[which.max(v1$freq)]
}else{
v=get(col)(x[ii,,drop=F])
}
o[as.character(m),]=v;
}
allOut[[col]]=o;
}
return(allOut)
}
Plot_ConcaveHull <- function(xx, yy, zz, lcolor="black", alpha=0.4, add=T, phi = 40, theta = 40, level=0.5/length(xx),xlim=quantile(xx,c(0,1)),ylim=quantile(yy,c(0,1)),zlim=quantile(zz,c(0,1))) {
library(MASS)
# print("datapoints nucleus:"); print(length(xx))
# print("datapoints mito:"); print(nrow(other))
# Remove outliers
# hQ=0.975; lQ=0.025
hQ=1; lQ=0
iK1=which(xx<=quantile(xx,hQ) & xx>=quantile(xx,lQ))
iK2=which(yy<=quantile(yy,hQ) & yy>=quantile(yy,lQ))
iK3=which(zz<=quantile(zz,hQ) & zz>=quantile(zz,lQ))
iK=intersect(iK1,iK2)
iK=intersect(iK,iK3)
xx=xx[iK]; yy=yy[iK]; zz = zz[iK]
qx=quantile(xx,c(0,1))
qy=quantile(yy,c(0,1))
qz=quantile(zz,c(0,1))
# Contour
n=1+c(qx[2]-qx[1], qy[2]-qy[1], qz[2]-qz[1])
# print("nucleus:"); print(n)
dens2 <- misc3d::kde3d(xx, yy, zz,n=round(n) )
RES=c(x=dens2$x[2]-dens2$x[1],y=dens2$y[2]-dens2$y[1],z=dens2$z[2]-dens2$z[1])
# Embed in larger array to render plots comparable across cells
dens <- array(0,dim=ceil(cbind(xlim,ylim,zlim)[2,]/RES))
# print("all:");
print(dim(dens))
dens[ceil(dens2$x/RES["x"]),ceil(dens2$y/RES["y"]), ceil(dens2$z/RES["z"])]=dens2$d
# print(quantile(dens2$x,c(0,1)))## Plot
plot3D::isosurf3D(x=1:nrow(dens),y=1:ncol(dens),z=1:dim(dens)[3],colvar = dens, col=lcolor, add=add, alpha=alpha,bty="n",phi = phi, theta = theta); #,drawlabels=F,lwd=2
# rgl::plot3d(1:nrow(dens), 1:ncol(dens), 1:dim(dens)[3], pch3d=20, size=2, axes=F, xlab="",ylab="", zlab="",col=lcolor, add=add, alpha=alpha)
}
FoFs=c("FoF1_231005_fluorescent.nucleus", "FoF2_231005_fluorescent.nucleus", "FoF3_231005_fluorescent.nucleus", "FoF4_231005_fluorescent.nucleus")
signals=list(nucleus.p="nucleus.p_Cells_Centers.csv",mito.p="mito.p_Cells_Centers.csv",cytoplasm.p="cytoplasm.p_Cells_Centers.csv")
signals_per_id=list()
for(FoF in FoFs){
OUTLINKED_=paste0(OUTLINKED,filesep,FoF,filesep)
f=list.files(OUTLINKED_,full.names = T, pattern = ".csv")
signals_per_id[[FoF]]=plyr::count(sapply(strsplit(f,"_"), function(x) x[length(x)-1]))
}
thedate=strsplit(as.character(Sys.time())," ")[[1]][1]
FoF=FoFs[3]
OUTLINKED_=paste0(OUTLINKED,filesep,FoF,filesep)
load(paste0(OUTPSEUDOTIME, filesep,"pseudotime_2024-05-15.RObj")) ## loads unsupervised pseudotime *model* list
load(paste0(OUTPSEUDOTIME, filesep,"cellCyclePredictionFromImgFeatures_svm_2024-05-15.RObj")) ## load *svm* details
fucci_raw=read.table( paste0(OUTPSEUDOTIME,filesep,"Fucci_stats.txt"),sep="\t", header = T)
imgStats = read.table(paste0(OUTPSEUDOTIME,filesep,"LabelFree_stats.txt"),sep="\t",header = T)
```
Figure 1: FUCCI to characterize discrete and continuous cell cycle progression in NCI-N87. Setup first:
```{r}
fuccicol=fliplr(rainbow(max(fucci_raw$cellCycle)*1.2)[1:max(fucci_raw$cellCycle)])
names(fuccicol) = c("G1", "G1/S","S","G2/M")
fucci_=fucci_raw[fucci_raw$FoF==FoF,]
coord_=readOrganelleCoordinates(signals_per_id[[FoF]], "nucleus.p", OUTLINKED_)
coord_[,c("x","y")] = coord_[,c("x","y")]/pixelsize_xy
coord_ = coord_[coord_$z == zslice*z_interval, ]
coord_$x=-coord_$x
coord_$x=-max(coord_$x)+coord_$x-min(coord_$x)
coord_1 <- coord_
TIF=list.files(paste0(RAWDATA,filesep,FoF), pattern=paste0("_z",zslice), full.names = T)
img=sapply(TIF, function(x) bioimagetools::readTIF(x), simplify = F)
img=sapply(img, function(x) EBImage::rotate(x,-180), simplify = F)
```
Figure 1A: Green and red Fucci channels overlaid.
