Unknown

Dataset Information

0

CAMML with the Integration of Marker Proteins (ChIMP).


ABSTRACT:

Motivation

Cell typing is a critical task in the analysis of single-cell data, particularly when studying complex diseased tissues. Unfortunately, the sparsity and noise of single-cell data make accurate cell typing of individual cells difficult. To address these challenges, we previously developed the CAMML method for multi-label cell typing of single-cell RNA-sequencing (scRNA-seq) data. CAMML uses weighted gene sets to score each profiled cell for multiple potential cell types. While CAMML outperforms other scRNA-seq cell typing techniques, it only leverages transcriptomic data so cannot take advantage of newer multi-omic single-cell assays that jointly profile gene expression and protein abundance (e.g. joint scRNA-seq/CITE-seq).

Results

We developed the CAMML with the Integration of Marker Proteins (ChIMP) method to support multi-label cell typing of individual cells jointly profiled via scRNA-seq and CITE-seq. ChIMP combines cell type scores computed on scRNA-seq data via the CAMML approach with discretized CITE-seq measurements for cell type marker proteins. The multi-omic cell type scores generated by ChIMP allow researchers to more precisely and conservatively cell type joint scRNA-seq/CITE-seq data.

Availability and implementation

An implementation of this work is available on CRAN at https://cran.r-project.org/web/packages/CAMML/.

Supplementary information

Supplementary data are available at Bioinformatics online.

SUBMITTER: Schiebout C 

PROVIDER: S-EPMC9710548 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature

altmetric image

Publications

CAMML with the Integration of Marker Proteins (ChIMP).

Schiebout Courtney C   Frost H Robert HR  

Bioinformatics (Oxford, England) 20221101 23


<h4>Motivation</h4>Cell typing is a critical task in the analysis of single-cell data, particularly when studying complex diseased tissues. Unfortunately, the sparsity and noise of single-cell data make accurate cell typing of individual cells difficult. To address these challenges, we previously developed the CAMML method for multi-label cell typing of single-cell RNA-sequencing (scRNA-seq) data. CAMML uses weighted gene sets to score each profiled cell for multiple potential cell types. While  ...[more]

Similar Datasets

2008-04-10 | GSE11104 | GEO
| S-EPMC9741637 | biostudies-literature
| S-EPMC10210129 | biostudies-literature
| S-EPMC11886890 | biostudies-literature
| S-EPMC10047527 | biostudies-literature
| S-EPMC9316264 | biostudies-literature
| S-EPMC4270834 | biostudies-literature
| S-EPMC12830795 | biostudies-literature
| S-EPMC5465527 | biostudies-literature
| S-EPMC3510789 | biostudies-literature