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dropseq_seurat_splitDEMs.R
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dropseq_seurat_splitDEMs.R
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#!/usr/local/bin/Rscript
#------------------------------------------------------------------------------
# DROPSEQ Analysis to accompany:
#
# Bacon et al, 2018
# Single-Cell Analysis Identifies Thymic Maturation Delay in Growth-Restricted
# Neonatal Mice
#
# Link to publication
# TO ADD ONCE AVAILABLE
#
# Script available from:
# https://github.com/CTR-BFX/2018_Bacon_Charnock-Jones
#
# CTR Code: CTR_DROPSEQ_0009
#
# Analysis Performed by Russell S. Hamilton
# CTR Bioinformatics
# Centre for Trophoblast Reseach, University of Cambridge, UK
# Copyright Russell S. Hamilton ([email protected])
#
#------------------------------------------------------------------------------
# License Information
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, version 3.
#
# This program is distributed in the hope that it will be useful, but
# WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
#------------------------------------------------------------------------------
#
# message("+--- Uncomment if packages need installing ---+")
#
#install.packages("devtools")
#library("devtools")
#curl -O http://r.research.att.com/libs/gfortran-4.8.2-darwin13.tar.bz2
#sudo tar fvxz gfortran-4.8.2-darwin13.tar.bz2 -C /
#install_github("satijalab/seurat")
#install.packages("dplyr", type = "source")
#devtools::install_github("lazappi/clustree", build_vignettes = TRUE)
message("+--- Loading in the libraries (start up messages supressed) ---+")
suppressPackageStartupMessages({
library("dplyr")
library("methods")
library("utils")
library("ggplot2")
library("cowplot")
library("Seurat")
library("Matrix")
library("useful")
library("reshape2")
library("biomaRt")
library("scran")
library("scater")
library("SingleCellExperiment")
})
genecut <- 300
mincells <- 3
resolution <- "0.4_to_1.0"
#resolution <- "0.6"
slxID <- "SLX-7632"
normalisation <- "log2"
# Script has to be run individually for with and without cell cycle regression
runcellcycle <- "0"
if(runcellcycle == 1){
regression <- "umi_mt_cc"
}else{
regression <- "umi_mt"
}
message(paste0("Regression = ", regression))
message(paste0("Resolution = ", resolution))
message(paste0("Normalisation = ", normalisation))
#
# Basedir will need updating to the appropriate location on the local system
#
baseDir <- "/storage/CTR-Projects/CTR_DropSeq/CTR_DropSeq_0009/SLX-7632/"
message("+--- Set up some useful functions --+")
centroidFunction <- function(tSNEClusterTable){
return (data.frame( x=mean(tSNEClusterTable$tSNE_1),y=mean(tSNEClusterTable$tSNE_2))) }
meanCentroidDist <- function(tSNEClusterTable, centroid){
median <- median( apply(tSNEClusterTable,1,function(x,centroid)
{(sqrt((tSNEClusterTable$tSNE_1 - centroid[1])^2+(tSNEClusterTable$tSNE_2-centroid[2])^2))},centroid) )
return( round(median, 3) ) }
message("+--- Download the ensEMBL gene identifiers ---+")
ensembl <- useEnsembl(biomart="ensembl", dataset="mmusculus_gene_ensembl")