```{r}
# Generate precursor images
# figname = paste0(OUTFIGS, filesep, "Fig1A_1.png")
# png(figname)
# bioimagetools::img(img[[1]][,,1]);
# dev.off()
#
# figname = paste0(OUTFIGS, filesep, "Fig1A_3.png")
# png(figname)
# bioimagetools::img(img[[3]][,,1]);
# dev.off()
# ImageJ edits
# open("Fig1A_1.png")
# run("Auto Crop");
# run("Green");
# run("RGB Color");
# open("Fig1A_3.png")
# run("Auto Crop");
# run("Red");
# run("RGB Color");
# imageCalculator("Add create", "Fig1A_1.png","Fig1A_3.png");
# selectImage("Result of Fig1A_1.png");
# run("Enhance Contrast...", "saturated=0.35");
# saveAs("Tiff", "Fig1A_overlay.tif");
figname = paste0(OUTFIGS, filesep, "Fig1A_",thedate,".png")
png(figname)
im <- bioimagetools::readTIF(paste0(OUTFIGS, filesep,"Fig1A_overlay.tif"))
im <- EBImage::resize(im, dim(im)[1]/1)
plot(raster::as.raster(im[,,,1]))
dev.off()
la=plotPNGorTIF(figname)
```
Figure 1B,C: Fucci based discrete cell cycle classification.
```{r}
palette(c("#FDE725FF", "#35B779FF", "#31688E", "#440154"))
figname = paste0(OUTFIGS, filesep, "Fig1B_",thedate,".png")
png(figname)
plot(fucci_raw$Intensity_IntegratedIntensity_green, fucci_raw$Intensity_IntegratedIntensity_red, col=fucci_raw$cellCycle, pch=20, log="xy", xlab="green signal", ylab="red signal", cex.lab=2, cex.axis=2)
legend("bottomright", names(fuccicol), fill=1:4)
dev.off()
la=plotPNGorTIF(figname)
fr = plyr::count(names(fuccicol)[fucci_raw$cellCycle])
fr$pct = fr$freq/sum(fr$freq)
print(fr)
# Generate precursor images
# cc <- fucci_ %>%
# select(ID, cellCycle)
# coord_ <- left_join(coord_, cc, by = join_by(id == ID))
#
# pal = colorRampPalette(c("#FDE725FF", "#35B779FF", "#31688E", "#440154"))
# figname = paste0(OUTFIGS, filesep,"overlay.png")
# png(figname)
# im <- bioimagetools::readTIF(paste0(OUTFIGS, filesep,"black_blank.tif"))
# im <- EBImage::resize(im, dim(im)[1]/1)
# plot(raster::as.raster(im[,,,1]))
# # points(coord_$x,coord_$y,col=coord_$cellCycle,main=paste(FoF,"fucci"),xaxt='n',yaxt='n', ann=FALSE)
# points(coord_$x,coord_$y,col=scales::alpha(pal(4)[coord_$cellCycle], 0.02),main=paste(FoF,"fucci"),xaxt='n',yaxt='n', ann=FALSE)
# dev.off()
#
# figname = paste0(OUTFIGS, filesep,"image.png")
# png(figname)
# bioimagetools::img(img[[2]][,,1]);
# legend("bottomleft", names(fuccicol) , fill=c("#FDE725FF", "#35B779FF", "#31688E", "#440154"))
# dev.off()
# Imagej edits
# open("image.png");
# open("overlay.png");
# For threshold, pass all colors and take all but the brightest and darkest intensities
# run("Color Threshold...");
# run("Make Inverse");
# setForegroundColor(0, 0, 0);
# Fill with black
# run("Fill", "slice");
# selectImage("overlay.png");
# run("Select None");
# Fill with black
# //setTool("rectangle");
# makeRectangle(68, 352, 32, 33);
# run("Fill", "slice");
# makeRectangle(77, 111, 4, 18);
# run("Fill", "slice");
# selectImage("image.png");
# run("Add Image...", "image=overlay.png x=0 y=0 opacity=60 zero");
# run("Flatten");
# saveAs("Tiff", "Fig1C_contour.tif");
figname = paste0(OUTFIGS, filesep, "Fig1C_",thedate,".png")
png(figname)
im <- bioimagetools::readTIF(paste0(OUTFIGS, filesep,"Fig1C_contour.tif"))
im <- EBImage::resize(im, dim(im)[1]/1)
plot(raster::as.raster(im[,,,1]))
dev.off()
la=plotPNGorTIF(figname)
```
Figure 1D: Fucci based pseudotime trajectory in PCA space.
