Towards unified quality verification of synthetic count data with countsimQC

Bioinformatics

Volumn 34, Issue 4, 2018, Pages 691-692

  Soneson, Charlotte ^a,b   Robinson, Mark D ^a,b  

^a UNIVERSITY OF ZURICH   (Switzerland)

^b SWISS INSTITUTE OF BIOINFORMATICS   (Switzerland)

Author keywords

[No Author keywords available]

Indexed keywords

PROCEDURES; SEQUENCE ANALYSIS; SOFTWARE; STANDARDS;

SEQUENCE ANALYSIS, RNA; SOFTWARE;

EID: 85042546527     PISSN: 13674803     EISSN: 14602059     Source Type: Journal    
DOI: 10.1093/bioinformatics/btx631     Document Type: Article

References (7)

1. Ewels,P. et al. (2016) MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics, btw354.
2. Robinson,M. D. et al. (2010) edger: a bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics, 26, 139-140.
3. Shalek,A. K. et al. (2014) Single-cell RNA-seq reveals dynamic paracrine control of cellular variation. Nature, 509, 363-369.
4. Soneson,C. (2014) compcoder-an R package for benchmarking differential expression methods for RNA-seq data. Bioinformatics, 30, 2517-2518.
5. Soneson,C. and Robinson,M. D. (2017) Bias, robustness and scalability in differential expression analysis of single-cell RNA-Seq data. bioRxiv, doi: 10. 1101/143289.
6. Vieth,B. et al. (2017) powsim: power analysis for bulk and single cell RNA-seq experiments. bioRxiv, doi:10. 1101/117150.
7. Zappia,L. et al. (2017) Splatter: simulation of single-cell RNA sequencing data. Genome Biol., 18, 174.