🤗 Rliger | 超好用的单细胞测序数据合并（3'和5'数据合并）（三）

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发布于 2023-02-24 13:54:18

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发布于 2023-02-24 13:54:18

文章被收录于专栏：R语言及实用科研软件

1写在前面

之前我们介绍了常用的三种合并datasets的方法: 👇

Harmony;
rliger;
Seurat。本期我们继续介绍其中的rliger包，如何用于3'和5'数据的合并。🤒

2用到的包

rm(list = ls())
library(Seurat)
library(SeuratDisk)
library(SeuratWrappers)
library(patchwork)
library(harmony)
library(rliger)
library(RColorBrewer)
library(tidyverse)
library(reshape2)
library(ggsci)
library(ggstatsplot)

3示例数据

这里我们提供1个3’ PBMC dataset和1个5’ PBMC dataset。🥰

matrix_3p <- Read10X_h5("./3p_pbmc10k_filt.h5",use.names = T)
matrix_5p <- Read10X_h5("./5p_pbmc10k_filt.h5",use.names = T)$`Gene Expression`

srat_3p <- CreateSeuratObject(matrix_3p,project = "pbmc10k_3p")
srat_5p <- CreateSeuratObject(matrix_5p,project = "pbmc10k_5p")
srat_3p
srat_5p

Note! 5' datset中还有一个assay，即VDJ data。🤜

4初步合并

4.1 简单合并

这里我们先用merge将2个数据集简单合并在一起。（这里我们默认做过初步过滤了哈，具体的大家可以看一下第一期的教学。）😘

pbmc_liger  <- merge(srat_3p,srat_5p)

4.2 标准操作

我们在这里做一下Normalization，寻找高变基因等等标准操作。😁 Note! 这里需要跟大家说下，rlinger在ScaleData时没有将数据中心化，我们需要设置为F。🤨

pbmc_liger    <- NormalizeData(pbmc_liger)
pbmc_liger    <- FindVariableFeatures(pbmc_liger)
pbmc_liger    <- ScaleData(pbmc_liger, split.by = "orig.ident", do.center = F)

4.3 合并数据

pbmc_liger  <- RunOptimizeALS(pbmc_liger, k = 30, lambda = 5, split.by = "orig.ident") 
pbmc_liger  <- RunQuantileNorm(pbmc_liger, split.by = "orig.ident")

5降维与聚类

5.1 寻找clusters

pbmc_liger  <- FindNeighbors(pbmc_liger,reduction = "iNMF",k.param = 10,dims = 1:30)
pbmc_liger  <- FindClusters(pbmc_liger)

5.2 聚类可视化

pbmc_liger    <- RunUMAP(pbmc_liger, dims = 1:ncol(pbmc_liger[["iNMF"]]), 
                         reduction = "iNMF", verbose = F)

pbmc_liger    <- SetIdent(pbmc_liger,value = "orig.ident")

p1 <- DimPlot(pbmc_liger,reduction = "umap") + 
  scale_color_npg()+
  plot_annotation(title = "10k 3' PBMC and 10k 5' PBMC cells, after integration (LIGER)")

p2 <- DimPlot(pbmc_liger, reduction = "umap", group.by = "orig.ident", 
              pt.size = .1, split.by = 'orig.ident') + 
  scale_color_npg()+
  NoLegend()

p1 + p2

pbmc_liger <- SetIdent(pbmc_liger,value = "seurat_clusters")

ncluster <- length(unique(pbmc_liger[[]]$seurat_clusters))

mycol <- colorRampPalette(brewer.pal(8, "Set2"))(ncluster)

DimPlot(pbmc_liger,reduction = "umap",label = T,
        cols = mycol, repel = T) + 
  NoLegend()

5.3 具体查看及可视化

我们看下各个clusters在两个datasets各有多少细胞。🧐

count_table <- table(pbmc_liger@meta.data$seurat_clusters, pbmc_liger@meta.data$orig.ident)
count_table

#### 可视化
count_table %>% 
  as.data.frame() %>% 
  ggbarstats(x = Var2, 
             y = Var1,
             counts = Freq)+
  scale_fill_npg()