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Nat Commun. 2018 Nov 9;9(1):4719. doi: 10.1038/s41467-018-07234-6.

Discovery of rare cells from voluminous single cell expression data.

Author information

1
Department of Electrical Engineering, Indian Institute of Technology Delhi, Hauz Khas, Delhi, 110016, India.
2
Department of Electrical Engineering, Indian Institute of Technology Delhi, Hauz Khas, Delhi, 110016, India. jayadeva@ee.iitd.ac.in.
3
Center for Computational Biology, Indraprastha Institute of Information Technology, Delhi, 110020, India. debarka@iiitd.ac.in.
4
Department of Computer Science and Engineering, Indraprastha Institute of Information Technology, Delhi, 110020, India. debarka@iiitd.ac.in.

Abstract

Single cell messenger RNA sequencing (scRNA-seq) provides a window into transcriptional landscapes in complex tissues. The recent introduction of droplet based transcriptomics platforms has enabled the parallel screening of thousands of cells. Large-scale single cell transcriptomics is advantageous as it promises the discovery of a number of rare cell sub-populations. Existing algorithms to find rare cells scale unbearably slowly or terminate, as the sample size grows to the order of tens of thousands. We propose Finder of Rare Entities (FiRE), an algorithm that, in a matter of seconds, assigns a rareness score to every individual expression profile under study. We demonstrate how FiRE scores can help bioinformaticians focus the downstream analyses only on a fraction of expression profiles within ultra-large scRNA-seq data. When applied to a large scRNA-seq dataset of mouse brain cells, FiRE recovered a novel sub-type of the pars tuberalis lineage.

PMID:
30413715
PMCID:
PMC6226447
DOI:
10.1038/s41467-018-07234-6
[Indexed for MEDLINE]
Free PMC Article

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