ensEMBL2id <- getBM(attributes=c('ensembl_gene_id', 'external_gene_name'), mart = ensembl)
message("+--- Read in the DEM file (WT) ---+")
matrix.data.WT <- read.table(gzfile(
paste0(baseDir, "SLX-7632.XXXXXXXXXX.WT/SLX-7632.XXXXXXXXXX.WT.dge.txt.gz")),
sep="\t",header=TRUE,row.names=1, stringsAsFactors = TRUE)
dim(matrix.data.WT)
message("+--- Read in the DEM file (KO) ---+")
matrix.data.KO <- read.table(gzfile(
paste0(baseDir, "SLX-7632.XXXXXXXXXX.KO/SLX-7632.XXXXXXXXXX.KO.dge.txt.gz")),
sep="\t",header=TRUE,row.names=1, stringsAsFactors = TRUE)
dim(matrix.data.KO)
message("+--- Read in the DEM file (WT and KO) ---+")
matrix.data <- read.table(gzfile(
paste0(baseDir, "SLX-7632.XXXXXXXXXX.20180117/SLX-7632.XXXXXXXXXX.dge.txt.gz")),
sep="\t",header=TRUE,row.names=1, stringsAsFactors = TRUE)
dim(matrix.data)
message("+--- Set Up the seutat matrix (WT) ---+")
matrix.su.WT <- CreateSeuratObject(raw.data = matrix.data.WT,
min.cells = mincells,
min.genes = genecut,
project = paste(slxID, "WT genecut=", genecut, " res=", resolution, sep=""))
message("+--- Set Up the seutat matrix (KO) ---+")
matrix.su.KO <- CreateSeuratObject(raw.data = matrix.data.KO,
min.cells = mincells,
min.genes = genecut,
project = paste(slxID, "KO genecut=", genecut, " res=", resolution, sep=""))
message("+--- wtvarplusko Set Up the seutat object for wt var genes plus KO method ---+")
matrix.su <- CreateSeuratObject(raw.data = matrix.data,
min.cells = mincells,
min.genes = genecut,
project = paste(slxID, "WTKO genecut=", genecut, " res=", resolution, sep=""))
message("+--- SCRAN/Cyclone Cell Cycle Calculations FULL WT/KO Matrix ---+")
matrix.data <- [email protected]
matrix.data$external_gene_name <- rownames(matrix.data)
matrix.data <- merge(matrix.data, ensEMBL2id, by="external_gene_name" )
rownames(matrix.data) <- matrix.data$ensembl_gene_id
matrix.data <- matrix.data[, -grep("external_gene_name", colnames(matrix.data))]
matrix.data <- matrix.data[, -grep("ensembl_gene_id", colnames(matrix.data))]
matrix.data <- data.matrix(matrix.data, rownames.force = TRUE)
dim(matrix.data)
numcells.raw <- ncol(matrix.su@data)
message(paste0("Number of cells (processed)=", numcells.raw))
message("Number of cells from [email protected]")
cells <- colnames([email protected])
length(cells)
message("Number of rows from matrix.su")
length( rownames(matrix.su@data) )
if(runcellcycle == 1){
message("+--- Cell cycle phase assignment ---+")
sce.annot <- SingleCellExperiment(list(counts=matrix.data))
mm.pairs <- readRDS(system.file("exdata", "mouse_cycle_markers.rds", package="scran"))
assigned <- cyclone(sce.annot, pairs=mm.pairs)
message( table(assigned$phases) )
meta_data_CC <- data.frame("Cell_cycle"=assigned$phases)
rownames(meta_data_CC) <- cells
matrix.su <- AddMetaData(matrix.su, metadata=meta_data_CC)
meta_data_G1 <- data.frame("Cell_cycle_score_G1"=assigned$scores$G1)
rownames(meta_data_G1) <- cells
matrix.su <- AddMetaData(matrix.su, metadata=meta_data_G1)
meta_data_S <- data.frame("Cell_cycle_score_S"=assigned$scores$S)
rownames(meta_data_S) <- cells
matrix.su <- AddMetaData(matrix.su, metadata=meta_data_S)
