A framework for list representation, enabling list stabilization through incorporation of gene exchangeabilities

TL;DR

This paper introduces a flexible framework that models gene exchangeability to stabilize gene lists from microarray data, improving robustness of gene rankings against sampling variations while maintaining biological relevance.

Contribution

The paper presents a novel framework that incorporates gene exchangeability into list representation, enhancing stability and comparability of gene lists in microarray studies.

Findings

More robust gene rankings compared to existing methods

Maintains biological significance while improving stability

Supports straightforward comparison of gene lists

Abstract

Analysis of multivariate data sets from e.g. microarray studies frequently results in lists of genes which are associated with some response of interest. The biological interpretation is often complicated by the statistical instability of the obtained gene lists with respect to sampling variations, which may partly be due to the functional redundancy among genes, implying that multiple genes can play exchangeable roles in the cell. In this paper we use the concept of exchangeability of random variables to model this functional redundancy and thereby account for the instability attributable to sampling variations. We present a flexible framework to incorporate the exchangeability into the representation of lists. The proposed framework supports straightforward robust comparison between any two lists. It can also be used to generate new, more stable gene rankings incorporating more information from the experimental data. Using a microarray data set from lung cancer patients we show that the proposed method provides more robust gene rankings than existing methods with respect to sampling variations, without compromising the biological significance.