Evaluating methods of inferring gene regulatory networks highlights their lack of performance for single cell gene expression data
Author:
Funder
NYSTEM Training Grant
NYSTEM
Publisher
Springer Science and Business Media LLC
Subject
Applied Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Structural Biology
Link
http://link.springer.com/content/pdf/10.1186/s12859-018-2217-z.pdf
Reference47 articles.
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3. Azizi E, et al. Bayesian inference for single-cell clustering and imputing. Genomics and Computational Biology. 2017;3(1):e46. https://genomicscomputbiol.org/ojs/index.php/GCB/article/view/46 .
4. Finak G, et al. MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data. Genome Biol. 2015;16(1):278.
5. Korthauer KD, et al. A statistical approach for identifying differential distributions in single-cell RNA-seq experiments. Genome Biol. 2016;17(1):222.
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