Tritchler D, et al. (2009) Filtering genes for cluster and network analysis. BMC Bioinformatics 10:193
Abstract: ABSTRACT: BACKGROUND: Prior to cluster analysis or genetic network analysis it is customary to filter, or remove genes considered to be irrelevant from the set of genes to be analyzed. Often genes which exhibit less variation across samples than some threshold value are deleted. This can improve interpretability and reduce bias. RESULTS: This paper introduces modular models for representing network structure in order to study the relative effects of different filtering methods. We show that cluster analysis and principal components are strongly affected by filtering. Filtering methods intended specifically for cluster and network analysis are introduced and compared by simulating modular networks with known statistical properties. To study more realistic situations, we analyze simulated "real" data based on well-characterized E. coli and S. cerevisiae regulatory networks. CONCLUSIONS: The methods introduced apply very generally, to any similarity matrix describing gene expression. One of the proposed methods, SUMCOV, performed well for all models simulated.
|Status: Published||Type: Journal Article||PubMed ID: 19549335|
Topics addressed in this paper
- To find other papers on a gene and topic, click on the colored ball in the appropriate box.
- displays other papers with information about that topic for that gene.
- displays other papers in SGD that are associated with that topic.
The topic is addressed in these papers but does not describe a specific gene or chromosomal feature.
- To go to the Locus page for a gene, click on the gene name.