Abstract
BackgroundThe growing wealth of public available gene expression data has made the systemic studies of how genes interact in a cell become more feasible. Liquid association (LA) describes the extent to which coexpression of two genes may vary based on the expression level of a third gene (the controller gene). However, genome-wide application has been difficult and resource-intensive. We propose a new screening algorithm for more efficient processing of LA estimation on a genome-wide scale and apply its use to a Saccharomyces cerevisiae data set.ResultsOn a test subset of the data, the fast screening algorithm achieved >99.8% agreement with the exhaustive search of LA values, while reduced run time by 81–93 %. Using a well-known yeast cell-cycle data set with 6,178 genes, we identified triplet combinations with significantly large LA values. In an exploratory gene set enrichment analysis, the top terms for the controller genes in these triplets with large LA values are involved in some of the most fundamental processes in yeast such as energy regulation, transportation, and sporulation.ConclusionIn summary, in this paper we propose a novel, efficient algorithm to explore LA on a genome-wide scale and identified triplets of interest in cell cycle pathways using the proposed method in a yeast data set. A software package named fastLiquidAssociation for implementing the algorithm is available through http://www.bioconductor.org.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-014-0371-5) contains supplementary material, which is available to authorized users.
Highlights
The growing wealth of public available gene expression data has made the systemic studies of how genes interact in a cell become more feasible
The findings presented by GO analysis could suggest feasible biological hypotheses; liquid association measure describes ‘association” between gene triplet, but it does not necessary confers “causation.” Further functional experiments will be needed to validate the top triplets identified with large modified liquid association (MLA) values
Some modifications of the fast liquid association algorithm could be: (1) For binary traits, ρdiff can be used as the liquid association measure
Summary
The growing wealth of public available gene expression data has made the systemic studies of how genes interact in a cell become more feasible. Large-scale gene expression data provide snapshots of transcription activity at a genome-wide scale. Data analyses for differential expression focuse on a single gene at a time [2,3,4]. These one-gene-at-a-time analyses separate data into groups depending on the phenotypic status and perform geneby-gene analysis. Recently the focus has shifted to higher order coexpression patterns (i.e. correlations of the expression levels of two or more genes) with the belief that they may reflect more fully the complex interactions between genes [5,6,7,8,9,10,11]
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