祖传的单个10x样本的seurat标准代码

最近有粉丝反映说我前年的单细胞转录组课程视频及代码被人拿到咸鱼上面在售卖，我···

其实单细胞领域进展太快，我那些课程内容关于R包相关的代码基本上过时了，因为R语言本身都经历了一个超级大的变革！考虑到不能把粉丝带歪，我早就全部公开了系列视频课程。还创立了《单细胞天地》这个公众号 ：

全网第一个单细胞课程（免费基础课程）

• 免费学习地址在B站：https://www.bilibili.com/video/av38741055 ，欢迎提问弹幕交流！
• 务必听课后完成结业考核20题：https://mp.weixin.qq.com/s/lpoHhZqi-_ASUaIfpnX96w
• 课程配套资料文档在：https://docs.qq.com/doc/DT2NwV0Fab3JBRUx0

技能树出品的第二个单细胞课程（进阶课程，仍然免费）

• 详情请自行阅读介绍 https://mp.weixin.qq.com/s/bLfO-8ri_SNUepGs4UwRQw
• 本课程长期答疑文档，https://docs.qq.com/doc/DT0FxbEpHYU5ZVlpu

因为课程涉及到知识点太多，所以我拆分成为了5个子课程，欢迎B站提问弹幕交流！全部链接是：

• 「生信技能树」单细胞进阶数据处理之文献导读，链接是：https://www.bilibili.com/video/BV17f4y1R7N8
• 「生信技能树」使用10X单细胞转录组数据探索免疫治疗，链接是：https://www.bilibili.com/video/BV1xD4y1S74P
• 「生信技能树」单细胞基因组数据拷贝数变异分析流程，链接是：https://www.bilibili.com/video/BV1Yf4y1R75R
• 「生信技能树」云服务器处理单细胞转录组数据，链接是：https://www.bilibili.com/video/BV154411Z7DU
• 「生信技能树」使用Smart-seq2单细胞转录组数据探索小鼠性腺发育，链接是：https://www.bilibili.com/video/BV1454y1q77Z

也希望可以帮助到你。

这里做一个统一的代码更新

复制粘贴就可以使用的代码哦，单个10x样本的seurat标准代码如下：

### ---------------
###
### Create: Jianming Zeng
### Date: 2020-08-27 15:00:26
### Email: [e-mail]
### Blog: http://www.bio-info-trainee.com/
### Forum:  http://www.biotrainee.com/thread-1376-1-1.html
### CAFS/SUSTC/Eli Lilly/University of Macau
### Update Log: 2020-08-27  First version
###
### ---------------

rm(list=ls())
options(stringsAsFactors = F)
library(Seurat)
pro='S1'
# 搞清楚你的10x单细胞项目的cellranger输出文件夹哦
hp_sce <- CreateSeuratObject(Read10X('scRNAseq_10_s1/filtered_feature_bc_matrix/'),
                             pro)

hp_sce
rownames(hp_sce)[grepl('^mt-',rownames(hp_sce))]
rownames(hp_sce)[grepl('^Rp[sl]',rownames(hp_sce))]

hp_sce[["percent.mt"]] <- PercentageFeatureSet(hp_sce, pattern = "^mt-")
fivenum(hp_sce[["percent.mt"]][,1])
rb.genes <- rownames(hp_sce)[grep("^Rp[sl]",rownames(hp_sce))]
C<-GetAssayData(object = hp_sce, slot = "counts")
percent.ribo <- Matrix::colSums(C[rb.genes,])/Matrix::colSums(C)*100
hp_sce <- AddMetaData(hp_sce, percent.ribo, col.name = "percent.ribo")

plot1 <- FeatureScatter(hp_sce, feature1 = "nCount_RNA", feature2 = "percent.mt")
plot2 <- FeatureScatter(hp_sce, feature1 = "nCount_RNA", feature2 = "nFeature_RNA")
CombinePlots(plots = list(plot1, plot2))

