Performance Analysis of Clustering Algorithms for Gene Expression Data

TL;DR

This paper evaluates clustering algorithms, specifically K-Means and ISODATA variants, for gene expression data, highlighting their effectiveness in identifying co-expressed genes without predefining cluster numbers.

Contribution

The study introduces AGMFI, an improved method for initializing clustering parameters, reducing dependency on initial seed selection and pre-specified cluster counts.

Findings

AGMFI enhances clustering quality over traditional ISODATA.

Clustering algorithms effectively identify biologically relevant gene groups.

Method reduces need for predefining number of clusters.

Abstract

Microarray technology is a process that allows thousands of genes simultaneously monitor to various experimental conditions. It is used to identify the co-expressed genes in specific cells or tissues that are actively used to make proteins, This method is used to analysis the gene expression, an important task in bioinformatics research. Cluster analysis of gene expression data has proved to be a useful tool for identifying co-expressed genes, biologically relevant groupings of genes and samples. In this paper we analysed K-Means with Automatic Generations of Merge Factor for ISODATA- AGMFI, to group the microarray data sets on the basic of ISODATA. AGMFI is to generate initial values for merge and Spilt factor, maximum merge times instead of selecting efficient values as in ISODATA. The initial seeds for each cluster were normally chosen either sequentially or randomly. The quality of the final clusters was found to be influenced by these initial seeds. For the real life problems, the suitable number of clusters cannot be predicted. To overcome the above drawback the current research focused on developing the clustering algorithms without giving the initial number of clusters.