Probabilistic cell-type assignment of single-cell RNA-seq for tumor microenvironment profiling

Single-cell RNA sequencing has enabled the decomposition of complex tissues into functionally distinct cell types. Often, investigators wish to assign cells to cell types through unsupervised clustering followed by manual annotation or via 'mapping' to existing data. However, manual interp...

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Vydané v:	Nature methods Ročník 16; číslo 10; s. 1007 - 1015
Hlavní autori:	Zhang, Allen W, O'Flanagan, Ciara, Chavez, Elizabeth A, Lim, Jamie L P, Ceglia, Nicholas, McPherson, Andrew, Wiens, Matt, Walters, Pascale, Chan, Tim, Hewitson, Brittany, Lai, Daniel, Mottok, Anja, Sarkozy, Clementine, Chong, Lauren, Aoki, Tomohiro, Wang, Xuehai, Weng, Andrew P, McAlpine, Jessica N, Aparicio, Samuel, Steidl, Christian, Campbell, Kieran R, Shah, Sohrab P
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States Nature Publishing Group 01.10.2019
Predmet:	Annotations Automation Cancer Clustering Computer simulation Datasets Gene expression Gene Expression Profiling Gene sequencing Humans Laboratories Lymphoma Lymphoma, Follicular - immunology Lymphoma, Follicular - pathology Mapping Medical research Ovarian cancer Probabilistic models Probability Research centers Ribonucleic acid RNA Sequence Analysis, RNA - methods Single-Cell Analysis - methods Stem cells Tumor Microenvironment Tumors
ISSN:	1548-7091, 1548-7105, 1548-7105
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Popis
Shrnutí:	Single-cell RNA sequencing has enabled the decomposition of complex tissues into functionally distinct cell types. Often, investigators wish to assign cells to cell types through unsupervised clustering followed by manual annotation or via 'mapping' to existing data. However, manual interpretation scales poorly to large datasets, mapping approaches require purified or pre-annotated data and both are prone to batch effects. To overcome these issues, we present CellAssign, a probabilistic model that leverages prior knowledge of cell-type marker genes to annotate single-cell RNA sequencing data into predefined or de novo cell types. CellAssign automates the process of assigning cells in a highly scalable manner across large datasets while controlling for batch and sample effects. We demonstrate the advantages of CellAssign through extensive simulations and analysis of tumor microenvironment composition in high-grade serous ovarian cancer and follicular lymphoma.
Bibliografia:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	1548-7091 1548-7105 1548-7105
DOI:	10.1038/s41592-019-0529-1