# 读取 GSE218316 的基因文件
gene <- read.table("D:/Book/T2DM/Data/10x/genes.tsv")$V1
head(gene)[1] "REG3A" "CXCL10" "CTRB2" "PRSS2" "REG1A" "SPINK1"length(gene)[1] 2000创建 SeuratObject
通过 Read10X()
施工中
通过逐个读取方法
施工中
读取其他形式的原始数据
施工中
通过已经预处理好的文件来创建 SeuratObject，并添加 metadata
数据集：GSE218316，一个关于胰岛细胞的数据集。
三个预处理好的 10x 文件，一个记录了
meta.data的文件GSE218316_filtered_counts_barcodes.tsv.gzGSE218316_filtered_counts_data.mtx.gzGSE218316_filtered_counts_gene.tsv.gzGSE218316_metadata_percell.csv.gz
前三份文件是作者过滤过的SeuratObject导出，最后一份文件提供了metadata，方便我们创建自己的SeuratObject时添加注释。
解决方案
处理文件
将上述头三个文件解压重命名为barcodes.tsv, matrix.mtx, genes.tsv，我把这三个文件放在了工作路径下的Data文件夹里。
解压GSE218316_metadata_percell.csv.gz，得到 GSE218316_metadata_percell.csv。
其中barcodes与矩阵文件都是和我们在前文中读取的格式一致，唯独genes.tsv和以往的features.tsv不同。我们可以看看差异：
# 随便挑一个原始的常规基因文件读取
gene <- read.table("D:/Book/T2DM/Data/10x/feature.tsv")$V2
head(gene)[1] "MIR1302-2HG" "FAM138A" "OR4F5" "AL627309.1" "AL627309.3"
[6] "AL627309.2" length(gene)[1] 33540不难发现处理过的基因文件只剩下了2000个genesymbol。由于格式不同，我们不可以用read_10x()函数来读取。且由于基因个数不是常规的基因数（下文讨论），无法将其合并到处理好的表达矩阵中。在这里我们可以替换成该数据集的原始基因文件（由于几个样本的平台相同，因此随便选一个基因文件作为输入即可）。
# 常规情况下，读取第二栏获取 genesymbol
dir.10x <- "D:/Book/T2DM/Data/10x/"
genes <- read.table(paste0(dir.10x, "feature.tsv"), stringsAsFactors = F, header = F)$V2
head(genes)[1] "MIR1302-2HG" "FAM138A" "OR4F5" "AL627309.1" "AL627309.3"
[6] "AL627309.2" # 此时只要读取第一栏
genes <- read.table(paste0(dir.10x, "genes.tsv"), stringsAsFactors = F, header = F)$V1
head(genes)[1] "REG3A" "CXCL10" "CTRB2" "PRSS2" "REG1A" "SPINK1"创建 SeuratObject
现在需要处理 metadata 文件。
# 导入 metadata
meta <- read.csv("D:/Book/T2DM/Data/GSE218316_metadata_percell.csv",header = T )
head(meta) X Condition Donor Timepoint seurat_clusters
1 B1_AAACCTGAGACTAAGT Glucose+Palmitate D1 24h 23
2 B1_AAACCTGAGCAGGCTA Glucose+Palmitate D1 24h 16
3 B1_AAACGGGGTTTCGCTC Glucose+Palmitate D1 24h 7
4 B1_AAAGATGCACATAACC Glucose+Palmitate D1 24h 28
5 B1_AAAGATGCACCTTGTC Glucose+Palmitate D1 24h 20
6 B1_AAAGATGTCGTCTGCT Glucose+Palmitate D1 24h 23
CTclass Celltype
1 Exocrine Duct
2 Exocrine Duct
3 Endocrine Beta
4 Exocrine Duct
5 Endocrine Alpha
6 Exocrine Duct我们在CreateSeuratObject()函数中的meta.data参数中导入meta变量即可。我在下面使用了分布读取的方法，这里直接用 read_10x() 也是可以的。
library(Seurat)Loading required package: SeuratObjectLoading required package: spThe legacy packages maptools, rgdal, and rgeos, underpinning the sp package,
which was just loaded, will retire in October 2023.
Please refer to R-spatial evolution reports for details, especially
https://r-spatial.org/r/2023/05/15/evolution4.html.
It may be desirable to make the sf package available;
package maintainers should consider adding sf to Suggests:.