VlnPlot(hp_sce, features = c("percent.ribo", "percent.mt"), ncol = 2)
VlnPlot(hp_sce, features = c("nFeature_RNA", "nCount_RNA"), ncol = 2)
VlnPlot(hp_sce, features = c("percent.ribo", "nCount_RNA"), ncol = 2)
hp_sce

hp_sce1 <- subset(hp_sce, subset = nFeature_RNA > 200 & nCount_RNA > 1000 & percent.mt < 20)
hp_sce1


sce=hp_sce1
sce
sce <- NormalizeData(sce, normalization.method =  "LogNormalize",
                     scale.factor = 10000)
GetAssay(sce,assay = "RNA")
sce <- FindVariableFeatures(sce,
                            selection.method = "vst", nfeatures = 2000)
# 步骤 ScaleData 的耗时取决于电脑系统配置（保守估计大于一分钟）
sce <- ScaleData(sce)
sce <- RunPCA(object = sce, pc.genes = VariableFeatures(sce))
DimHeatmap(sce, dims = 1:12, cells = 100, balanced = TRUE)
ElbowPlot(sce)
sce <- FindNeighbors(sce, dims = 1:15)
sce <- FindClusters(sce, resolution = 0.2)
table([e-mail]$RNA_snn_res.0.2)
sce <- FindClusters(sce, resolution = 0.8)
table([e-mail]$RNA_snn_res.0.8)


set.seed(123)
sce <- RunTSNE(object = sce, dims = 1:15, do.fast = TRUE)
DimPlot(sce,reduction = "tsne",label=T)
phe=data.frame(cell=rownames([e-mail]),
               res2=[e-mail]$RNA_snn_res.0.2,
               res8=[e-mail]$RNA_snn_res.0.8,
               cluster =[e-mail]$seurat_clusters)
head(phe)
table(phe$cluster)
tsne_pos=Embeddings(sce,'tsne')
DimPlot(sce,reduction = "tsne",label=T,split.by ='orig.ident')

head(phe)
table(phe$cluster)
head(tsne_pos)
dat=cbind(tsne_pos,phe)
save(dat,file=paste0(pro,'_for_tSNE.pos.Rdata'))
load(file=paste0(pro,'_for_tSNE.pos.Rdata'))
library(ggplot2)
p=ggplot(dat,aes(x=tSNE_1,y=tSNE_2,color=cluster))+geom_point(size=0.95)
p=p+stat_ellipse(data=dat,aes(x=tSNE_1,y=tSNE_2,fill=cluster,color=cluster),
                 geom = "polygon",alpha=0.2,level=0.9,type="t",linetype = 2,show.legend = F)+coord_fixed()
print(p)
theme= theme(panel.grid =element_blank()) +   ## 删去网格
  theme(panel.border = element_blank(),panel.background = element_blank()) +   ## 删去外层边框
  theme(axis.line = element_line(size=1, colour = "black"))
p=p+theme+guides(colour = guide_legend(override.aes = list(size=5)))
print(p)
ggplot2::ggsave(filename = paste0(pro,'_tsne_res0.2.pdf'))

plot1 <- FeatureScatter(sce, feature1 = "nCount_RNA", feature2 = "percent.mt")
plot2 <- FeatureScatter(sce, feature1 = "nCount_RNA", feature2 = "nFeature_RNA")
CombinePlots(plots = list(plot1, plot2))
ggplot2::ggsave(filename = paste0(pro,'_CombinePlots.pdf'))

VlnPlot(sce, features = c("percent.ribo", "percent.mt"), ncol = 2)
ggplot2::ggsave(filename = paste0(pro,'_mt-and-ribo.pdf'))
VlnPlot(sce, features = c("nFeature_RNA", "nCount_RNA"), ncol = 2)
ggplot2::ggsave(filename = paste0(pro,'_counts-and-feature.pdf'))
VlnPlot(sce, features = c("percent.ribo", "nCount_RNA"), ncol = 2)


table([e-mail]$seurat_clusters)

for( i in unique([e-mail]$seurat_clusters) ){
  markers_df <- FindMarkers(object = sce, ident.1 = i, min.pct = 0.25)
  print(x = head(markers_df))
  markers_genes =  rownames(head(x = markers_df, n = 5))
  VlnPlot(object = sce, features =markers_genes,log =T )
  ggsave(filename=paste0(pro,'_VlnPlot_subcluster_',i,'_sce.markers_heatmap.pdf'))
  FeaturePlot(object = sce, features=markers_genes )
  ggsave(filename=paste0(pro,'_FeaturePlot_subcluster_',i,'_sce.markers_heatmap.pdf'))
}

sce.markers <- FindAllMarkers(object = sce, only.pos = TRUE, min.pct = 0.25,
                              thresh.use = 0.25)

