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scds: computational annotation of doublets in single-cell RNA sequencing data
Author(s) -
Abha Bais,
Dennis Kostka
Publication year - 2019
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btz698
Subject(s) - bioconductor , identification (biology) , computer science , in silico , annotation , scalability , expression (computer science) , artificial intelligence , computational biology , data mining , biology , gene , genetics , database , botany , programming language
Single-cell RNA sequencing (scRNA-seq) technologies enable the study of transcriptional heterogeneity at the resolution of individual cells and have an increasing impact on biomedical research. However, it is known that these methods sometimes wrongly consider two or more cells as single cells, and that a number of so-called doublets is present in the output of such experiments. Treating doublets as single cells in downstream analyses can severely bias a study's conclusions, and therefore computational strategies for the identification of doublets are needed.

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