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NIFTI: an evolutionary approach for finding number of clusters in microarray data.

Sudhakar Jonnalagadda¹, Rajagopalan Srinivasan

¹Department of Chemical and Biomolecular Engineering, National University of Singapore, 10 Kent Ridge Crescent, 119260 Singapore. sudhakar@nus.edu.sg

BMC Bioinformatics

|January 31, 2009

Summary

Determining the optimal number of clusters in gene expression data is crucial. Our novel method, using the Net InFormation Transfer Index (NIFTI), accurately identifies the best partition, outperforming existing techniques.

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Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Clustering is vital for gene expression data analysis, organizing genes and assays to understand functions and disease subtypes.
Current clustering algorithms often require users to pre-specify the number of clusters, risking inaccurate biological insights.
Incorrect cluster number specification can lead to missed discoveries or artificial data splitting.

Purpose of the Study:

To develop a novel, robust method for automatically determining the optimal number of clusters in gene expression datasets.
To address the limitations of existing methods that require a priori specification of cluster numbers.

Main Methods:

A new procedure evaluates different data partitions generated by clustering algorithms.
The method utilizes a novel Net InFormation Transfer Index (NIFTI) to measure information change with added clusters.
Optimal partition selection is based on maximizing Total Information Content (TIC), considering dynamic cluster member rearrangement.

Main Results:

The proposed method successfully identified the correct number of clusters across four diverse microarray datasets.
Performance evaluation demonstrated superior accuracy compared to established methods.
The approach showed independence from the specific clustering algorithm employed.

Conclusions:

The novel method effectively determines the optimal number of clusters in gene expression data.
This approach offers a significant improvement over existing techniques, enhancing the reliability of biological data analysis.
The method's invariance to clustering techniques broadens its applicability in bioinformatics.