umap突出显示两种不同的模型
umap突出显示两种不同的模型
提问于 2020-04-03 19:38:26
我正在尝试为来自人类样本和ptx样本的单细胞数据创建umap。我可以得到umap图，显示不同的集群，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)回答 1
Stack Overflow用户
发布于 2021-03-13 17:55:09
元数据中应该有一个列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) 绘图：
DimPlot(scData,group.by="orig.ident")
页面原文内容由Stack Overflow提供。腾讯云小微IT领域专用引擎提供翻译支持
原文链接：
https://stackoverflow.com/questions/61011028
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