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

Abstract:

: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.

journal_name

Nat Methods

journal_title

Nature methods

authors

Zhang AW,O'Flanagan C,Chavez EA,Lim JLP,Ceglia N,McPherson A,Wiens M,Walters P,Chan T,Hewitson B,Lai D,Mottok A,Sarkozy C,Chong L,Aoki T,Wang X,Weng AP,McAlpine JN,Aparicio S,Steidl C,Campbell KR,Shah SP

doi

10.1038/s41592-019-0529-1

subject

Has Abstract

pub_date

2019-10-01 00:00:00

pages

1007-1015

issue

10

eissn

1548-7091

issn

1548-7105

pii

10.1038/s41592-019-0529-1

journal_volume

16

pub_type

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