DT::datatable(sce.markers)
write.csv(sce.markers,file=paste0(pro,'_sce.markers.csv'))
library(dplyr)
top10 <- sce.markers %>% group_by(cluster) %>% top_n(10, avg_logFC)
DoHeatmap(sce,top10$gene,size=3)
ggsave(filename=paste0(pro,'_sce.markers_heatmap.pdf'))
# FeaturePlot( sce,  top10$gene )
# ggsave(filename=paste0(pro,'_sce.markers_FeaturePlot.pdf'),height = 49)


library(SingleR)
sce_for_SingleR <- GetAssayData(sce, slot="data")
mouseImmu <- ImmGenData()
pred.mouseImmu <- SingleR(test = sce_for_SingleR, ref = mouseImmu, labels = mouseImmu$label.main)

mouseRNA <- MouseRNAseqData()
pred.mouseRNA <- SingleR(test = sce_for_SingleR, ref = mouseRNA, labels = mouseRNA$label.fine )

cellType=data.frame(seurat=[e-mail]$seurat_clusters,
                    mouseImmu=pred.mouseImmu$labels,
                    mouseRNA=pred.mouseRNA$labels)
sort(table(cellType[,2]))
sort(table(cellType[,3]))
table(cellType[,2:3])
save(sce,cellType, file=paste0(pro,'_output.Rdata') )

rm(list=ls())
options(stringsAsFactors = F)
library(Seurat)
pro='S1'
library(SingleR)
load(file=paste0(pro,'_output.Rdata') )

cg=names(tail(sort(table(cellType[,2]))))
cellname=cellType[,2]
cellname[!cellname %in% cg ]='other'
table([e-mail]$seurat_clusters,cellname)
table(cellname)
# Epithelial cells, 0,2,5,7,8,12
# Endothelial cells, 10
# Fibroblasts , 1,3,6,9,14,15
# Macrophages , 4,13,16
# NKT , 11
# other ,4,11
# Stromal cells,
cl=[e-mail]$seurat_clusters
ps=ifelse(cl %in% c(0,2,5,7,8,12),'epi',
       ifelse(cl %in% c(1,3,6,9,14,15),'fibro',
              ifelse(cl %in% c(4,13,16),'macro',
                     ifelse(cl %in% c(10),'endo',
                            ifelse(cl %in% c(11),'NKT','other'
                            )))))
table(ps)
cellname=paste(cl,ps)
[e-mail]$cellname=cellname
DimPlot(sce,reduction = "tsne",label=T,group.by = 'cellname')
ggplot2::ggsave(filename = paste0(pro,'_tsne_res0.2_singleR_raw.pdf'))
dat$cluster=cellname
library(ggplot2)
p=ggplot(dat,aes(x=tSNE_1,y=tSNE_2,color=cluster))+geom_point(size=0.95)
p=p+stat_ellipse(data=dat,aes(x=tSNE_1,y=tSNE_2,fill=cluster,color=cluster),
                 geom = "polygon",alpha=0.2,level=0.9,type="t",linetype = 2,show.legend = F)+coord_fixed()
print(p)
theme= theme(panel.grid =element_blank()) +   ## 删去网格
  theme(panel.border = element_blank(),panel.background = element_blank()) +   ## 删去外层边框
  theme(axis.line = element_line(size=1, colour = "black"))
p=p+theme+guides(colour = guide_legend(override.aes = list(size=5)))
print(p)
ggplot2::ggsave(filename = paste0(pro,'_tsne_res0.2_singleR_pretty.pdf'))

这里面包括了我一直强调的单细胞基础10讲相关内容 ：

还有一些个性化汇总。

如果你需要的是CNS文章全部图表，发表级别的代码，那就必须看CNS文章自带的GitHub哈，比如：你要的rmarkdown文献图表复现全套代码来了（单细胞）

写到最后

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