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scRNA-seq.R
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scRNA-seq.R
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library(Seurat)
library(Matrix)
library(ggplot2)
library(utils)
library(dplyr)
library(qusage)
library(cowplot)
# import the data as Seurat S4 datasets
load_matrix <- function(file) {
file_name <- paste0('../rna-seq/',file, "/filtered_feature_bc_matrix/")
print(file_name)
barcode.path <- paste0(file_name, "barcodes.tsv.gz")
features.path <- paste0(file_name,"features.tsv.gz")
matrix.path <- paste0(file_name,"matrix.mtx.gz")
mat <- readMM(file = matrix.path)
feature.names <- read.delim(features.path,
header = FALSE,
stringsAsFactors = FALSE
)
barcode.names <- read.delim(barcode.path,
header = FALSE,
stringsAsFactors = FALSE
)
colnames(mat) <- barcode.names$V1
rownames(mat) <- feature.names$V2
# setup data
mat <- CreateSeuratObject(
counts = mat,
project = "Adipocyte",
min.cells = 3,
min.features = 200
)
mat$phenotype <- file # set the phenotype
return(mat)
}
# preprocess
down_stream <- function(data) {
data[["percent.mt"]] <- PercentageFeatureSet(data, pattern = "^MT-")
data <- subset(
data,
subset = nFeature_RNA > 500 & nFeature_RNA < 4000 & percent.mt < 40
)
data <- NormalizeData(
data,
normalization.method = "LogNormalize",
scale.factor = 10000
)
data <- NormalizeData(data)
data <- FindVariableFeatures(
data,
selection.method = "vst",
nfeatures = 2000
)
all.genes <- rownames(data)
data <- ScaleData(data, features = all.genes) # scale the data
data <- RunPCA(data, features = VariableFeatures(object = data))
data <- JackStraw(data, num.replicate = 100)
data <- ScoreJackStraw(data, dims = 1:20)
return(data)
}
# clustering
cluster_vis <- function(data) {
data <- FindNeighbors(data, dims = 1:20)
data <- FindClusters(data, resolution = 0.7)
data <- RunUMAP(data, dims = 1:20)
return(data)
}
# plot the experssion of 'Adipoq', 'Cdh5', and 'Pdgfra'
analysis <- function(data) {
data <- down_stream(data)
data <- cluster_vis(data)
VlnPlot(data, c('Adipoq', 'Cdh5', 'Pdgfra'))
return(data)
}
# reperform the subset data
reperform <- function (data, name) {
data <- GetAssay(data)@counts
data <- CreateSeuratObject(
counts = data,
project = "Adipocyte",
min.cells = 0,
min.features = 0
)
data$phenotype <- name
data <- down_stream(data)
data <- cluster_vis(data)
return(data)
}
# merge the subset
merge_downstream <- function (data1, data2) {
data <- merge(data1, data2)
data <- down_stream(data)
data <- cluster_vis(data)
return(data)
}
# find the markers between GFPpos and GFPneg
marker_table <- function(data, top_gene) {
Idents(data) <- data$phenotype
markers <- FindAllMarkers(
data,
only.pos = TRUE,
min.pct = 0.2,
logfc.threshold = 0.25
)
return(markers)
}
# for four datasets
if (getOption('run.main', default=TRUE)) {
# read data from 4 datasets
pb <- txtProgressBar(style=3)
file_name <- c('iBATneg', 'iBATpos', 'iWATneg', 'iWATpos')
for (i in 1:length(file_name)) {
assign(file_name[i], load_matrix(file_name[i]))
setTxtProgressBar(pb, i/length(file_name))
}
close(pb)
# for the iBATneg data
iBATneg <- analysis(iBATneg)
VlnPlot(iBATneg, c('Adipoq', 'Cdh5', 'Pdgfra'))
iBATneg_subset <- subset(iBATneg, idents = c(0, 4, 8)) # select only the 0, 4 and 8
# for the iBATpos data
iBATpos <- analysis(iBATpos)
VlnPlot(iBATpos,c('Adipoq', 'Cdh5', 'Pdgfra'))
FeaturePlot(iBATpos, features = c("Adipoq"), cols = c("grey", "red"), pt.size = 1)
DimPlot(iBATpos, label = T, pt.size = 1, label.size = 6)
iBATpos_subset <- subset(iBATpos, idents = c(0, 1, 2, 3, 4, 5, 7)) # select only the 0, 1, 2, 3, 4, 5 and 7
# for the iWATneg data
iWATneg <- analysis(iWATneg)
VlnPlot(iWATneg,c('Adipoq', 'Cdh5', 'Pdgfra'))
DimPlot(iWATneg, label = T, pt.size = 1, label.size = 6)
FeaturePlot(iWATneg, features = c("Adipoq"), cols = c("grey", "red"), pt.size = 1)
FeaturePlot(iWATneg, features = c("Pdgfra"), cols = c("grey", "red"), pt.size = 1)
iWATneg_subset <- subset(iWATneg, idents = c(4, 6)) # select only the 4 and 6
# for the iWATpos data
iWATpos <- analysis(iWATpos)
VlnPlot(iWATpos,c('Adipoq', 'Cdh5', 'Pdgfra'))
iWATpos_subset <- subset(iWATpos, idents = c(1, 2, 6, 8)) # select only the 1, 2, 6 and 8
# reanalysis the subset data
iBATneg_subset <- reperform(iBATneg_subset, 'iBATneg')
iBATpos_subset <- reperform(iBATpos_subset, 'iBATpos')
iWATneg_subset <- reperform(iWATneg_subset, 'iWATneg')
iWATpos_subset <- reperform(iWATpos_subset, 'iWATpos')
# merge the data
iBAT <- merge_downstream(iBATneg_subset, iBATpos_subset) # for iBAT cells
iWAT <- merge_downstream(iWATneg_subset, iWATpos_subset) # for iWAT cells
# save the UMAP figure for iBAT and iWAT
pdf('/path/to/iBAT_umap.pdf')
DimPlot(iBAT, group.by = 'phenotype', pt.size = 1) + NoLegend()
dev.off()
pdf('/path/to/iWAT_umap.pdf')
DimPlot(iWAT, group.by = 'phenotype', pt.size = 1) + NoLegend()
dev.off()
# Heat map
iBAT_markers <- marker_table(iBAT)
top30_B <- iBAT_markers %>% group_by(cluster) %>% top_n(n = 30, wt = avg_logFC)
DoHeatmap(iBAT, features = top30_B$gene, group.by = "phenotype")
}