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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Published in:Nature methods Vol. 16; no. 10; pp. 1007 - 1015
Main Authors: 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
Format: Journal Article
Language:English
Published: United States Nature Publishing Group 01.10.2019
Subjects:
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
Online Access:Get full text
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Summary: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.
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ISSN:1548-7091
1548-7105
1548-7105
DOI:10.1038/s41592-019-0529-1