meta_data_G2M <- data.frame("Cell_cycle_score_G2M"=assigned$scores$G2M)
rownames(meta_data_G2M) <- cells
matrix.su <- AddMetaData(matrix.su, metadata=meta_data_G2M)
[email protected]$Cell_Cycle_Diff <- [email protected]$Cell_cycle_score_S - [email protected]$Cell_cycle_score_G2M
message( head([email protected]$Cell_Cycle_Diff) )
} else {
message("+--- SKIPPING Cell cycle phase assignment ---+")
}
message("+--- wtvarplusko Look at MT genes (WT)---+")
mito.genes <- grep("^mt-", rownames(matrix.su@data), value = T)
percent.mito <- colSums(expm1(matrix.su@data[mito.genes, ]))/colSums(expm1(matrix.su@data))
matrix.su <- AddMetaData(matrix.su, percent.mito, "percent.mito")
matrix.su <- FilterCells(object = matrix.su,
subset.names = c("nGene", "percent.mito"),
low.thresholds = c(genecut, -Inf), high.thresholds = c(2500, 0.05))
message("+--- wtvarplusko Summary of genes / cells in the matrix (WT) ---+")
numrows <- nrow(matrix.su@data)
numcells <- ncol(matrix.su@data)
message( paste("+--- wtvarplusko Number of cells=", numcells, " Number of genes=", numrows, " ---+", sep=""))
if(numcells < 100){ message("+--- wtvarplusko Below 100 cells after filtering ---+"); return(numcells)}
message("+--- wtvarplusko nUMI Summary (WT) ---+")
summary( [email protected]$nUMI )
message("+--- wtvarplusko nGene Summary (WT) ---+")
summary( [email protected]$nGene )
message("+--- wtvarplusko Normalise Data (WT) ---+")
matrix.su <- NormalizeData(object = matrix.su, normalization.method = "LogNormalize", scale.factor = 1e4)
matrix.su.WTvar <- matrix.su
message("+--- wtvarplusko FindVariableGenes (WT) ---+")
matrix.su <- FindVariableGenes(object = matrix.su, do.plot=FALSE, mean.function = ExpMean,
dispersion.function = LogVMR,
x.low.cutoff = 0.0125, x.high.cutoff = 3, y.cutoff = 0.5)
message("+--- wtvarplusko Scale Data (WT) ---+")
if(runcellcycle == 1) {
matrix.su <- ScaleData(object = matrix.su, vars.to.regress = c("nUMI", "percent.mito", "Cell_Cycle_Diff"))
matrix.su.WTvar <- ScaleData(object = matrix.su.WTvar, vars.to.regress = c("nUMI", "percent.mito", "Cell_Cycle_Diff"))
} else {
matrix.su <- ScaleData(object = matrix.su, vars.to.regress = c("nUMI", "percent.mito"))
matrix.su.WTvar <- ScaleData(object = matrix.su.WTvar, vars.to.regress = c("nUMI", "percent.mito"))
}
if(runcellcycle == 1){
message("+--- SCRAN/Cyclone Cell Cycle Calculations ONLY WT Matrix ---+")
matrix.WT.data <- [email protected]
matrix.WT.data$external_gene_name <- rownames(matrix.WT.data)
matrix.WT.data <- merge(matrix.WT.data, ensEMBL2id, by="external_gene_name" )
rownames(matrix.WT.data) <- matrix.WT.data$ensembl_gene_id
matrix.WT.data <- matrix.WT.data[, -grep("external_gene_name", colnames(matrix.WT.data))]
matrix.WT.data <- matrix.WT.data[, -grep("ensembl_gene_id", colnames(matrix.WT.data))]
matrix.WT.data <- data.matrix(matrix.WT.data, rownames.force = TRUE)
dim(matrix.WT.data)
numcells.raw <- ncol(matrix.su.WT@data)
message( paste0("Number of cells (processed)=", numcells.raw))
message("Number of cells from [email protected]")
cells <- colnames([email protected])
length(cells)
message("Number of rows from matrix.su.WT")
