Load the PBMC dataset

pbmc.data0 <- read.csv("GSE115746_cells_exon_counts.csv",header=T,row.names=1)
pbmc.data0 $celltype=read.csv("GSE71585_Clustering_Results.csv/GSE71585_Clustering_Results.csv") pbmc.data1 <- read.csv("20180410-BY3_1kgenes/cell_barcode_count.csv",header=T,row.names=1) pbmc.data1$ celltype=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)

所有数据(reference和query)预处理和找高变基因

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 数据预处理

1.2 找高变基因

1.3 找anchor

1.4 整合

1.5 可视化

2.整合query:FindIntegration

query.list <- pbmc.list[c(2,3)]

2.1 数据预处理

2.2 找高变基因

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 可视化

3.投影，信息转换

3.1 找reference和query之间的anchor

pbmc.anchors <- FindTransferAnchors(reference = pbmc.reference, query = pbmc.integrated,
dims = 1:30)

3.2 根据reference对query进行细胞分类

predictions <- TransferData(anchorset = pbmc.anchors, refdata = pbmc.reference$celltype,
dims = 1:30)
pancreas.query <- AddMetaData(pbmc.integrated, metadata = predictions)