Cargando…
Gene dispersion is the key determinant of the read count bias in differential expression analysis of RNA-seq data
BACKGROUND: In differential expression analysis of RNA-sequencing (RNA-seq) read count data for two sample groups, it is known that highly expressed genes (or longer genes) are more likely to be differentially expressed which is called read count bias (or gene length bias). This bias had great effec...
Autores principales: | Yoon, Sora, Nam, Dougu |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2017
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5445461/ https://www.ncbi.nlm.nih.gov/pubmed/28545404 http://dx.doi.org/10.1186/s12864-017-3809-0 |
Ejemplares similares
-
Benchmarking RNA-seq differential expression analysis methods using spike-in and simulation data
por: Baik, Bukyung, et al.
Publicado: (2020) -
Improving Gene-Set Enrichment Analysis of RNA-Seq Data with Small Replicates
por: Yoon, Sora, et al.
Publicado: (2016) -
nf-rnaSeqCount: A Nextflow pipeline for obtaining raw read counts from RNA-seq data
por: Mpangase, Phelelani T., et al.
Publicado: (2021) -
Benchmarking integration of single-cell differential expression
por: Nguyen, Hai C. T., et al.
Publicado: (2023) -
Error estimates for the analysis of differential expression from RNA-seq count data
por: Burden, Conrad J., et al.
Publicado: (2014)