How to split grouping information in a single expression matrix?

Question

[Xuhao]

Hello everyone,

I am a rookie in single-cell RNA seq analysis. Currently, I am learning to analyze the data (GSE150703) in R. The colname is like NORM_P14_WR_Mccarrol_r3_CGGTAATTGCCC and OIR_P17_WR_Joyal_r1_TCGCGCCACTAG. When I read the table and created a Seuratobject I found the orig.ident only contains NORM. How can I split the grouping information into four groups such as NORM P14, NORM P17, OIR P14, OIR P17?

Thank you tons.

Answer 1

[bk11]

Please do this to create Seurat object from GSE150703 data-

library(data.table)
ftp="https://ftp.ncbi.nlm.nih.gov/geo/series/GSE150nnn/GSE150703/suppl/GSE150703_retina_NORM_OIR_P14_P17_C57_WR_CD73FT_noamg_normalizedUMI_Count_DGEmatrix.txt.gz"
counts <-data.table::fread(ftp)

rownames(counts)=counts$V1
counts[1:5,1:5]


              V1 NORM_P14_WR_Mccarrol_r3_CGGTAATTGCCC NORM_P14_WR_Mccarrol_r5_AGTACCGTAAGG NORM_P14_WR_Mccarrol_r5_CTAATAAAGCTC NORM_P14_WR_Mccarrol_r2_CGCCGATAGATG
1: 0610005C13RIK                                    0                                    0                                    0                             0.000000
2: 0610007N19RIK                                    0                                    0                                    0                             0.000000
3: 0610007P14RIK                                    0                                    0                                    0                             3.267762
4: 0610009B14RIK                                    0                                    0                                    0                             0.000000
5: 0610009B22RIK                                    0                                    0                                    0                             0.000000

counts$V1 <- NULL
seurat_obj=CreateSeuratObject(counts)
seurat_obj
An object of class Seurat 
21408 features across 31271 samples within 1 assay 
Active assay: RNA (21408 features, 0 variable features)
 1 layer present: counts

head([e-mail])
                                     orig.ident nCount_RNA nFeature_RNA
NORM_P14_WR_Mccarrol_r3_CGGTAATTGCCC       NORM   1040.047          301
NORM_P14_WR_Mccarrol_r5_AGTACCGTAAGG       NORM   1002.495          296
NORM_P14_WR_Mccarrol_r5_CTAATAAAGCTC       NORM   1079.672          326
NORM_P14_WR_Mccarrol_r2_CGCCGATAGATG       NORM   1023.541          299
NORM_P14_WR_Mccarrol_r6_TAGAGTCGAGCC       NORM   1117.872          362
NORM_P14_WR_Mccarrol_r3_CCGACTCCCCAA       NORM   1579.136          567

#changing orig.ident in the following steps
[e-mail]$Idents=rownames([e-mail])
[e-mail]$Idents=stringr::str_extract([e-mail]$Idents, "[^_]*_[^_]*")

[e-mail]$orig.ident <- NULL
[e-mail]$orig.ident <- [e-mail]$Idents
[e-mail]$Idents <- NULL
head([e-mail])
                                     nCount_RNA nFeature_RNA orig.ident
NORM_P14_WR_Mccarrol_r3_CGGTAATTGCCC   1040.047          301   NORM_P14
NORM_P14_WR_Mccarrol_r5_AGTACCGTAAGG   1002.495          296   NORM_P14
NORM_P14_WR_Mccarrol_r5_CTAATAAAGCTC   1079.672          326   NORM_P14
NORM_P14_WR_Mccarrol_r2_CGCCGATAGATG   1023.541          299   NORM_P14
NORM_P14_WR_Mccarrol_r6_TAGAGTCGAGCC   1117.872          362   NORM_P14
NORM_P14_WR_Mccarrol_r3_CCGACTCCCCAA   1579.136          567   NORM_P14

table([e-mail]$orig.ident)
NORM_P14 NORM_P17  OIR_P14  OIR_P17 
   10332     3450     9590     7899 

[Xuhao]

Thanks a million for your support.