```{r}
tmp=plot_dimred(model[[paste0("Fucci_",FoF)]],color_cells = "pseudotime", size_cells=1.84,size_milestones=5)
tmp
ggsave(paste0(OUTFIGS,filesep,"Fig1D_",thedate,"_Fucci_",FoF,"_pseudotimeTSNE.png"),units="px",width =480, height = 380, plot=tmp, dpi=100)
```
Figure 1E: Fucci based pseudotime inference reveals cell cycle progression
```{r}
# Arrange data for applying custom continuous color scale
cc <- fucci_ %>%
select(ID, pseudotime)
coord_a <- left_join(coord_, cc, by = join_by(id == ID))
coord_a$order <- findInterval(coord_a$pseudotime, sort(coord_a$pseudotime))
# Create legend image -> manually crop borders before producing the next graph
# pal = colorRampPalette(c("#FDE725FF", "#35B779FF", "#31688EFF", "#440154FF"))
# png(paste0(OUTFIGS, filesep, "legend.png"), width = 250, height = 300)
# plot.new()
# lgd_ = rep(NA, 9)
# lgd_[c(1,5,9)] = c("G1", "S", "G2/M")
# legend(x = 0, y = 1.02,
# legend = lgd_,
# fill = pal(9),
# border = NA,
# y.intersp = 0.5,
# bty = "n",
# cex = 2, text.font = 4)
# dev.off()
pal = colorRampPalette(c("#440154", "#31688E", "#35B779FF", "#FDE725FF"))
figname = paste0(OUTFIGS, filesep,"Fig1E_",thedate,".png")
png(figname)
bioimagetools::img(img[[2]][,,1]);
fucci_[,"pseudotimeCol"]=round(fucci_[,"pseudotime"]*10)
points(coord_a$x,coord_a$y,col=scales::alpha(pal(nrow(coord_a))[coord_a$order], 0.03),xaxt='n',yaxt='n', ann=FALSE,main=paste(FoF,"fucci pseudotime"))
legend <- png::readPNG(paste0(OUTFIGS, filesep, "legend.png"))
rasterImage(legend,0,0,150,200)
dev.off()
la=plotPNGorTIF(figname)
```
Figure 1F + supplementary Fig: Pseudotime is differentially distributed across the four discrete cell cycle classes.
```{r}
figname = paste0(OUTFIGS, filesep,"Fig1F_",thedate,"_plusSIFig.png")
png( figname, width = 1400, height = 400)
par(mfrow=c(1,4))
ii = sapply(FoFs[c(1,2,4,3)], function(x) grep(x,names(model$Fucci_All$pseudotime),value=T))
sapply(names(ii), function(x) boxplot(model$Fucci_All$pseudotime[ii[[x]]]~fucci_raw[ii[[x]],"cellCycle"], xlab="", names=names(fuccicol), ylab="fucci pseudotime", main=x,cex.lab=2,cex.main=1.2, cex.axis=2)); #
dev.off()
la=plotPNGorTIF(figname)
figname = paste0(OUTFIGS, filesep,"Fig1F_",thedate,".png")
png( figname,width = 480,height = 480)
boxplot(model$Fucci_All$pseudotime[rownames(fucci_raw)]~fucci_raw[,"cellCycle"], xlab="", names=names(fuccicol), ylab="fucci pseudotime", main="",cex.main=0.6, cex.axis=2, cex.lab=2)
dev.off()
la=plotPNGorTIF(figname)
```
Figure 2: Allen model training
```{r}
```
Figure 3: Quantification of nuclei, mitochondria and cytoplasm features at single-cell resolution with label free imaging. Setup first:
```{r}
coord_=readOrganelleCoordinates(signals_per_id[[FoF]], names(signals), OUTLINKED_)
imgStats_=imgStats[imgStats$FoF==FoF,]
doplotcentercoord=c(200, 200)
ii=which(coord_$signal=="nucleus.p")
centroids=grpstats(coord_[ii,c("x","y","z","id")], g = coord_$id[ii],statscols = "median")$median
o2=flexclust::dist2(centroids[,c("x","y")],doplotcentercoord)
alpha=list(cytoplasm.p=0.035,cytoplasm.t=0.01,nucleus.t=1,nucleus.p=0.135,mito.t=0.035,mito.p=0.035)
```
Figure 3B,C: Nuclei, mitochondria and cytoplasm of 10 representative cells from region highlighted in (A). Every data point is color-coded according to its cell membership.
```{r}
# Cells were selected manually for both spatial proximity and cycle state variation
coi=as.character(c(63, 43, 239, 26, 89, 86))
coi=setdiff(coi, rownames(imgStats_)[imgStats_$segmentationError])
coord__=coord_[coord_$id %in% coi,]
list_plots <- vector('list', length(coi))
for (i in seq_along(list_plots)) {
figname = paste0(OUTFIGS, filesep, "Fig2_", coi[i], "_", thedate, ".png")
png(figname, width = 600,height = 600)
par(mfrow=c(1,1), mai=c(0,0,0,0))
col=which(cell==coi)
a= coord__[coord__$id == coi[i],]
a[,xyz]=sweep(a[,xyz],MARGIN = 2,STATS = apply(a[,xyz],2, min),FUN = "-")
a[,xyz] = 1+ a[,xyz]
xyzlim=apply(a[,xyz],2, quantile, c(0,1))
a$z=z_interval*a$z
a_ <- a %>%
filter(id == coi[i], signal=="nucleus.p")
Plot_ConcaveHull(a_$x, a_$y, a_$z, xlim = xyzlim[,"x"], ylim = xyzlim[,"y"], zlim = xyzlim[,"z"],level = 10, lcolor = "magenta", alpha=0.25,add = F, phi = 90,theta = 0)
a_ <- a %>%
filter(id == coi[i], signal=="mito.p")
plot3D::points3D(a_$x, a_$y, a_$z,pch3d=20, axes=F, xlab="",ylab="", zlab="",col="yellow",alpha=0.2, add=T)
a_ <- a %>%
filter(id == coi[i], signal=="cytoplasm.p")
plot3D::points3D(a_$x, a_$y, a_$z,pch3d=20, axes=F, xlab="",ylab="", zlab="",col="cyan",alpha=0.2, add=T)
dev.off()
p <- magick::image_read(figname)
plot <- magick::image_trim(p)
list_plots[[i]] <- magick::image_ggplot(plot)
}
# Cells arranged correctly from left to right; relative spacing and image flip produced by hand for publication
list_plots[[1]]+list_plots[[3]]+list_plots[[2]]
ggsave(paste0(OUTFIGS, filesep, "Fig3B_", thedate, ".png"))
list_plots[[4]]+list_plots[[5]]+list_plots[[6]]
ggsave(paste0(OUTFIGS, filesep, "Fig3C_", thedate, ".png"))
```
Figure 3A: Predicted nuclei, mitochondria and cytoplasm signals.
