Mining coherent patterns from heterogeneous microarray data

TL;DR: This paper proposes a model, F-cluster, for mining subspace coherent patterns from heterogeneous gene expression data and analyzes the search space of the problem and gives a naïve solution for it.

Abstract: Microarray technology is a powerful tool for geneticists to monitor interactions among tens of thousands of genes simultaneously. There has been extensive research on coherent subspace clustering of gene expressions measured under consistent experimental settings. However, these methods assume that all experiments are run using the same batch of microarray chips with similar characteristics of noise. Algorithms developed under this assumption may not be applicable for analyzing data collected from heterogeneous settings, where the set of genes being monitored may be different and expression levels may be not directly comparable even for the same gene. In this paper, we propose a model, F-cluster, for mining subspace coherent patterns from heterogeneous gene expression data. We compare our model with previously proposed models. We analyze the search space of the problem and give a naive solution for it.