单细胞分析Seurat包的学习

Satija Lab https://satijalab.org/seurat/v3.1/pbmc3k_tutorial.html

按照Seurat的说明走了一遍流程：

流程主要包括：
QC and data filtration, calculation of high-variance genes, dimensional reduction, graph-based clustering, and the identification of cluster markers.

首先，升级一下Seurat(3.02升级到3.13)，不升级的话后边用umap分群的时会报错。install.packages('Seurat')安装失败，最后是本地安装成功。
和之前的Seurat2.0的版本相比，Seurat3.0变化很大；可以参考https://satijalab.org/seurat/v3.0/sctransform_vignette.html

也可以参考刘小泽的简书比较：单细胞Seurat包升级，换汤不换药 - 简书 https://www.jianshu.com/p/ac4621f4688a

单细胞Seurat包升级之2,700 PBMCs分析（上） - 简书 https://www.jianshu.com/p/beca2faf94f7

单细胞Seurat包升级之2,700 PBMCs分析（下） - 简书 https://www.jianshu.com/p/b46b6b6d344f

Seurat的分析流程如下：

pbmc.counts <- Read10X(data.dir = "~/Downloads/pbmc3k/filtered_gene_bc_matrices/hg19/")
pbmc <- CreateSeuratObject(counts = pbmc.counts)
pbmc <- NormalizeData(object = pbmc)
pbmc <- FindVariableFeatures(object = pbmc)
pbmc <- ScaleData(object = pbmc)
pbmc <- RunPCA(object = pbmc)
pbmc <- FindNeighbors(object = pbmc)
pbmc <- FindClusters(object = pbmc)
pbmc <- RunTSNE(object = pbmc)
DimPlot(object = pbmc, reduction = "tsne")