```{r}
# ImageJ edits
# open("cytoplasm.p.tif");
# run("Cyan");
# run("RGB Color");
# open("nucleus.p.tif");
# run("Magenta");
# run("RGB Color");
# open("mito.p.tif");
# run("Yellow");
# imageCalculator("Add create stack", "cytoplasm.p.tif","nucleus.p.tif");
# selectImage("Result of cytoplasm.p.tif");
# imageCalculator("Add create stack", "Result of cytoplasm.p.tif","mito.p.tif");
# selectImage("Result of Result of cytoplasm.p.tif");
# saveAs("Tiff", "Fig3A_stack.tif");
# run("Slice Keeper", "first=14 last=14 increment=1");
# saveAs("Tiff", "Fig3A_slice.tif");
# Create legend image -> manually crop borders before producing the next graph
# pal = colorRampPalette(c("magenta", "white", "yellow", "white", "cyan"))
# png(paste0(OUTFIGS, filesep, "legend_2.png"), width = 300, height = 250)
# plot.new()
# lgd_ = rep(NA, 5)
# lgd_[c(1,3,5)] = c("Nucleus", "Mitochondria", "Cytoplasm")
# legend(x = 0, y = 1.02,
# legend = lgd_,
# fill = pal(5),
# border = NA,
# y.intersp = 0.5,
# bty = "n",
# cex = 2, text.font = 0.2)
# dev.off()
# Get center of cells, correct coordinates for image size
cent_df <- coord_ %>%
group_by(id) %>%
summarise(x = mean(x), y = mean(y)) %>%
mutate(x = 1024 - (x*4.3103), y = y*4.3103)
# Select coordinates for target cells
cent_df <- cent_df %>%
filter(id %in% coi)
# Assign labels for cells for figure
cent_labels <- cent_df[order(-cent_df$y),]
cent_labels <- cent_labels %>%
mutate(cell = row_number())
# Create labeled image for splicing into figure
# figname = paste0(OUTFIGS, filesep, "Fig3A_custom_labeled.png")
# png(figname, width = 1024, height = 1024, units = "px")
# im <- bioimagetools::readTIF(paste0(OUTFIGS, filesep,"Fig3A_slice.tif"))
# im <- EBImage::resize(im, dim(im)[1]/1)
# plot(raster::as.raster(im[,,,1]))
# text(cent_labels$x, cent_labels$y+4, labels=cent_labels$cell, col="green", cex=1)
# dev.off()
# Create unlabeled image for main figure
figname = paste0(OUTFIGS, filesep, "Fig3A_",thedate,".png")
png(figname, width = 1024, height = 1024, units = "px")
im <- bioimagetools::readTIF(paste0(OUTFIGS, filesep,"Fig3A_slice.tif"))
im <- EBImage::resize(im, dim(im)[1]/1)
plot(raster::as.raster(im[,,,1]))
legend <- png::readPNG(paste0(OUTFIGS, filesep, "legend_2.png"))
rasterImage(legend,0,0,200,100)
dev.off()
la=plotPNGorTIF(figname)
```
Figure 3D: Cell features quantified from label free imaging correlate with cell cycle classes as defined by FUCCI.
```{r}
# Get list of features and cell cycle states
var_list <- svm$svmFeatures
cycle <- fucci_raw[rownames(imgStats),"cellCycle"]
# Merge cell feature data and cell cycle states with IDs
imgStats_b <- imgStats %>%
rownames_to_column(var = "long_ID") %>%
cbind(cycle) %>%
dplyr::select(c(long_ID, ID, FoF, cellCycle = cycle, var_list))
# Set colors per field
col <- c("FoF1_231005_fluorescent.nucleus" = "yellow",
"FoF2_231005_fluorescent.nucleus" = "red",
"FoF3_231005_fluorescent.nucleus" = "green",
"FoF4_231005_fluorescent.nucleus" = "blue")
# Plot per feature
list_plots <- vector('list', length(var_list))
for (i in seq_along(list_plots)) {
list_plots[[i]] <- ggplot(imgStats_b) +
geom_boxplot(aes(as.character(cellCycle), .data[[var_list[i]]], fill = FoF)) +
scale_fill_manual(values = col) +
theme_light() +
theme(axis.title.x=element_blank(), legend.position = "none") +
scale_x_discrete(labels = c("1" = "G1", "2" = "G1/S", "3" = "S", "4" = "G2"))
}
# Add legend
list_plots[[11]] <- ggplot(imgStats_b) +
geom_boxplot(aes(as.character(cellCycle), vol_nucleus.p, fill = FoF)) +
scale_fill_manual(values = col) +
theme_light() +
theme(axis.title.x=element_blank()) +
scale_x_discrete(labels = c("1" = "G1", "2" = "G1/S", "3" = "S", "4" = "G2"))
# Assemble plots
a <- (list_plots[[1]] + list_plots[[2]] + list_plots[[3]])
b <- (list_plots[[4]] + list_plots[[5]] + list_plots[[6]])
c <- (list_plots[[7]] + list_plots[[8]] + list_plots[[9]])
d <- (list_plots[[11]] + list_plots[[10]])
# Generate figures
figname <- paste0(OUTFIGS, filesep, "Fig3D_",thedate,".png")
ggsave(figname,units="px",width = 480, height = 480, plot=list_plots[[1]], dpi=140)
la=plotPNGorTIF(figname)
a/b/c/d
figname <- paste0(OUTFIGS, filesep, "Fig3D_",thedate,"_plusSIFig.png")
ggsave(figname,units="px",width = 1980, height = 2380, dpi=300)
```
Figure 4: Characterization of discrete and continuous cell cycle progression in NCI-N87 with label free imaging.
