Introduction

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.

Publications

  1. GiniClust: detecting rare cell types from single-cell gene expression data with Gini index.
    Cite this
    Jiang L, Chen H, Pinello L, Yuan GC, 2016-07-01 - Genome biology

Credits

  1. Lan Jiang
    Developer

    Boston Children's Hospital, Boston, United States of America

  2. Huidong Chen
    Developer

    Department of Computer Science and Technology, Tongji University, China

  3. Luca Pinello
    Developer

    Department of Biostatistics, Harvard T.H. Chan School of Public Health, France

  4. Guo-Cheng Yuan
    Investigator

    Harvard Stem Cell Institute, Cambridge

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Summary
AccessionBT000300
Tool TypeApplication
Category
PlatformsLinux/Unix
TechnologiesR
User InterfaceTerminal Command Line
Download Count0
Submitted ByGuo-Cheng Yuan