Filtering Genes for Cluster and Network Analysis
1 Department of Biostatistics, University of Toronto, Toronto, Ontario, Canada
2 Department of Child Health Evaluative Sciences, Hospital for Sick Children Research Institute, Toronto, Ontario, Canada
3 Department of Biostatistics, State University of New York at Buffalo, Buffalo, New York, USA
4 Ontario Cancer Institute, Toronto, Ontario, Canada
Citation and License
BMC Bioinformatics 2009, 10:193 doi:10.1186/1471-2105-10-193Published: 23 June 2009
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 whose variation across samples is less than an arbitrary threshold value are deleted. This can improve interpretability and reduce bias.
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.
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.