length( rownames(matrix.su.WT@data) )
message("+--- Cell cycle phase assignment ---+")
sce.annot <- SingleCellExperiment(list(counts=matrix.WT.data))
mm.pairs <- readRDS(system.file("exdata", "mouse_cycle_markers.rds", package="scran"))
assigned <- cyclone(sce.annot, pairs=mm.pairs)
message( table(assigned$phases) )
meta_data_CC <- data.frame("Cell_cycle"=assigned$phases)
rownames(meta_data_CC) <- cells
matrix.su.WT <- AddMetaData(matrix.su.WT, metadata=meta_data_CC)
meta_data_G1 <- data.frame("Cell_cycle_score_G1"=assigned$scores$G1)
rownames(meta_data_G1) <- cells
matrix.su.WT <- AddMetaData(matrix.su.WT, metadata=meta_data_G1)
meta_data_S <- data.frame("Cell_cycle_score_S"=assigned$scores$S)
rownames(meta_data_S) <- cells
matrix.su.WT <- AddMetaData(matrix.su.WT, metadata=meta_data_S)
meta_data_G2M <- data.frame("Cell_cycle_score_G2M"=assigned$scores$G2M)
rownames(meta_data_G2M) <- cells
matrix.su.WT <- AddMetaData(matrix.su.WT, metadata=meta_data_G2M)
[email protected]$Cell_Cycle_Diff <- [email protected]$Cell_cycle_score_S - [email protected]$Cell_cycle_score_G2M
} else {
message("+--- SKIPPING: SCRAN/Cyclone Cell Cycle Calculations ONLY WT Matrix ---+")
}
if(runcellcycle == 1){
message("+--- SCRAN/Cyclone Cell Cycle Calculations ONLY KO Matrix ---+")
matrix.KO.data <- [email protected]
matrix.KO.data$external_gene_name <- rownames(matrix.KO.data)
matrix.KO.data <- merge(matrix.KO.data, ensEMBL2id, by="external_gene_name" )
rownames(matrix.KO.data) <- matrix.KO.data$ensembl_gene_id
matrix.KO.data <- matrix.KO.data[, -grep("external_gene_name", colnames(matrix.KO.data))]
matrix.KO.data <- matrix.KO.data[, -grep("ensembl_gene_id", colnames(matrix.KO.data))]
matrix.KO.data <- data.matrix(matrix.KO.data, rownames.force = TRUE)
dim(matrix.KO.data)
numcells.raw <- ncol(matrix.su.KO@data)
message( paste0("Number of cells (processed)=", numcells.raw))
message("Number of cells from [email protected]")
cells <- colnames([email protected])
length(cells)
message("Number of rows from matrix.su.KO")
length( rownames(matrix.su.KO@data) )
message("+--- Cell cycle phase assignment ---+")
sce.annot <- SingleCellExperiment(list(counts=matrix.KO.data))
mm.pairs <- readRDS(system.file("exdata", "mouse_cycle_markers.rds", package="scran"))
assigned <- cyclone(sce.annot, pairs=mm.pairs)
message( table(assigned$phases) )
meta_data_CC <- data.frame("Cell_cycle"=assigned$phases)
rownames(meta_data_CC) <- cells
matrix.su.KO <- AddMetaData(matrix.su.KO, metadata=meta_data_CC)
meta_data_G1 <- data.frame("Cell_cycle_score_G1"=assigned$scores$G1)
rownames(meta_data_G1) <- cells
matrix.su.KO <- AddMetaData(matrix.su.KO, metadata=meta_data_G1)
meta_data_S <- data.frame("Cell_cycle_score_S"=assigned$scores$S)
rownames(meta_data_S) <- cells
matrix.su.KO <- AddMetaData(matrix.su.KO, metadata=meta_data_S)
meta_data_G2M <- data.frame("Cell_cycle_score_G2M"=assigned$scores$G2M)
rownames(meta_data_G2M) <- cells
matrix.su.KO <- AddMetaData(matrix.su.KO, metadata=meta_data_G2M)
[email protected]$Cell_Cycle_Diff <- [email protected]$Cell_cycle_score_S - [email protected]$Cell_cycle_score_G2M
}else{
