A semi-automatic cell type annotation method for single-cell RNA sequencing dataset
Single-cell RNA sequencing (scRNA-seq) has been widely applied to provide insights into the cell-by-cell expression difference in a given bulk sample. Accordingly, numerous analysis methods have been developed. As it involves simultaneous analyses of many cell and genes, efficiency of the methods is...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BioMed Central
2020-09-01
|
| Series: | Genomics & Informatics |
| Subjects: | |
| Online Access: | http://genominfo.org/upload/pdf/gi-2020-18-3-e26.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832569949472686080 |
|---|---|
| author | Wan Kim Sung Min Yoon Sangsoo Kim |
| author_facet | Wan Kim Sung Min Yoon Sangsoo Kim |
| author_sort | Wan Kim |
| collection | DOAJ |
| description | Single-cell RNA sequencing (scRNA-seq) has been widely applied to provide insights into the cell-by-cell expression difference in a given bulk sample. Accordingly, numerous analysis methods have been developed. As it involves simultaneous analyses of many cell and genes, efficiency of the methods is crucial. The conventional cell type annotation method is laborious and subjective. Here we propose a semi-automatic method that calculates a normalized score for each cell type based on user-supplied cell type–specific marker gene list. The method was applied to a publicly available scRNA-seq data of mouse cardiac non-myocyte cell pool. Annotating the 35 t-stochastic neighbor embedding clusters into 12 cell types was straightforward, and its accuracy was evaluated by constructing co-expression network for each cell type. Gene Ontology analysis was congruent with the annotated cell type and the corollary regulatory network analysis showed upstream transcription factors that have well supported literature evidences. The source code is available as an R script upon request. |
| format | Article |
| id | doaj-art-a7c6327053dc486db8b57c69facd25b5 |
| institution | Kabale University |
| issn | 2234-0742 |
| language | English |
| publishDate | 2020-09-01 |
| publisher | BioMed Central |
| record_format | Article |
| series | Genomics & Informatics |
| spelling | doaj-art-a7c6327053dc486db8b57c69facd25b52025-02-02T18:37:17ZengBioMed CentralGenomics & Informatics2234-07422020-09-01183e2610.5808/GI.2020.18.3.e26616A semi-automatic cell type annotation method for single-cell RNA sequencing datasetWan KimSung Min YoonSangsoo KimSingle-cell RNA sequencing (scRNA-seq) has been widely applied to provide insights into the cell-by-cell expression difference in a given bulk sample. Accordingly, numerous analysis methods have been developed. As it involves simultaneous analyses of many cell and genes, efficiency of the methods is crucial. The conventional cell type annotation method is laborious and subjective. Here we propose a semi-automatic method that calculates a normalized score for each cell type based on user-supplied cell type–specific marker gene list. The method was applied to a publicly available scRNA-seq data of mouse cardiac non-myocyte cell pool. Annotating the 35 t-stochastic neighbor embedding clusters into 12 cell types was straightforward, and its accuracy was evaluated by constructing co-expression network for each cell type. Gene Ontology analysis was congruent with the annotated cell type and the corollary regulatory network analysis showed upstream transcription factors that have well supported literature evidences. The source code is available as an R script upon request.http://genominfo.org/upload/pdf/gi-2020-18-3-e26.pdfcell type annotationco-expression networkregulatory networksingle-cell rna sequencingtranscription factor |
| spellingShingle | Wan Kim Sung Min Yoon Sangsoo Kim A semi-automatic cell type annotation method for single-cell RNA sequencing dataset Genomics & Informatics cell type annotation co-expression network regulatory network single-cell rna sequencing transcription factor |
| title | A semi-automatic cell type annotation method for single-cell RNA sequencing dataset |
| title_full | A semi-automatic cell type annotation method for single-cell RNA sequencing dataset |
| title_fullStr | A semi-automatic cell type annotation method for single-cell RNA sequencing dataset |
| title_full_unstemmed | A semi-automatic cell type annotation method for single-cell RNA sequencing dataset |
| title_short | A semi-automatic cell type annotation method for single-cell RNA sequencing dataset |
| title_sort | semi automatic cell type annotation method for single cell rna sequencing dataset |
| topic | cell type annotation co-expression network regulatory network single-cell rna sequencing transcription factor |
| url | http://genominfo.org/upload/pdf/gi-2020-18-3-e26.pdf |
| work_keys_str_mv | AT wankim asemiautomaticcelltypeannotationmethodforsinglecellrnasequencingdataset AT sungminyoon asemiautomaticcelltypeannotationmethodforsinglecellrnasequencingdataset AT sangsookim asemiautomaticcelltypeannotationmethodforsinglecellrnasequencingdataset AT wankim semiautomaticcelltypeannotationmethodforsinglecellrnasequencingdataset AT sungminyoon semiautomaticcelltypeannotationmethodforsinglecellrnasequencingdataset AT sangsookim semiautomaticcelltypeannotationmethodforsinglecellrnasequencingdataset |