umap 突出显示两种不同的模型答案

【问题标题】：umap highlighting two different modelsumap 突出显示两种不同的模型
【发布时间】：2020-04-03 11:38:26
【问题描述】：

我正在尝试为来自人类样本和 ptx 样本的单细胞数据创建一个 umap。我可以得到显示不同集群的 umap 图，但我想显示 ptx 样本和人类样本的位置。

我的代码如下：

#create the Seurat object

OD_10K_HUMAN <- CreateSeuratObject(counts = HUMAN_OD_10K.data, min.cells = 0, project = "human")
SD_5K_HUMAN <- CreateSeuratObject(counts = HUMAN_SD_5K.data, min.cells = 0, project = "human")
BNL.5K <- CreateSeuratObject(counts = SD_BNL_5K.data, min.cells = 0, project = "ptx")
BNL.10K <- CreateSeuratObject(counts = OD_BNL_10K.data, min.cells = 0, project = "ptx")
BNM.10K <- CreateSeuratObject(counts = OD_BNM_10K.data, min.cells = 0, project = "ptx")
BNM.5K <- CreateSeuratObject(counts = SD_BNM_5K.data, min.cells = 0, project = "ptx")

#merge data
scData <- merge(BNL.10K, y = c(BNL.5K, BNM.10K, BNM.5K, SD_5K_HUMAN, OD_10K_HUMAN), add.cell.ids = c("A", "B", "C", "D", "E", "F"), project = "HTB2876")

mark the mito genes
mito.genes <- grep(pattern = "^MT-", x = rownames(x = scData), value = TRUE)
length(mito.genes)
scData[["percent.mt"]] <- PercentageFeatureSet(scData, pattern = "^MT-")

scData[["log_nCount_RNA"]] <- log2(scData[["nCount_RNA"]]+1)

# remove cells with <200 RNA molecules, or >6000 molecules, or >30% mito
scData <- subset(scData, subset = nFeature_RNA > 200 & nFeature_RNA < 6000 & percent.mt < 30)
scData <- NormalizeData(scData, normalization.method = "LogNormalize", scale.factor = 10000)
scData <- FindVariableFeatures(scData, selection.method = "vst", nfeatures = 2000)

all.genes <- rownames(scData)
scData <- ScaleData(scData, features = VariableFeatures(object = scData), vars.to.regress = c("nCount_RNA"))
scData <- RunPCA(scData, features = VariableFeatures(object = scData))
#DimPlot(scData, reduction = "pca")

numPC = 20
scData <- FindNeighbors(scData, dims = 1:numPC)
scData <- FindClusters(scData, resolution = 0.4)
scData <- RunUMAP(scData, dims = 1:numPC)

DimPlot(scData, reduction = "umap", label = TRUE)

【问题讨论】：

标签： r seurat runumap

【解决方案1】：

您的元数据中应该有一列orig.ident，所以假设我们像您一样合并数据：

library(Seurat)
library(magrittr)
data1 = CreateSeuratObject(counts = pbmc_small[["RNA"]]@counts[,1:40],project="human")
data2 = CreateSeuratObject(counts = pbmc_small[["RNA"]]@counts[,41:80],project="ptx")

scData <- merge(data1,data2)

运行 umap：

scData = scData %>% 
SCTransform() %>% 
RunPCA() %>% 
RunUMAP(dims=1:15)

剧情：

DimPlot(scData,group.by="orig.ident")

【讨论】：