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Copy pathGrubman_EC_altas.R
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Grubman_EC_altas.R
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#Description: this R script provides the single cell data analysis for maunscript: A single-cell altas of entorhinal cortex from individuals with Alzheimer's disease revelas cell-type-specific gene expression regulation
#Dataset: GEO -- GSE138852
# dir_work = "" --provide a dir_work
setwd(dir_work)
dir_data = "../data"
library(Seurat)
library(dplyr)
library(Matrix)
library(BRETIGEA)
library(rray)
library(ggplot2)
library(patchwork)
library(grid)
library(cowplot)
dir_counts = paste(dir_data, "GSE138852_counts.csv", sep = '/')
raw_counts<-read.table(dir_counts,sep=',', header = T, row.names = 1)
ad_samples <- grep(pattern = "AD", x = colnames(raw_counts), value = TRUE)
ad_raw_counts = raw_counts[, ad_samples]
ct_samples <- grep(pattern = "Ct", x = colnames(raw_counts), value = TRUE)
ct_raw_counts = raw_counts[, ct_samples]
ad_obj <- CreateSeuratObject(counts = ad_raw_counts,project = "AD")
ad_obj$genotype <- "AD"
ct_obj <- CreateSeuratObject(counts = ct_raw_counts,project = "CT")
ct_obj$genotype <- "CONTROL"
data <- merge(x = ad_obj, y = ct_obj, add.cell.ids = c("AD", "CT"), project = "JF_PROJ")
rm(raw_counts)
rm(ad_raw_counts)
rm(ad_obj)
rm(ct_raw_counts)
rm(ct_obj)
data <- NormalizeData(object = data, normalization.method = "LogNormalize",
scale.factor = 10000)
data <- FindVariableFeatures(data, selection.method = "mean.var.plot", mean.cutoff = c(0.0125, 3), dispersion.cutoff = c(0.5, Inf))
data <- ScaleData(data)
data <- RunPCA(data, features = VariableFeatures(object = data))
data <- FindNeighbors(data, dims = 1:19)
data <- FindClusters(data, resolution = 0.8)
data <- RunUMAP(object = data, dims = 1:19)
UMAPPlot(object = data, label = T)
# dir_plot = c('../plots/heatmap.png')
# png(dir_plot, width = 15, height = 6, units = 'in', res = 300)
# feature_genes = c("GRIN1", "SYT1", "RBFOX3", "SNAP25", "GRIN2A", "SLC17A7", "SATB2", "GAD1", "GAD2", "SST", "NPY", "PLP1", "MBP", "CLDN11", "MOG", "CAMK2A", "CCK", "ATP1A1", "PDE10A", "TAC1", "PENK", "SDK2", "COL5A1", "NPTX1", "PDE1A", "ETL4","SLC1A2","GJA1","AQP4","HEXB","CSF1R","C1QA","P2RY12","PDGFRA","VCAN","CSPG4","OLIG1","VTN","FLT1","CLDN5")
# DoHeatmap(data, features = feature_genes, size = 3, slot = "data")
# dev.off()
all_nuclei = colnames(data)
cluster3_nuclei = all_nuclei[which(data$seurat_clusters == 3)]
cluster5_nuclei = all_nuclei[which(data$seurat_clusters == 5)]
astrocyte_nuclei = c(cluster3_nuclei, cluster5_nuclei)
astrocyte_data = data[, astrocyte_nuclei]
#====subcluster astrocyte===#
astrocyte_data <- FindVariableFeatures(astrocyte_data, selection.method = "vst", nfeatures = 2000)
astrocyte_data <- ScaleData(astrocyte_data)
astrocyte_data <- RunPCA(astrocyte_data)
# astrocyte_data <- JackStraw(astrocyte_data, num.replicate = 50, dims = 40)
#
# astrocyte_data <- ScoreJackStraw(astrocyte_data, dims = 1:40)
#
# JackStrawPlot(astrocyte_data, dims = 1:40)
#
astrocyte_data <- FindNeighbors(astrocyte_data, dims = 1:10)
astrocyte_data <- FindClusters(astrocyte_data, resolution = 0.1)
astrocyte_data <- RunTSNE(object = astrocyte_data, dims = 1:10)
TSNEPlot(object = astrocyte_data, label = TRUE)
dir_plot = c('../plots/astrocyte_subcluster.pdf')
pdf(dir_plot, width = 9, height = 9)
DimPlot(astrocyte_data, reduction = "tsne", label = FALSE, pt.size = 1.5) + scale_color_manual(values=c("#4682B4", "#3CB371", "#DC143C", "#FFA500", "#BA55D3")) + NoLegend() +
theme(axis.title.x = element_text(size=22), axis.title.y = element_text(size=22), axis.text.x = element_text(size=22), axis.text.y = element_text(size=22), legend.text=element_text(size=22))
dev.off()
all.genes = rownames(astrocyte_data)
cluster_markers = FindMarkers(astrocyte_data, ident.1 = 1, test.use = "MAST", features = all.genes, logfc.threshold = 0.0, min.pct = 0.000000001)
dir_micro_markers = c('../result/astrocyte_subcluster1_markers.txt')
write.table(cluster_markers, dir_micro_markers, sep = '\t', row.names = T, col.names = T)
#=====================stacked violin plot==========================#
# https://divingintogeneticsandgenomics.rbind.io/post/stacked-violin-plot-for-visualizing-single-cell-data-in-seurat/
## plot.margin to adjust the white space between each plot.
## ... pass any arguments to VlnPlot in Seurat
modify_vlnplot<- function(obj,
feature,
pt.size = 0.1,
plot.margin = unit(c(-0.05, 0, -0.05, 0), "cm"),
...) {
p<- VlnPlot(obj, features = feature, pt.size = pt.size, ... ) + scale_fill_manual(values=c("#4682B4", "#3CB371", "#DC143C", "#FFA500", "#BA55D3")) +
xlab("") + ylab("") + ggtitle(feature) +
theme(legend.position = "none",
plot.title = element_text(size = 22),
axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
axis.title.y = element_text(size = 20, angle = 0),
axis.text.y = element_text(size = 20),
plot.margin = plot.margin )
return(p)
}
## extract the max value of the y axis
extract_max<- function(p){
ymax<- max(ggplot_build(p)$layout$panel_scales_y[[1]]$range$range)
return(ceiling(ymax))
}
## main function
StackedVlnPlot<- function(obj, features,
pt.size = 0.2,
plot.margin = unit(c(-0.05, 0, -0.05, 0), "cm"),
...) {
plot_list<- purrr::map(features, function(x) modify_vlnplot(obj = obj,feature = x, ...))
# Add back x-axis title to bottom plot. patchwork is going to support this?
# plot_list[[length(plot_list)]]<- plot_list[[length(plot_list)]] +
# theme(axis.text.x=element_text(size = 20), axis.ticks.x = element_line())
plot_list[[length(plot_list)]]<- plot_list[[length(plot_list)]] +
theme(axis.text.x=element_blank(), axis.ticks.x = element_line())
# change the y-axis tick to only max value
ymaxs<- purrr::map_dbl(plot_list, extract_max)
plot_list<- purrr::map2(plot_list, ymaxs, function(x,y) x +
scale_y_continuous(limits = c(0, y)) +
expand_limits(y = y))
p<- patchwork::wrap_plots(plotlist = plot_list, ncol = 2)
return(p)
}
# features = c("GLUL", "NRXN1", "CADM2", "PTN", "GPC5")
features = c("GFAP", "CD44", "HSPB1", "TNC", "SLC1A2", "SLC1A3", "GLUL", "NRXN1", "CADM2", "PTN", "GPC5")
dir_plot = '../plots/stacked_vlnplot.png'
png(dir_plot, width = 12, height = 14, units = 'in', res = 300)
StackedVlnPlot(obj = astrocyte_data, features = features)
dev.off()