A comprehensive evaluation of module detection methods for gene expression data
Modules composed of groups of genes with similar expression profiles tend to be functionally related and co-regulated. Here, Saelens et al evaluate the performance of 42 computational methods and provide practical guidelines for module detection in gene expression data.
Guardado en:
Autores principales: | Wouter Saelens, Robrecht Cannoodt, Yvan Saeys |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
Nature Portfolio
2018
|
Materias: | |
Acceso en línea: | https://doaj.org/article/5aabf899f0a64832b9a0b43de76a8607 |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
Spearheading future omics analyses using dyngen, a multi-modal simulator of single cells
por: Robrecht Cannoodt, et al.
Publicado: (2021) -
Trajectory-based differential expression analysis for single-cell sequencing data
por: Koen Van den Berge, et al.
Publicado: (2020) -
Weighted change-point method for detecting differential gene expression in breast cancer microarray data.
por: Yao Wang, et al.
Publicado: (2012) -
Detection of patient subgroups with differential expression in omics data: a comprehensive comparison of univariate measures.
por: Maike Ahrens, et al.
Publicado: (2013) -
Data-driven detection of subtype-specific differentially expressed genes
por: Lulu Chen, et al.
Publicado: (2021)