z-logo
open-access-imgOpen Access
Comparison of methods to detect differentially expressed genes between single-cell populations
Author(s) -
Maria K. Jaakkola,
Fatemeh Seyednasrollah,
Arfa Mehmood,
Laura L. Elo
Publication year - 2016
Publication title -
briefings in bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.204
H-Index - 113
eISSN - 1477-4054
pISSN - 1467-5463
DOI - 10.1093/bib/bbw057
Subject(s) - rna seq , computational biology , gene , single cell analysis , biology , rna , cell , reproducibility , gene expression , genetics , computer science , transcriptome , mathematics , statistics
We compared five statistical methods to detect differentially expressed genes between two distinct single-cell populations. Currently, it remains unclear whether differential expression methods developed originally for conventional bulk RNA-seq data can also be applied to single-cell RNA-seq data analysis. Our results in three diverse comparison settings showed marked differences between the different methods in terms of the number of detections as well as their sensitivity and specificity. They, however, did not reveal systematic benefits of the currently available single-cell-specific methods. Instead, our previously introduced reproducibility-optimization method showed good performance in all comparison settings without any single-cell-specific modifications.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.
Having issues? You can contact us here
Accelerating Research

Address

John Eccles House
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom