Cluster-separated classification approach for gene expression analysis

Abstract

Due to dramatic progress in high-throughput sequencing technologies and widespread of microarray assays over the last decade, gene expression data has been accumulating at an accelerating pace. All this insured gene expression profiling to be extensively used as a powerful technique for phenotype classification in many biological studies. However, this is not always possible to replicate a particular experiment with various organisms or tissues to achieve sample size that will be large enough to meet the assumptions of classical statistical methods used to deliver reliable classification results. Small dataset size due to lack of sample objects can also be a problem when trying to reuse the data from public databases submitted by other researchers from their experiments. In this paper we introduce a two-step classification method for a specific task of phenotype identification, which firstly clusters data and then performs classification within each cluster. We apply this method to a real dataset for the purpose of bacterial gene-expression analysis and present its results.