CAraCAl: CAMML with the integration of chromatin accessibility.
Cell typing
Gene activity
scATAC-seq
scRNA-seq
Journal
BMC bioinformatics
ISSN: 1471-2105
Titre abrégé: BMC Bioinformatics
Pays: England
ID NLM: 100965194
Informations de publication
Date de publication:
13 Jun 2024
13 Jun 2024
Historique:
received:
07
03
2024
accepted:
10
06
2024
medline:
14
6
2024
pubmed:
14
6
2024
entrez:
13
6
2024
Statut:
epublish
Résumé
A vital step in analyzing single-cell data is ascertaining which cell types are present in a dataset, and at what abundance. In many diseases, the proportions of varying cell types can have important implications for health and prognosis. Most approaches for cell type annotation have centered around cell typing for single-cell RNA-sequencing (scRNA-seq) and have had promising success. However, reliable methods are lacking for many other single-cell modalities such as single-cell sequencing assay for transposase-accessible chromatin (scATAC-seq), which quantifies the extent to which genes of interest in each cell are epigenetically "open" for expression. To leverage the informative potential of scATAC-seq data, we developed CAMML with the integration of chromatin accessibility (CAraCAl), a bioinformatic method that performs cell typing on scATAC-seq data. CAraCAl performs cell typing by scoring each cell for its enrichment of cell type-specific gene sets. These gene sets are composed of the most upregulated or downregulated genes present in each cell type according to projected gene activity. We found that CAraCAl does not improve performance beyond CAMML when scRNA-seq is present, but if only scATAC-seq is available, CAraCAl performs cell typing relatively successfully. As such, we also discuss best practices for cell typing and the strengths and weaknesses of various cell annotation options.
Sections du résumé
BACKGROUND
BACKGROUND
A vital step in analyzing single-cell data is ascertaining which cell types are present in a dataset, and at what abundance. In many diseases, the proportions of varying cell types can have important implications for health and prognosis. Most approaches for cell type annotation have centered around cell typing for single-cell RNA-sequencing (scRNA-seq) and have had promising success. However, reliable methods are lacking for many other single-cell modalities such as single-cell sequencing assay for transposase-accessible chromatin (scATAC-seq), which quantifies the extent to which genes of interest in each cell are epigenetically "open" for expression.
RESULTS
RESULTS
To leverage the informative potential of scATAC-seq data, we developed CAMML with the integration of chromatin accessibility (CAraCAl), a bioinformatic method that performs cell typing on scATAC-seq data. CAraCAl performs cell typing by scoring each cell for its enrichment of cell type-specific gene sets. These gene sets are composed of the most upregulated or downregulated genes present in each cell type according to projected gene activity.
CONCLUSIONS
CONCLUSIONS
We found that CAraCAl does not improve performance beyond CAMML when scRNA-seq is present, but if only scATAC-seq is available, CAraCAl performs cell typing relatively successfully. As such, we also discuss best practices for cell typing and the strengths and weaknesses of various cell annotation options.
Identifiants
pubmed: 38872103
doi: 10.1186/s12859-024-05833-3
pii: 10.1186/s12859-024-05833-3
doi:
Substances chimiques
Chromatin
0
Transposases
EC 2.7.7.-
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
212Subventions
Organisme : NIH HHS
ID : R35GM146586
Pays : United States
Organisme : NIH HHS
ID : R21CA253408
Pays : United States
Organisme : NIH HHS
ID : P20GM130454
Pays : United States
Organisme : NIH HHS
ID : P30CA023108
Pays : United States
Informations de copyright
© 2024. The Author(s).
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