Including transcription factor information in the superparamagnetic clustering of microarray data

TL;DR

This paper enhances the superparamagnetic clustering algorithm by integrating transcription factor data, leading to more accurate gene clusters in yeast cell cycle analysis and potential discovery of new cell cycle genes.

Contribution

We introduce SPCTF, a modified clustering algorithm that incorporates transcription factor information, improving gene cluster detection in microarray data.

Findings

SPCTF produces larger, more comprehensive gene clusters.

Some newly identified genes were later confirmed by other studies.

Clusters with potential new cell cycle genes were identified for further research.

Abstract

In this work, we modify the superparamagnetic clustering algorithm (SPC) by adding an extra weight to the interaction formula that considers which genes are regulated by the same transcription factor. With this modified algorithm that we call SPCTF, we analyze Spellman et al. microarray data for cell cycle genes in yeast, and find clusters with a higher number of elements compared with those obtained with the SPC algorithm. Some of the incorporated genes by using SPCFT were not detected at first by Spellman et al. but were later identified by other studies, whereas several genes still remain unclassified. The clusters composed by unidentified genes were analyzed with MUSA, the motif finding using an unsupervised approach algorithm, and this allow us to select the clusters whose elements contain cell cycle transcription factor binding sites as clusters worth of further experimental studies because they would probably lead to new cell cycle genes. Finally, our idea of introducing available information about transcription factors to optimize the gene classification could be implemented for other distance-based clustering algorithms