rm(list = ls())
library(dplyr)
library(Seurat)
setwd("D:/PROJECT/IMC/integration")
Load the PBMC dataset
pbmc.data0 <- read.csv("GSE115746_cells_exon_counts.csv",header=T,row.names=1)
pbmc.data0celltype=read.csv("20180410-BY3_1kgenes/class_labels.csv")
pbmc.data2 <- read.csv("20180505_BY3_1kgenes/cell_barcode_count.csv",header=T,row.names=1)
pbmc.data2$celltype=read.csv("20180505_BY3_1kgenes/class_labels.csv")
pbmc.list=list(pbmc.data0,pbmc.data1,pbmc.data2)
所有數(shù)據(jù)(reference和query)預(yù)處理和找高變基因
for (i in 1:length(pbmc.list)) {
pbmc.list[[i]] <- NormalizeData(pancreas.list[[i]], verbose = FALSE)
pbmc.list[[i]] <- FindVariableFeatures(pancreas.list[[i]], selection.method = "vst",
nfeatures = 1020, verbose = FALSE)
}
1.整合reference:FindIntegration
pbmc.reference <- pbmc.list[1]
1.1 數(shù)據(jù)預(yù)處理
1.2 找高變基因
1.3 找anchor
1.4 整合
1.5 可視化
2.整合query:FindIntegration
query.list <- pbmc.list[c(2,3)]
2.1 數(shù)據(jù)預(yù)處理
2.2 找高變基因
2.3 找anchor
pbmc.anchors <- FindIntegrationAnchors(object.list = query.list, dims = 1:30)
2.4 整合z
pbmc.integrated <- IntegrateData(anchorset = pbmc.anchors, dims = 1:30)
pbmc.integrated
pbmc.integrated@assays$RNA
2.5 可視化
3.投影，信息轉(zhuǎn)換
3.1 找reference和query之間的anchor
pbmc.anchors <- FindTransferAnchors(reference = pbmc.reference, query = pbmc.integrated,
dims = 1:30)
3.2 根據(jù)reference對query進行細(xì)胞分類
predictions <- TransferData(anchorset = pbmc.anchors, refdata = pbmc.reference$celltype,
dims = 1:30)
pancreas.query <- AddMetaData(pbmc.integrated, metadata = predictions)
