Last updated on March 19, 2024 pm
安装补充包
获取非负表达矩阵
使用《使用metacell进行分群聚类》中的数据
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| sce <- readRDS('SRX8890106.rds') sce@meta.data <- readRDS('SRX8890106_meta.rds')
sce <- subset(sce, seurat_clusters == 6 & DF.classifications_0.25_0.04_416 == 'Singlet')
sce <- Seurat::SCTransform(sce, vst.flavor = "v2", assay = 'RNA', vars.to.regress = c("CC.Difference", "percent.mt", "percent.rp"), verbose = F)
DefaultAssay(sce) <- 'RNA' sce <- Seurat::NormalizeData(sce) sce <- Seurat::ScaleData(sce, do.center = F,
features = Seurat::VariableFeatures(sce, assay = 'SCT')) vm <- sce[[Seurat::DefaultAssay(sce)]]@scale.data
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NMF分解聚类
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| saveRDS(vm, 'vm.rds') vm <- readRDS('vm.rds') require(NMF) res <- NMF::nmf(vm, 2:7, method = "snmf/r", seed='ica') plot(res)
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| require(NMF) res <- NMF::nmf(vm, 4, method = "snmf/r", seed = 'ica') DefaultAssay(sce) <- 'SCT' sce <- Seurat::RunPCA(sce, assay="SCT", verbose = FALSE) sce@reductions$nmf <- sce@reductions$pca sce@reductions$nmf@cell.embeddings <- t(coef(res)) sce@reductions$nmf@feature.loadings <- basis(res) sce <- RunUMAP(sce, reduction = 'nmf', dims = 1:4) group <- predict(res) sce$nmf_group <- group[colnames(sce)] options(repr.plot.width = 6, repr.plot.height = 6) DimPlot(sce, reduction = "umap", label = T, repel = T, group.by = c('nmf_group'))
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![](https://img.limour.top/2023/09/01/64f1af1ac9dde.webp)
提取signatures
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| coefmap(res) consensusmap(res) df <- extractFeatures(res, 20L) df <- lapply(df, function(x){rownames(res)[x]}) df <- as.data.frame(do.call("rbind", df)) df
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【迁移】基于NMF分解的降维聚类
https://hexo.limour.top/ji-yu-NMF-fen-jie-de-jiang-wei-ju-lei