The sp package is now running under evolution status 2
(status 2 uses the sf package in place of rgdal)
Attaching package: 'SeuratObject'The following objects are masked from 'package:base':
intersect, saveRDSLoading Seurat v5 beta version
To maintain compatibility with previous workflows, new Seurat objects will use the previous object structure by default
To use new Seurat v5 assays: Please run: options(Seurat.object.assay.version = 'v5')library(SeuratObject)
dir.10x <- "D:/Book/T2DM/Data/10x/"
# 这里 features.tsv.gz 替换成原始的基因文件，在这里我把文件解压为 feature.tsv
## read_10x()
genes <- read.table(paste0(dir.10x, "feature.tsv"), stringsAsFactors = F, header = F)$V2
genes <- make.unique(genes, sep = ".")
barcodes <- readLines(paste0(dir.10x, "barcodes.tsv"))
mtx <- Matrix::readMM(paste0(dir.10x, "matrix.mtx"))
mtx <- as(mtx, "dgCMatrix")'as(<dgTMatrix>, "dgCMatrix")' is deprecated.
Use 'as(., "CsparseMatrix")' instead.
See help("Deprecated") and help("Matrix-deprecated").colnames(mtx) <- barcodes
rownames(mtx) <- genes
obj <- CreateSeuratObject(
counts = mtx,
# 传入 meta.data
meta.data = meta,
project = "Pancreatic islet cells"
)查看新创建的SeuratObject：
objAn object of class Seurat
33540 features across 23845 samples within 1 assay
Active assay: RNA (33540 features, 0 variable features)
2 layers present: counts, datahead(obj@meta.data) orig.ident X Condition Donor
B1_AAACCTGAGACTAAGT B1 B1_AAACCTGAGACTAAGT Glucose+Palmitate D1
B1_AAACCTGAGCAGGCTA B1 B1_AAACCTGAGCAGGCTA Glucose+Palmitate D1
B1_AAACGGGGTTTCGCTC B1 B1_AAACGGGGTTTCGCTC Glucose+Palmitate D1
B1_AAAGATGCACATAACC B1 B1_AAAGATGCACATAACC Glucose+Palmitate D1
B1_AAAGATGCACCTTGTC B1 B1_AAAGATGCACCTTGTC Glucose+Palmitate D1
B1_AAAGATGTCGTCTGCT B1 B1_AAAGATGTCGTCTGCT Glucose+Palmitate D1
Timepoint seurat_clusters CTclass Celltype nCount_RNA
B1_AAACCTGAGACTAAGT 24h 23 Exocrine Duct 6161
B1_AAACCTGAGCAGGCTA 24h 16 Exocrine Duct 11882
B1_AAACGGGGTTTCGCTC 24h 7 Endocrine Beta 23081
B1_AAAGATGCACATAACC 24h 28 Exocrine Duct 4864
B1_AAAGATGCACCTTGTC 24h 20 Endocrine Alpha 10304
B1_AAAGATGTCGTCTGCT 24h 23 Exocrine Duct 9934
nFeature_RNA
B1_AAACCTGAGACTAAGT 2103
B1_AAACCTGAGCAGGCTA 2760
B1_AAACGGGGTTTCGCTC 3569
B1_AAAGATGCACATAACC 1544
B1_AAAGATGCACCTTGTC 1630
B1_AAAGATGTCGTCTGCT 2399讨论
需要注意各个变量中长度的对齐。
mtx 为 33540 x 23845 的矩阵。其中基因 33540 个，细胞 23845 个。
head(mtx)6 x 23845 sparse Matrix of class "dgCMatrix" [[ suppressing 34 column names 'B1_AAACCTGAGACTAAGT', 'B1_AAACCTGAGCAGGCTA', 'B1_AAACGGGGTTTCGCTC' ... ]]
MIR1302-2HG . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-2HG ......
FAM138A ......
OR4F5 ......
AL627309.1 ......
AL627309.3 ......
AL627309.2 ......
.....suppressing 23811 columns in show(); maybe adjust 'options(max.print= *, width = *)'
..............................dim(mtx)[1] 33540 23845我们在传入基因和 metadata 的时候，需要保证他们的数量与 mtx 是一致的。
length(genes)[1] 33540dim(meta)[1] 23845 7dim(mtx)[1] 33540 23845工作环境
devtools::session_info()─ Session info ───────────────────────────────────────────────────────────────
setting value
version R version 4.3.1 (2023-06-16 ucrt)
os Windows 11 x64 (build 22621)
system x86_64, mingw32
ui RTerm
language (EN)
collate Chinese (Simplified)_China.utf8
ctype Chinese (Simplified)_China.utf8
tz Asia/Hong_Kong
date 2023-11-05
pandoc 3.1.1 @ C:/Program Files/RStudio/resources/app/bin/quarto/bin/tools/ (via rmarkdown)
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