Figure 4A: Supervised approach to predict discrete cell cycle state from nuclei, mitochondria and cytoplasm features.
```{r}
# Make diagram in pptx showing:
print(paste("Total cells:",nrow(imgStats),"split into:"), quote = F)
print(paste("Training:",length(unlist(svm$training)),"cells"), quote = F)
print(paste("Testing:",length(unlist(svm$testing)),"cells"), quote = F)
print(paste("SVM trained on",length(svm$svmFeatures),"features and applied on test set"))
# Define the variable
tot_cells = nrow(imgStats)
num_cells_A <- length(unlist(svm$training))
num_cells_B <- length(unlist(svm$testing))
num_features <- length(svm$svmFeatures)
figname = paste0(OUTFIGS, filesep, "Fig4A_",thedate,".png")
# Create the diagram using the grViz function
graph <- grViz(glue("
digraph diagram {{
# Define the graph attributes
graph [layout = dot, rankdir = TB]
# Define the node attributes
node [shape = box, style = filled, color = lightgrey, fontname = Helvetica]
# Define the nodes
node1 [label = '{tot_cells} Total cells']
node2 [label = '{num_cells_A} cells']
node3 [label = '{num_cells_B} cells']
node4 [label = 'SVM trained on {num_features} cell features']
# Define the edges (arrows) with labels on the opposite side
node1 -> node2 [label = 'Training', labeldistance = 2, labelangle = 180]
node1 -> node3 [label = 'Test', labeldistance = 2, labelangle = 180]
node2 -> node4
node4 -> node3 [label = 'application']
# Subgraph to control the layout
{{ rank = same; node2; node3 }}
{{ rank = max; node4 }}
}}
"))
graph
# Export the graph to a PNG file
graph <- DiagrammeRsvg::export_svg(graph)
graph <- charToRaw(graph)
rsvg::rsvg_png(graph, figname)
```
Figure 4B: Performance of trained classifier on test set.
```{r}
confMat = read.xlsx(paste0(OUTPSEUDOTIME, filesep,"cellCyclePrediction_svm_2024-05-15.xlsx"), sheetIndex = 1, row.names=T)
tmp=sapply(c("Class..1","Class..2","Class..3","Class..4"), function(x) as.numeric(confMat[grep(x, rownames(confMat)),"Balanced.Accuracy"]))
figname=paste0(OUTFIGS, filesep,"Fig4B_",thedate,".png")
png(figname )
boxplot(tmp, names=names(fuccicol), ylab="Balanced.Accuracy",cex.axis=2, cex.lab=1.7)
dev.off()
la=plotPNGorTIF(figname)
```
Figure 4C: Unsupervised approach to predict continuous cell cycle progression from nuclei, mitochondria and cytoplasm features. .
```{r}
tmp=plot_dimred(model[[FoF]],color_cells = "pseudotime", size_cells=1.84,size_milestones=5)
tmp
ggsave(paste0(OUTFIGS,filesep,"Fig4C_",thedate,"_labelFree_",FoF,"_pseudotimeTSNE.png"),units="px",width = 0.9*480, height = 480, plot=tmp, dpi=100)
```
Figure 4D + supplementary Fig: Pseudotime derived from label free imaging features is differentially distributed across the four FUCCI-informed cell cycle classes.
