画出像烟花一样的单细胞umap图，原因竟然是？

生信技能树

发布于 2025-02-05 13:39:07

10200

代码可运行

文章被收录于专栏：生信技能树生信技能树

运行总次数：0

代码可运行

我们生信马拉松培训班的最后一周是单细胞学习，在上完这周的课后，有个学员做了一个GEO数据集的单细胞分析，但是他画出来的umap图像是为了庆祝新年，像烟花一样绚烂，如下：

学员把他用的数据和代码打包发了出来，下面就来看看是怎么回事！激动地搓搓手，这么有意思的umap图可不多见：

数据介绍：GSE125527

GSE125527数据：https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE125527。

样本为健康状态和溃疡性结肠炎（UC）中胃肠道黏膜和外周免疫系统的组织，文献中的结果如下：

简单分析一下

1、数据预处理

学院给出的数据如下：共15个样本

GSM3576396_HC-11.tsv.gz
GSM3576397_HC-12.tsv.gz
GSM3576398_HC-13.tsv.gz
GSM3576399_UC-12.tsv.gz
GSM3576400_UC-13.tsv.gz
GSM3576401_UC-14.tsv.gz
GSM3576402_UC-15.tsv.gz
GSM3576403_UC-16.tsv.gz
GSM3576404_UC-17.tsv.gz
GSM3576405_UC-18.tsv.gz
GSM3576406_HC-14.tsv.gz
GSM3576407_HC-15.tsv.gz
GSM3576408_HC-16.tsv.gz
GSM3576409_HC-17.tsv.gz
GSM3576410_HC-18.tsv.gz

批量读取并创建Seuart对象：

###
### Create: Jianming Zeng
### Date:  2023-12-31  
### Email: jmzeng1314@163.com
### 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: 2023-12-31   First version 
### Update Log: 2024-12-09   by juan zhang (492482942@qq.com)
### 

rm(list=ls())
options(stringsAsFactors = F)
library(ggsci)
library(dplyr) 
library(future)
library(Seurat)
library(clustree)
library(cowplot)
library(data.table)
library(ggplot2)
library(patchwork)
library(stringr)
library(qs)
library(Matrix)
getwd()

###### step1: 导入数据 ######   
samples <- list.files("GSE125527_RAW/")
samples 

sceList <- lapply(samples, function(pro){ 
  #pro <- samples[1]
  print(pro)
  ct <- fread(file.path("GSE125527_RAW/" ,pro),data.table = F)
  ct[1:4,1:4]
  rownames(ct) <- ct[,1]
  ct<- ct[,-1]
  ct <- t(ct)
  ct[1:4,1:4]
  sce <- CreateSeuratObject(counts=ct , project = gsub(".tsv.gz","",pro))
  return(sce)
}) 

sce.all <- merge(x=sceList[[1]], y=sceList[ -1 ])
sce.all <- JoinLayers(sce.all)
sce.all

names(sce.all@assays$RNA@layers)
dim(sce.all[["RNA"]]$counts )
sce.all[["RNA"]]$counts[1:5,1:5]

as.data.frame(sce.all@assays$RNA$counts[1:10, 1:5])
head(sce.all@meta.data, 10)
table(sce.all$orig.ident) 

temp <- str_split(sce.all$orig.ident, "_", n=2, simplify = T)
head(temp)

# add group and sample
sce.all$sample1 <- temp[,1]
sce.all$sample2 <- temp[,2]
sce.all$group <- str_split(temp[,2], pattern ="-" ,n = 2,simplify = T)[,1]

head(sce.all@meta.data)
table(sce.all$sample1)
table(sce.all$sample2)
table(sce.all$group,sce.all$sample1)
table(sce.all$orig.ident)

sce.all$orig.ident <- sce.all$sample2


library(qs)
qsave(sce.all, file="GSE125527/sce.all.qs")

sce.all

共得到：32154个细胞，10105个基因。

> sce.all
An object of class Seurat 
10105 features across 32154 samples within 1 assay 
Active assay: RNA (10105 features, 0 variable features)
 1 layer present: counts

2、简单质控

###### step2: QC质控 ######
dir.create("./1-QC")

# 1.计算线粒体基因比例
mito_genes <- grep("^MT-", rownames(sce.all),ignore.case = T, value = T) 
# 可能是13个线粒体基因
print(mito_genes)
sce.all <- PercentageFeatureSet(sce.all, features = mito_genes, col.name = "percent_mito")
fivenum(sce.all@meta.data$percent_mito)

# 2.计算核糖体基因比例
ribo_genes <- grep("^Rp[sl]", rownames(sce.all),ignore.case = T, value = T)
sce.all <- PercentageFeatureSet(sce.all,  features = ribo_genes, col.name = "percent_ribo")
fivenum(sce.all@meta.data$percent_ribo)

# 3.计算红血细胞基因比例
Hb_genes <- grep("^Hb[^(p)]", rownames(sce.all),ignore.case = T,value = T)
print(Hb_genes)
sce.all <- PercentageFeatureSet(sce.all, features=Hb_genes, col.name="percent_hb")
fivenum(sce.all@meta.data$percent_hb)

# 可视化细胞的上述比例情况
# pic1
p1 <- VlnPlot(sce.all, group.by = "orig.ident", features = c("nFeature_RNA", "nCount_RNA"), 
              pt.size = 0.02, ncol = 2) + NoLegend()
w <- length(unique(sce.all$orig.ident))/3+5;w
ggsave(filename="1-QC/Vlnplot1.pdf",plot=p1,width = w,height = 5)

