Cargando…
How to normalize metatranscriptomic count data for differential expression analysis
BACKGROUND: Differential expression analysis on the basis of RNA-Seq count data has become a standard tool in transcriptomics. Several studies have shown that prior normalization of the data is crucial for a reliable detection of transcriptional differences. Until now it has not been clear whether a...
Autores principales: | Klingenberg, Heiner, Meinicke, Peter |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2017
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5649605/ https://www.ncbi.nlm.nih.gov/pubmed/29062598 http://dx.doi.org/10.7717/peerj.3859 |
Ejemplares similares
-
Statistical approaches for differential expression analysis in metatranscriptomics
por: Zhang, Yancong, et al.
Publicado: (2021) -
Characterization of CRISPR RNA transcription by exploiting stranded metatranscriptomic data
por: Ye, Yuzhen, et al.
Publicado: (2016) -
Error estimates for the analysis of differential expression from RNA-seq count data
por: Burden, Conrad J., et al.
Publicado: (2014) -
MIntO: A Modular and Scalable Pipeline For Microbiome Metagenomic and Metatranscriptomic Data Integration
por: Saenz, Carmen, et al.
Publicado: (2022) -
GMPR: A robust normalization method for zero-inflated count data with application to microbiome sequencing data
por: Chen, Li, et al.
Publicado: (2018)