Cargando…
Differential expression analysis for sequence count data
High-throughput sequencing assays such as RNA-Seq, ChIP-Seq or barcode counting provide quantitative readouts in the form of count data. To infer differential signal in such data correctly and with good statistical power, estimation of data variability throughout the dynamic range and a suitable err...
Autores principales: | Anders, Simon, Huber, Wolfgang |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2010
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3218662/ https://www.ncbi.nlm.nih.gov/pubmed/20979621 http://dx.doi.org/10.1186/gb-2010-11-10-r106 |
Ejemplares similares
-
DGEclust: differential expression analysis of clustered count data
por: Vavoulis, Dimitrios V, et al.
Publicado: (2015) -
Detecting differential usage of exons from RNA-seq data
por: Anders, Simon, et al.
Publicado: (2012) -
GoM DE: interpreting structure in sequence count data with differential expression analysis allowing for grades of membership
por: Carbonetto, Peter, et al.
Publicado: (2023) -
UMI-count modeling and differential expression analysis for single-cell RNA sequencing
por: Chen, Wenan, et al.
Publicado: (2018) -
Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2
por: Love, Michael I, et al.
Publicado: (2014)