Mitsunori Kayano

We address an issue of detecting a switching mechanism in gene expression, where two genes are positively correlated for one experimental condition while they are negatively correlated for another. We compare the performance of existing methods for this issue, roughly divided into two types: interaction test (IT) and the difference of correlation coefficients. Interaction test, currently a standard approach for detecting epistasis in genetics, is the log-likelihood ratio test between two logistic regressions with/without an interaction term, resulting in checking the strength of interaction between two genes. On the other hand, two correlation coefficients can be computed for two experimental conditions and the difference of them shows the alteration of expression trends in a more straightforward manner. In our experiments, we tested three different types of correlation coefficients: Pearson, Spearman and a midcorrelation (biweight midcorrelation). The experiment was performed by using ~ 2.3 × 10⁹ combinations selected out of the GEO (Gene Expression Omnibus) database. We sorted all combinations according to the p-values of IT or by the absolute values of the difference of correlation coefficients and then visually evaluated the top ranked combinations in terms of the switching mechanism. The result showed that 1) combinations detected by IT included non-switching combinations and 2) Pearson was affected by outliers easily while Spearman and the midcorrelation seemed likely to avoid them.

Motivation: We address the issue of finding a three-way gene interaction, i.e. two interacting genes in expression under the genotypes of another gene, given a dataset in which expressions and genotypes are measured at once for each individual. This issue can be a general, switching mechanism in expression of two genes, being controlled by categories of another gene, and finding this type of interaction can be a key to elucidating complex biological systems. The most suitable method for this issue is likelihood ratio test using logistic regressions, which we call interaction test, but a serious problem of this test is computational intractability at a genome-wide level. Results: We developed a fast method for this issue which improves the speed of interaction test by around 10 times for any size of datasets, keeping highly interacting genes with an accuracy of ∼85%. We applied our method to ∼3 × 10⁸ three-way combinations generated from a dataset on human brain samples and detected three-way gene interactions with small P-values. To check the reliability of our results, we first conducted permutations by which we can show that the obtained P-values are significantly smaller than those obtained from permuted null examples. We then used GEO (Gene Expression Omnibus) to generate gene expression datasets with binary classes to confirm the detected three-way interactions by using these datasets and interaction tests. The result showed us some datasets with significantly small P-values, strongly supporting the reliability of the detected three-way interactions. Availability: Software is available from http://www.bic.kyoto-u.ac.jp/pathway/kayano/bioinfo_three-way.html Contact:kayano@kuicr.kyoto-u.ac.jp Supplementary information: Supplementary data are available at Bioinformatics online.