Similarity of transcription profiles for genes in gene sets

Authors:
Marko Toplak;Tomaž Curk;Blaž Zupan
Affiliations:
Faculty of Computer and Information Sciences, University of Ljubljana, Slovenia;Faculty of Computer and Information Sciences, University of Ljubljana, Slovenia;Faculty of Computer and Information Sciences, University of Ljubljana, Slovenia and Dept. of Human and Mol. Genetics, Baylor College of Medicine, Houston
Venue:
ICANNGA'11 Proceedings of the 10th international conference on Adaptive and natural computing algorithms - Volume Part II
Year:
2011

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Abstract

In gene set focused knowledge-based analysis we assume that genes from the same functional gene set have similar transcription profiles. We compared the distributions of similarity scores of gene transcription profiles between genes from the same gene sets and genes chosen at random. In line with previous research, our results show that transcription profiles of genes from the same gene sets are on average indeed more similar than random transcription profiles, although the differences are slight. We performed the experiments on 35 human cancer data sets, with KEGG pathways and BioGRID interactions as gene set sources. Pearson correlation coefficient and interaction gain were used as association measures.