GiniClust: Detecting Rare Cell Types from Single-cell Gene Expression Data with Gini Index

Overview

Journal Genome Biol

Specialties Biology
Genetics

Date 2016 Jul 3

PMID 27368803

Citations 128

Authors

Lan Jiang

Huidong Chen

Luca Pinello

Guo-Cheng Yuan

Affiliations

Soon will be listed here.

Abstract

High-throughput single-cell technologies have great potential to discover new cell types; however, it remains challenging to detect rare cell types that are distinct from a large population. We present a novel computational method, called GiniClust, to overcome this challenge. Validation against a benchmark dataset indicates that GiniClust achieves high sensitivity and specificity. Application of GiniClust to public single-cell RNA-seq datasets uncovers previously unrecognized rare cell types, including Zscan4-expressing cells within mouse embryonic stem cells and hemoglobin-expressing cells in the mouse cortex and hippocampus. GiniClust also correctly detects a small number of normal cells that are mixed in a cancer cell population.

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