```{r}
figname=paste0(OUTFIGS, filesep,"Fig4D_",thedate,".png")
png(figname)
par(mfrow=c(1,1))
boxplot(model$All$pseudotime[rownames(fucci_raw)]~fucci_raw[,"cellCycle"], xlab="", names=names(fuccicol), ylab="label free pseudotime", main="",cex.main=0.6, cex.axis=1.506, cex.lab=1.7)
dev.off()
la=plotPNGorTIF(figname)
figname=paste0(OUTFIGS, filesep,"Fig4D_",thedate,"_plusSIFig.png")
png(figname, width = 1000, height = 800)
par(mfrow=c(2,3))
ii = sapply(FoFs[c(1,2,4,3)], function(x) grep(x,rownames(fucci_raw),value=T))
# sapply(names(ii), function(x) boxplot(model[[x]]$pseudotime[ii[[x]]]~fucci_raw[ii[[x]],"cellCycle"], xlab="", names=names(fuccicol), ylab="label free pseudotime", main=x,cex.main=0.6))
sapply(names(ii), function(x) boxplot(model$All$pseudotime[ii[[x]]]~fucci_raw[ii[[x]],"cellCycle"], xlab="", names=names(fuccicol), ylab="label free pseudotime", main=x,cex.lab=1.7, cex.axis=2,cex.main=1.2))
ii=names(model$All$pseudotime)
ii=grep(FoFs[1], ii, value=T, invert = T)
te=cor.test(model$All$pseudotime[ii], model$Fucci_All$pseudotime[ii])
col=rainbow(length(unique(fucci_raw[ii,"FoF"])))
names(col)=unique(fucci_raw[ii,"FoF"])
plot(model$All$pseudotime[ii], model$Fucci_All$pseudotime[ii], main=paste("r=",round(te$estimate,2),"P=",te$p.value), xlab="label free pseudotime", ylab="fucci pseudotime", col=col[fucci_raw[ii,"FoF"]])
legend("bottomleft",sapply(strsplit(names(col),"_"),"[[",1),fill=col)
dev.off()
la=plotPNGorTIF(figname)
```
Figure 4E: representative cell across cell cycle: fucci.
```{r}
# Create an 'is not in' function
'%!in%' <- function(x,y)!('%in%'(x,y))
# Get fucci signals and coordinates
fucci_df <- coord_[grep("nuc", coord_$signal),]
fucci_df$cell <- paste0("cell", fucci_df$id)
rownames(fucci_df) <- NULL
# Get pseudotimes
pseud_df <- model$Fucci_All$pseudotime
pseud_df$cell_id <- rownames(pseud_df)
pseud_df <- as_tibble(pseud_df)
pseud_df <- pseud_df %>%
pivot_longer(cols = everything()) %>%
separate(name, into = c("FoF", NA, NA, "cell"), sep = "_") %>%
filter(FoF == "FoF3")
# Merge fucci signals/coordinates with pseudotimes
fucci_pseud <- left_join(fucci_df, pseud_df, by = join_by(cell == cell))
colnames(fucci_pseud)[colnames(fucci_pseud) == 'value'] <- 'pseudotime'
# Get three sets of cells with pseudotimes closest to the listed values
target_list_1 <- pseud_df %>%
filter(!if_any(value, is.na)) %>%
filter(abs(value-0.1)==min(abs(value-0.1)) |
abs(value-0.3)==min(abs(value-0.3)) |
abs(value-0.5)==min(abs(value-0.5)) |
abs(value-0.7)==min(abs(value-0.7)) |
abs(value-0.9)==min(abs(value-0.9)))
target_list_2 <- pseud_df %>%
filter(!if_any(value, is.na)) %>%
filter(cell %!in% unique(target_list_1$cell)) %>%
filter(abs(value-0.1)==min(abs(value-0.1)) |
abs(value-0.3)==min(abs(value-0.3)) |
abs(value-0.5)==min(abs(value-0.5)) |
abs(value-0.7)==min(abs(value-0.7)) |
abs(value-0.9)==min(abs(value-0.9)))
target_list_3 <- pseud_df %>%
filter(!if_any(value, is.na)) %>%
filter(cell %!in% unique(target_list_1$cell) & cell %!in% unique(target_list_2$cell)) %>%
filter(abs(value-0.1)==min(abs(value-0.1)) |
abs(value-0.3)==min(abs(value-0.3)) |
abs(value-0.5)==min(abs(value-0.5)) |
abs(value-0.7)==min(abs(value-0.7)) |
abs(value-0.9)==min(abs(value-0.9)))
target_df <- fucci_pseud %>%
filter(cell %in% unique(target_list_1$cell) |
cell %in% unique(target_list_2$cell) |
cell %in% unique(target_list_3$cell))
# Get data of target cells
target_cells <- target_df %>%
group_by(id) %>%
summarise(pseudotime = mean(pseudotime))
# Graph cells
target_cells_list <- unique(target_df$id)
list_plots <- vector('list', length(target_cells_list))
for(j in seq_along(list_plots)) {
a <- coord_[coord_$id %in% target_cells_list[j],]
a[,xyz] <- sweep(a[,xyz], MARGIN = 2, STATS = apply(a[,xyz], 2, min), FUN = "-")
a[,xyz] <- 1 + a[,xyz]
xyzlim <- apply(a[,xyz], 2, quantile, c(0,1))
a$z <- z_interval * a$z
a_ <- a %>%
filter(id == target_cells_list[j], signal == "nucleus.p")
figname <- paste0(OUTFIGS, filesep, "Fig4E_", target_cells_list[j], ".png")
png(figname, width = 600, height = 600)
