Cargando…
CAMML with the Integration of Marker Proteins (ChIMP)
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...
Autores principales: | , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9710548/ https://www.ncbi.nlm.nih.gov/pubmed/36214642 http://dx.doi.org/10.1093/bioinformatics/btac674 |
_version_ | 1784841391107473408 |
---|---|
author | Schiebout, Courtney Frost, H Robert |
author_facet | Schiebout, Courtney Frost, H Robert |
author_sort | Schiebout, Courtney |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9710548 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-97105482022-12-01 CAMML with the Integration of Marker Proteins (ChIMP) Schiebout, Courtney Frost, H Robert Bioinformatics Original Paper 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. Oxford University Press 2022-10-10 /pmc/articles/PMC9710548/ /pubmed/36214642 http://dx.doi.org/10.1093/bioinformatics/btac674 Text en © The Author(s) 2022. Published by Oxford University Press. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Original Paper Schiebout, Courtney Frost, H Robert CAMML with the Integration of Marker Proteins (ChIMP) |
title | CAMML with the Integration of Marker Proteins (ChIMP) |
title_full | CAMML with the Integration of Marker Proteins (ChIMP) |
title_fullStr | CAMML with the Integration of Marker Proteins (ChIMP) |
title_full_unstemmed | CAMML with the Integration of Marker Proteins (ChIMP) |
title_short | CAMML with the Integration of Marker Proteins (ChIMP) |
title_sort | camml with the integration of marker proteins (chimp) |
topic | Original Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9710548/ https://www.ncbi.nlm.nih.gov/pubmed/36214642 http://dx.doi.org/10.1093/bioinformatics/btac674 |
work_keys_str_mv | AT schieboutcourtney cammlwiththeintegrationofmarkerproteinschimp AT frosthrobert cammlwiththeintegrationofmarkerproteinschimp |