message("+--- SKIPPING SCRAN/Cyclone Cell Cycle Calculations ONLY KO Matrix ---+")
}
message("+--- Look at MT genes (WT)---+")
mito.genes <- grep("^mt-", rownames(matrix.su.WT@data), value = T)
head(mito.genes)
head( colSums(expm1(matrix.su.WT@data[mito.genes, ])) )
percent.mito <- colSums(expm1(matrix.su.WT@data[mito.genes, ]))/colSums(expm1(matrix.su.WT@data))
head(percent.mito)
matrix.su.WT <- AddMetaData(matrix.su.WT, percent.mito, "percent.mito")
matrix.su.WT <- FilterCells(object = matrix.su.WT,
subset.names = c("nGene", "percent.mito"),
low.thresholds = c(genecut, -Inf), high.thresholds = c(2500, 0.05))
message("+--- Look at MT genes (KO)---+")
mito.genes <- grep("^mt-", rownames(matrix.su.KO@data), value = T)
percent.mito <- colSums(expm1(matrix.su.KO@data[mito.genes, ]))/colSums(expm1(matrix.su.KO@data))
matrix.su.KO <- AddMetaData(matrix.su.KO, percent.mito, "percent.mito")
matrix.su.KO <- FilterCells(object = matrix.su.KO,
subset.names = c("nGene", "percent.mito"),
low.thresholds = c(genecut, -Inf), high.thresholds = c(2500, 0.05))
message("+--- Summary of genes / cells in the matrix (WT) ---+")
numrows <- nrow(matrix.su.WT@data)
numcells <- ncol(matrix.su.WT@data)
message( paste("+--- Number of cells=", numcells, " Number of genes=", numrows, " ---+", sep=""))
if(numcells < 100){ message("+--- Below 100 cells after filtering ---+"); return(numcells)}
message("+--- nUMI Summary (WT) ---+")
summary( [email protected]$nUMI )
message("+--- nGene Summary (WT) ---+")
summary( [email protected]$nGene )
message("+--- Summary of genes / cells in the matrix (KO) ---+")
numrows <- nrow(matrix.su.KO@data)
numcells <- ncol(matrix.su.KO@data)
message( paste("+--- Number of cells=", numcells, " Number of genes=", numrows, " ---+", sep=""))
if(numcells < 100){ message("+--- Below 100 cells after filtering ---+"); return(numcells)}
message("+--- nUMI Summary (KO) ---+")
summary( [email protected]$nUMI )
message("+--- nGene Summary (KO) ---+")
summary( [email protected]$nGene )
message("+--- Normalise Data (WT) ---+")
matrix.su.WT <- NormalizeData(object = matrix.su.WT, normalization.method = "LogNormalize", scale.factor = 1e4)
message("+--- Scale Data (WT) ---+")
if(runcellcycle == 1){
matrix.su.WT <- ScaleData(object = matrix.su.WT, vars.to.regress = c("nUMI", "percent.mito", "Cell_Cycle_Diff"))
}else{
matrix.su.WT <- ScaleData(object = matrix.su.WT, vars.to.regress = c("nUMI", "percent.mito"))
}
message("+--- Find Variable genes (WT) ---+")
matrix.su.WT <- FindVariableGenes(object = matrix.su.WT, do.plot=FALSE,
mean.function = ExpMean,
dispersion.function = LogVMR,
x.low.cutoff = 0.0125, x.high.cutoff = 3, y.cutoff = 0.5)
message("+--- Normalise Data (KO) ---+")
matrix.su.KO <- NormalizeData(object = matrix.su.KO, normalization.method = "LogNormalize", scale.factor = 1e4)
message("+--- Find Variable genes (KO) ---+")
if(runcellcycle == 1){
matrix.su.KO <- ScaleData(object = matrix.su.KO, vars.to.regress = c("nUMI", "percent.mito", "Cell_Cycle_Diff"))
}else{
matrix.su.KO <- ScaleData(object = matrix.su.KO, vars.to.regress = c("nUMI", "percent.mito"))
}