Plot_ConcaveHull(a_$x, a_$y, a_$z, xlim = xyzlim[,"x"], ylim = xyzlim[,"y"], zlim = xyzlim[,"z"], level = 10, lcolor = "gray", alpha = 0.25, add = F, phi = 30, theta = 140)
a_$fucci_ch00 <- round(a_$fucci_ch00, 1)
a_$fucci_ch02 <- round(a_$fucci_ch02, 2)
for(x in unique(a_$fucci_ch00)){
i <- which(a_$fucci_ch00 == x)
plot3D::points3D(a_$x[i], a_$y[i], a_$z[i], pch3d = 20, axes = F, xlab = "", ylab = "", zlab = "", col = "red", alpha = x, add = T)
}
for(x in unique(a_$fucci_ch02)){
i <- which(a_$fucci_ch02 == x)
plot3D::points3D(a_$x[i], a_$y[i], a_$z[i], pch3d = 20, axes = F, xlab = "", ylab = "", zlab = "", col = "green", alpha = x, add = T)
}
dev.off()
}
# Add labels
for(j in seq_along(list_plots)) {
figname <- paste0(OUTFIGS, filesep, "Fig4E_", target_cells_list[j], ".png")
p <- magick::image_read(figname)
plot <- magick::image_trim(p)
plot <- magick::image_ggplot(plot)
q <- target_cells %>%
filter(id == target_cells_list[j])
xlab <- round(q$pseudotime[1], digits = 4)
list_plots[[j]] <- plot +
ggtitle(xlab)
}
# Create legend image
pal = colorRampPalette(c("green", "white", "red"))
png(paste0(OUTFIGS, filesep, "legend_3.png"), width = 300, height = 250)
plot.new()
lgd_ = rep(NA, 3)
lgd_[c(1,3)] = c("fucci_ch00", "fucci_ch02")
legend(x = 0, y = 1.02,
legend = lgd_,
fill = pal(3),
border = NA,
y.intersp = 0.5,
bty = "n",
cex = 2, text.font = 0.2)
dev.off()
p <- magick::image_read(paste0(OUTFIGS, filesep, "legend_3.png"))
plot <- magick::image_trim(p)
legend <- magick::image_ggplot(plot)
# Assemble figure: order determined manually for ascending pseudotime top to bottom, left to right
png(paste0(OUTFIGS, filesep, "Fig4E_", thedate, ".png"), width = 1500, height = 750)
grid.arrange(
arrangeGrob(
textGrob("A", x = 0, y = 1, vjust = 1, hjust = 0, gp = gpar(fontsize = 12, fontface = "bold")),
list_plots[[5]], list_plots[[4]], list_plots[[3]], list_plots[[1]], list_plots[[15]], legend,
ncol = 7,
widths = unit(c(0.1, rep(0.5, 5), 0.3), "null")
),
arrangeGrob(
textGrob("B", x = 0, y = 1, vjust = 1, hjust = 0, gp = gpar(fontsize = 12, fontface = "bold")),
list_plots[[14]], list_plots[[8]], list_plots[[6]], list_plots[[2]], list_plots[[9]], legend,
ncol = 7,
widths = unit(c(0.1, rep(0.5, 5), 0.3), "null")
),
arrangeGrob(
textGrob("C", x = 0, y = 1, vjust = 1, hjust = 0, gp = gpar(fontsize = 12, fontface = "bold")),
list_plots[[10]], list_plots[[12]], list_plots[[7]], list_plots[[13]], list_plots[[11]],legend,
ncol = 7,
widths = unit(c(0.1, rep(0.5, 5), 0.3), "null")
),
nrow = 3
)
```
Figure 4F: representative cell across cell cycle: label free.
```{r}
# Get coordinates
coord_df <- coord_
coord_df$cell <- paste0("cell", coord_df$id)
# Get label free pseudotimes
pseud_df <- model$All$pseudotime
pseud_df$cell_id <- rownames(pseud_df)
pseud_df <- as_tibble(pseud_df)
pseud_df <- pseud_df %>%
pivot_longer(cols = everything()) %>%
separate(name, into = c("FoF", NA, NA, "cell"), sep = "_") %>%
filter(FoF == "FoF3")
# Merge coordinates with pseudotimes
coord_pseud <- left_join(coord_df, pseud_df, by = join_by(cell == cell))
colnames(coord_pseud)[colnames(coord_pseud) == 'value'] <- 'pseudotime'
# Get three sets of cells with pseudotimes closest to the listed values
target_list_1 <- pseud_df %>%
filter(!if_any(value, is.na)) %>%
filter(abs(value-0.1)==min(abs(value-0.1)) |
abs(value-0.3)==min(abs(value-0.3)) |
abs(value-0.5)==min(abs(value-0.5)) |
abs(value-0.7)==min(abs(value-0.7)) |
abs(value-0.9)==min(abs(value-0.9)))
target_list_2 <- pseud_df %>%
filter(!if_any(value, is.na)) %>%
filter(cell %!in% unique(target_list_1$cell)) %>%
filter(abs(value-0.1)==min(abs(value-0.1)) |
abs(value-0.3)==min(abs(value-0.3)) |
abs(value-0.5)==min(abs(value-0.5)) |
abs(value-0.7)==min(abs(value-0.7)) |
abs(value-0.9)==min(abs(value-0.9)))
target_list_3 <- pseud_df %>%
filter(!if_any(value, is.na)) %>%
filter(cell %!in% unique(target_list_1$cell) & cell %!in% unique(target_list_2$cell)) %>%
filter(abs(value-0.1)==min(abs(value-0.1)) |
abs(value-0.3)==min(abs(value-0.3)) |
abs(value-0.5)==min(abs(value-0.5)) |
abs(value-0.7)==min(abs(value-0.7)) |
abs(value-0.9)==min(abs(value-0.9)))
# Get data of target cells
target_df <- coord_pseud %>%