# pic2
p2 <- VlnPlot(sce.all, group.by = "orig.ident", features = c("percent_mito", "percent_ribo", "percent_hb"), 
              pt.size = 0.0, ncol = 3) +  NoLegend()
w <- length(unique(sce.all$orig.ident))/2+5;w
ggsave(filename="1-QC/Vlnplot2.pdf",plot=p2,width = w,height = 5)

## 根据上述指标，过滤低质量细胞/基因
# 过滤指标1:最少表达基因数的细胞&最少表达细胞数的基因
# 一般来说，在CreateSeuratObject的时候已经是进行了这个过滤操作
# 如果后期看到了自己的单细胞降维聚类分群结果很诡异，就可以回过头来看质量控制环节
# 先走默认流程即可
dim(sce.all)

# 过滤细胞：细胞中基因表达数少于多少基因 以及 大于多少基因
summary(sce.all$nFeature_RNA)
sce.all.filt <- subset(sce.all, subset = nFeature_RNA > 200 & nFeature_RNA < 2500)
dim(sce.all.filt)

# 过滤细胞：细胞中基因表达count数少于多少 以及 大于多少
sce.all.filt <- subset(sce.all.filt, subset = nCount_RNA > 3 )
dim(sce.all.filt)

# 过滤细胞指标2：线粒体/核糖体基因比例/红细胞(根据上面的violin图)
sce.all.filt <- subset(sce.all.filt, subset = percent_mito < 5)
dim(sce.all.filt)
# sce.all.filt <- subset(sce.all.filt, subset = percent_ribo > 3)
# dim(sce.all.filt)
sce.all.filt <- subset(sce.all.filt, subset = percent_hb < 1)
dim(sce.all.filt)

# 过滤后每个样本中的细胞数
stat_raw <- as.data.frame(table(sce.all$orig.ident))
colnames(stat_raw) <- c("sampleid","cellnum_raw")
stat_filter <- as.data.frame(table(sce.all.filt$orig.ident))
colnames(stat_filter) <- c("sampleid","cellnum_filter")
stat <- merge(stat_raw, stat_filter, by="sampleid")
stat$filtered <- stat$cellnum_raw - stat$cellnum_filter
head(stat)
write.table(stat, "1-QC/stat.xls",row.names = F,sep = "\t",quote = F)


# 可视化过滤后的情况
p1_filtered <- VlnPlot(sce.all.filt, group.by = "orig.ident", features = c("nFeature_RNA", "nCount_RNA"), 
                       pt.size = 0, ncol = 2) + NoLegend()
w <- length(unique(sce.all.filt$orig.ident))/3+5;w 
ggsave(filename="1-QC/Vlnplot1_filtered.pdf",plot=p1_filtered,width = w,height = 5)

p2_filtered <- VlnPlot(sce.all.filt, group.by = "orig.ident", features = c("percent_mito", "percent_ribo", "percent_hb"), pt.size = 0, ncol = 3) + NoLegend()
w <- length(unique(sce.all.filt$orig.ident))/2+5;w 
ggsave(filename = "1-QC/Vlnplot2_filtered.pdf",plot=p2_filtered,width = w,height = 5)

然后走标准的降维聚类流程，并进行harmony，得到的umap图如下：

得到的结果非常标准，跟文献中的也是一致。

到这里，心里开始非常好奇了，一开始我想了很多情况，是不是特殊的组织什么的，因为以前看到过类似精子或者具有干性的样本得到的umap图也有一些像线条一样扭来扭曲的图。

如2022年2月发表在Hum Mol Genet杂志上的文章《Single-cell ATAC-Seq reveals cell type-specific transcriptional regulation and unique chromatin accessibility in human spermatogenesis》中的生殖细胞和6种睾丸体细胞分析结果umap图：

又如 2018年发表在 Cell Stem Cell杂志上的文章《Single-Cell RNA Sequencing Analysis Reveals Sequential Cell Fate Transition during Human Spermatogenesis》中成人睾丸细胞单细胞umap图：

都是这种丝状，线条状连续的umap散点图。

3、抽丝剥茧，看学员的操作

既然学员的代码都给了，那回头看看呀，初看没什么特别的，然后一步一步运行一下：

#####-----step1:读取数据，创建合并Seurat对象   

dir='1step_LOAD/GSE125527_RAW'
samples=list.files( dir )
samples 

sceList = lapply(samples,function(pro){ 
  print(pro)  
  ct=fread(file.path( dir ,pro),data.table = F)
  ct[1:4,1:4]
  rownames(ct)=ct[,1]
  colnames(ct) = paste(gsub('_filtered_gene_bc_matrices.csv.gz','',pro),
                       colnames(ct) ,sep = '_')
  ct=ct[,-1] 
  sce =CreateSeuratObject(counts =  ct ,
                          project =  pro  ,
                          min.cells = 5,
                          min.features = 300 )
  return(sce)
}) 


do.call(rbind,lapply(sceList, dim))

sce.all=merge(x=sceList[[1]],
              y=sceList[ -1 ])



names(sce.all@assays$RNA@layers)
sce.all[["RNA"]]$counts 

# Alternate accessor function with the same result
LayerData(sce.all, assay = "RNA", layer = "counts")
sce.all <- JoinLayers(sce.all)
dim(sce.all[["RNA"]]$counts )

as.data.frame(sce.all@assays$RNA$counts[1:10, 1:2])