Comparison of threshold selection methods for microarray gene co-expression matrices.

ABSTRACT:

Background

Network and clustering analyses of microarray co-expression correlation data often require application of a threshold to discard small correlations, thus reducing computational demands and decreasing the number of uninformative correlations. This study investigated threshold selection in the context of combinatorial network analysis of transcriptome data.

Findings

Six conceptually diverse methods - based on number of maximal cliques, correlation of control spots with expressed genes, top 1% of correlations, spectral graph clustering, Bonferroni correction of p-values, and statistical power - were used to estimate a correlation threshold for three time-series microarray datasets. The validity of thresholds was tested by comparison to thresholds derived from Gene On

SUBMITTER: Borate BR 

PROVIDER: S-EPMC2794870 | biostudies-literature | 2009 Dec

REPOSITORIES: biostudies-literature