/
test.R
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/
test.R
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library(Seurat)
library(tidyverse)
library(dplyr)
library(stringr)
aggr.data <- Read10X(data.dir = "aggr")
aggr <- CreateSeuratObject(counts = aggr.data, project = "aggr", min.cells = 3, min.features = 200)
aggr
timePoints <- sapply(colnames(aggr), function(x) unlist(strsplit(x, "\\-"))[2])
timePoints <-ifelse(timePoints == '1', 'Day_2',
ifelse(timePoints == '2', 'Day_4',
ifelse(timePoints == '3', 'Day_3', 'Day_0')))
table(timePoints)
aggr <- AddMetaData(object = aggr, metadata = timePoints, col.name = 'TimePoints')
table(aggr@meta.data$TimePoints)
aggr[["percent.mt"]] <- PercentageFeatureSet(aggr, pattern = "^mt-")
VlnPlot(aggr, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3, group.by = 'TimePoints')
#aggr <- subset(aggr, subset = nFeature_RNA > 1000 & nFeature_RNA < 8000 & percent.mt < 20)
#dim(aggr)
aggr <- NormalizeData(aggr, normalization.method = "LogNormalize", scale.factor = 10000)
all.genes <- rownames(aggr)
aggr <- ScaleData(aggr,features = all.genes)
aggr <- FindVariableFeatures(aggr, selection.method = "vst", nfeatures = 5000)
aggr <- RunPCA(aggr, features = VariableFeatures(object = aggr), verbose = FALSE)
aggr<- RunTSNE(aggr, dims = 1:20)
DimPlot(aggr, reduction = "tsne", label = TRUE, group.by = 'TimePoints')
FeaturePlot(aggr, features = c("Ddx4", "Gfra1", "Stra8", "Spo11", "Dmc1", "Spata22", "Meiob"), reduction = "tsne")