Inference on differences between classes using cluster-specific contrasts of mixed effects

TL;DR

This paper introduces a novel cluster-specific contrast method using mixed effects to detect differentially expressed genes, improving ranking accuracy and FDR control in bioinformatics analyses.

Contribution

The method incorporates gene-specific mixed effects into cluster contrasts, allowing for more accurate detection of DE genes without relying on hard gene-cluster assignments.

Findings

Outperforms existing methods in gene ranking accuracy.

Achieves higher power in FDR-controlled multiple testing.

Reduces false discoveries in DE gene detection.

Abstract

The detection of differentially expressed (DE) genes is one of the most commonly studied problems in bioinformatics. For example, the identification of DE genes between distinct disease phenotypes is an important first step in understanding and developing treatment drugs for the disease. It can also contribute significantly to the construction of a discriminant rule for predicting the class of origin of an unclassified tissue sample from a patient. We present a novel approach to the problem of detecting DE genes that is based on a test statistic formed as a weighted (normalized) cluster-specific contrast in the mixed effects of the mixture model used in the first instance to cluster the gene profiles into a manageable number of clusters. The key factor in the formation of our test statistic is the use of gene-specific mixed effects in the cluster-specific contrast. It thus means that the (soft) assignment of a given gene to a cluster is not crucial. This is because in addition to class differences between the (estimated) fixed effects terms for a cluster, gene-specific class differences also contribute to the cluster-specific contributions to the final form of the test statistic. The proposed test statistic can be used where the primary aim is to rank the genes in order of evidence against the null hypothesis of no DE. We also show how a P-value can be calculated for each gene for use in multiple hypothesis testing where the intent is to control the false discovery rate (FDR) at some desired level. With the use of real and simulated data sets, we show that the proposed contrast-based approach outperforms other methods commonly used for the detection of DE genes both in a ranking context with lower proportion of false discoveries and in a multiple hypothesis testing context with higher power for a specified level of the FDR.