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GiniClust: detecting rare cell types from single-cell gene expression data with Gini index
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GiniClust: detecting rare cell types from single-cell gene expression data with Gini index
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Journal Article
GiniClust: detecting rare cell types from single-cell gene expression data with Gini index
2016
Overview
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.
Publisher
BioMed Central,Springer Nature B.V
Subject
/ Animal Genetics and Genomics
/ Animals
/ Biomedical and Life Sciences
/ Cancer
/ Cerebellar Cortex - metabolism
/ Computational Biology - methods
/ cortex
/ Gene Expression Regulation - genetics
/ Genomes
/ Genomics
/ High-Throughput Nucleotide Sequencing
/ Method
/ Methods
/ Mice
/ Microbial Genetics and Genomics
/ Mouse Embryonic Stem Cells - metabolism
/ Noise
/ RNA