message("+--- Find Variable genes (KO) ---+")
matrix.su.KO <- FindVariableGenes(object = matrix.su.KO, do.plot=FALSE,
mean.function = ExpMean,
dispersion.function = LogVMR,
x.low.cutoff = 0.0125, x.high.cutoff = 3, y.cutoff = 0.5)
message("+--- Find union of variable genes ---+")
hvg.matrix.su.WT <- rownames(x = head(x = [email protected], n = 1000))
hvg.matrix.su.KO <- rownames(x = head(x = [email protected], n = 1000))
hvg.union <- union(x = hvg.matrix.su.WT, y = hvg.matrix.su.KO)
head( hvg.union)
message( length(hvg.union) )
[email protected][, "protocol"] <- "WT"
[email protected][, "protocol"] <- "KO"
message("+--- Method 1 : PCA with WT variable genes ---+")
message("+--- Method 1 : Run PCA ---+")
matrix.su.WTvar <- RunPCA(object = matrix.su.WTvar, pc.genes = hvg.matrix.su.WT,
do.print = TRUE, pcs.print = 1:5, genes.print = 5)
matrix.su.WTvar <- ProjectPCA(matrix.su.WTvar)
message("+--- Method 1 : Find Clusters ---+")
matrix.su.WTvar <- FindClusters(object=matrix.su.WTvar, dims.use = 1:10, reduction.type = "pca",
resolution = 0, print.output = FALSE, save.SNN = TRUE)
for(res in c( 0.2, 0.4, 0.6, 0.8, 1.0 ))
{
message(paste0("+--- Method 1 : Finding Clusters at ", res, " resolution ---+"))
matrix.su.WTvar <- FindClusters(object=matrix.su.WTvar, reduction.type = "pca", resolution = res, print.output = FALSE)
}
message("+--- Method 1 : tSNE ---+")
matrix.su.WTvar <- RunTSNE(matrix.su.WTvar, dims.use = 1:10, do.fast = T)
matrix.WTvar.tsne <- as.data.frame( GetCellEmbeddings(object = matrix.su.WTvar,
reduction.type = "tsne", dims.use = 1:2) )
matrix.WTvar.tsne$Experiment <- gsub("\\.[0-9]$", "", rownames(matrix.WTvar.tsne))
matrix.WTvar.tsne$Experiment <- gsub("..{12}$","", matrix.WTvar.tsne$Experiment)
matrix.WTvar.tsne$Experiment <- gsub("SLX......","", matrix.WTvar.tsne$Experiment)
matrix.WTvar.tsne$Experiment <- gsub("N701|N704|N705|N706","WT", matrix.WTvar.tsne$Experiment)
matrix.WTvar.tsne$Experiment <- gsub("N702|N703|N707", "KO", matrix.WTvar.tsne$Experiment)
matrix.WTvar.tsne$Cluster <- [email protected]$res.0.6
message("+--- Method 1 : Save ROBJs ---+")
save(matrix.su.WTvar, file=paste0(slxID, ".matrix.su.WTvar.genecut.", genecut, ".res.", resolution, ".reg.", regression, ".robj"))
save(matrix.WTvar.tsne, file=paste0(slxID, ".matrix.WTvar.tsne.genecut.", genecut, ".res.", resolution, ".reg.", regression, ".robj"))
pdf(paste0(slxID, ".", "matrix.su.WTvar.tSNEs.colbyexpt.", regression, ".pdf") ,width=15,height=15, onefile=FALSE)
par(bg=NA)
ggplot(data=as.data.frame(matrix.WTvar.tsne), aes(x=tSNE_1, y=tSNE_2, colour=Experiment)) +
geom_point(alpha=0.5, size=2) +
ggtitle(paste0(slxID, " matrix.WTvar.tsne res=", resolution, " genecut=", genecut, "regression=", regression))
dev.off()
pdf(paste0(slxID, ".", "matrix.su.WTvar.tSNEs.colbyclust.", regression, ".pdf") ,width=15,height=15, onefile=FALSE)
par(bg=NA)
ggplot(data=as.data.frame(matrix.WTvar.tsne), aes(x=tSNE_1, y=tSNE_2, colour=Cluster)) +
geom_point(alpha=0.5, size=2) +
ggtitle(paste0(slxID, " matrix.WTvar.tsne res=", resolution, " genecut=", genecut, "regression=", regression))
dev.off()
message("+--- END OF SCRIPT ---+")