MSc Dissertation: Exclusive Row Biclustering for Gene Expression Using a Combinatorial Auction Approach

TL;DR

This dissertation introduces a novel exclusive row biclustering method for gene expression data, combining existing algorithms with combinatorial auction techniques to identify non-overlapping gene groups effectively.

Contribution

It presents a new approach for exclusive row biclustering using combinatorial auctions and a threshold tuning method based on the Gap statistic.

Findings

Successfully identifies large non-overlapping biclusters in synthetic data.

Effectively applies to real-world gene expression datasets.

Threshold tuning via Gap statistic is reliable across examples.

Abstract

The availability of large microarray data has led to a growing interest in biclustering methods in the past decade. Several algorithms have been proposed to identify subsets of genes and conditions according to different similarity measures and under varying constraints. In this paper we focus on the exclusive row biclustering problem for gene expression data sets, in which each row can only be a member of a single bicluster while columns can participate in multiple ones. This type of biclustering may be adequate, for example, for clustering groups of cancer patients where each patient (row) is expected to be carrying only a single type of cancer, while each cancer type is associated with multiple (and possibly overlapping) genes (columns). We present a novel method to identify these exclusive row biclusters through a combination of existing biclustering algorithms and combinatorial auction techniques. We devise an approach for tuning the threshold for our algorithm based on comparison to a null model in the spirit of the Gap statistic approach. We demonstrate our approach on both synthetic and real-world gene expression data and show its power in identifying large span non-overlapping rows sub matrices, while considering their unique nature. The Gap statistic approach succeeds in identifying appropriate thresholds in all our examples.