filter(cell %in% unique(target_list_1$cell) |
cell %in% unique(target_list_2$cell) |
cell %in% unique(target_list_3$cell))
# Graph cells
for(i in unique(target_df$cell)) {
p <- target_df %>%
filter(cell == i)
# Get pseudotime for cell
xlab <- round(p[1,14], digits = 4)
a <- ggplot(p) +
geom_tile(aes(x, y, fill = signal, alpha = signal)) +
scale_alpha_manual(values = c("nucleus.p" = 0.01, "mito.p" = 0.05, "cytoplasm.p" = 0.05)) +
theme_void() +
theme(legend.position = "none",
axis.title.x = element_text(),
panel.background = element_rect(fill = 'black', colour = 'black')) +
labs(x = xlab) +
scale_fill_manual(values = c("nucleus.p" = "magenta", "mito.p" = "yellow", "cytoplasm.p" = "cyan"))
assign(paste0("plot_", i), a)
}
# Add legends to cells with pseudotime ~0.9
p <- target_df %>%
filter(cell == "cell199")
xlab <- round(p[1,14], digits = 4)
plot_cell199 <- ggplot(p) +
geom_tile(aes(x, y, fill = signal, alpha = signal)) +
scale_alpha_manual(values = c("nucleus.p" = 0.01, "mito.p" = 0.05, "cytoplasm.p" = 0.03)) +
theme_void() +
theme(axis.title.x = element_text(),
panel.background = element_rect(fill = 'black', colour = 'black')) +
labs(x = xlab) +
guides(fill = guide_legend(override.aes = list(alpha = 1))) +
scale_fill_manual(values = c("nucleus.p" = "magenta", "mito.p" = "yellow", "cytoplasm.p" = "cyan"))
p <- target_df %>%
filter(cell == "cell216")
xlab <- round(p[1,14], digits = 4)
plot_cell216 <- ggplot(p) +
geom_tile(aes(x, y, fill = signal, alpha = signal)) +
scale_alpha_manual(values = c("nucleus.p" = 0.01, "mito.p" = 0.05, "cytoplasm.p" = 0.03)) +
theme_void() +
theme(axis.title.x = element_text(),
panel.background = element_rect(fill = 'black', colour = 'black')) +
labs(x = xlab) +
guides(fill = guide_legend(override.aes = list(alpha = 1))) +
scale_fill_manual(values = c("nucleus.p" = "magenta", "mito.p" = "yellow", "cytoplasm.p" = "cyan"))
p <- target_df %>%
filter(cell == "cell80")
xlab <- round(p[1,14], digits = 4)
plot_cell80 <- ggplot(p) +
geom_tile(aes(x, y, fill = signal, alpha = signal)) +
scale_alpha_manual(values = c("nucleus.p" = 0.01, "mito.p" = 0.05, "cytoplasm.p" = 0.03)) +
theme_void() +
theme(axis.title.x = element_text(),
panel.background = element_rect(fill = 'black', colour = 'black')) +
labs(x = xlab) +
guides(fill = guide_legend(override.aes = list(alpha = 1))) +
scale_fill_manual(values = c("nucleus.p" = "magenta", "mito.p" = "yellow", "cytoplasm.p" = "cyan"))
# Assemble figure: order determined manually for ascending pseudotime top to bottom, left to right
png(paste0(OUTFIGS, filesep, "Fig4F_", thedate, ".png"), width = 1500, height = 750)
grid.arrange(
arrangeGrob(
textGrob("A", x = 0, y = 1, vjust = 1, hjust = 0, gp = gpar(fontsize = 12, fontface = "bold")),
plot_cell23, plot_cell160, plot_cell158, plot_cell140, plot_cell199,
ncol = 6,
widths = unit(c(0.1, rep(0.5, 4), 0.8), "null")
),
arrangeGrob(
textGrob("B", x = 0, y = 1, vjust = 1, hjust = 0, gp = gpar(fontsize = 12, fontface = "bold")),
plot_cell63, plot_cell270, plot_cell208, plot_cell301, plot_cell216,
ncol = 6,
widths = unit(c(0.1, rep(0.5, 4), 0.8), "null")
),
arrangeGrob(
textGrob("C", x = 0, y = 1, vjust = 1, hjust = 0, gp = gpar(fontsize = 12, fontface = "bold")),
plot_cell40, plot_cell149, plot_cell21, plot_cell189, plot_cell80,
ncol = 6,
widths = unit(c(0.1, rep(0.5, 4), 0.8), "null")
),
nrow = 3
)
dev.off()
```
Figure 5: Sequencing imaging integration
Figure 5A: Sequencing derived pseudotime of cell cycle progression
```{r}
# cell_ID <- model$Seq$cell_ids
# model_df <- cbind(pseudotime = model$Seq$pseudotime, cell_ID)
tmp=plot_dimred(model$Seq,color_cells = "pseudotime", size_cells=1.84,size_milestones=5)
tmp
ggsave(paste0(OUTFIGS,filesep,"Fig5A_",thedate,"_scRNA_pseudotimeTSNE.png"), units="px",width = 680, height = 480, plot=tmp, dpi=100)
```
Figure 5B: Co-clustering of sequenced and imaged cells based on pseudotime
```{r}
# Co-cluster image and sequencing stats
load(paste0(OUTPSEUDOTIME, filesep,"scRNAdataset_2024-05-15.RObj")); ## loads pq
tmp=as.data.frame(model$All$pseudotime)
colnames(tmp)="pseudotime"