Journal Article

Combining partial correlation and an information theory approach to the reversed engineering of gene co-expression networks

Antonio Reverter and Eva K. F. Chan

in Bioinformatics

Volume 24, issue 21, pages 2491-2497
Published in print November 2008 | ISSN: 1367-4803
Published online September 2008 | e-ISSN: 1460-2059 | DOI: http://dx.doi.org/10.1093/bioinformatics/btn482
Combining partial correlation and an information theory approach to the reversed engineering of gene co-expression networks

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Motivation: We present PCIT, an algorithm for the reconstruction of gene co-expression networks (GCN) that combines the concept partial correlation coefficient with information theory to identify significant gene to gene associations defining edges in the reconstruction of GCN. The properties of PCIT are examined in the context of the topology of the reconstructed network including connectivity structure, clustering coefficient and sensitivity.

Results: We apply PCIT to a series of simulated datasets with varying levels of complexity in terms of number of genes and experimental conditions, as well as to three real datasets. Results show that, as opposed to the constant cutoff approach commonly used in the literature, the PCIT algorithm can identify and allow for more moderate, yet not less significant, estimates of correlation (r) to still establish a connection in the GCN. We show that PCIT is more sensitive than established methods and capable of detecting functionally validated gene–gene interactions coming from absolute r values as low as 0.3. These bona fide associations, which often relate to genes with low variation in expression patterns, are beyond the detection limits of conventional fixed-threshold methods, and would be overlooked by studies relying on those methods.

Availability: FORTRAN 90 source code to perform the PCIT algorithm is available as Supplementary File 1.

Contact: tony.reverter-gomez@csiro.au

Supplementary information: Supplementary data are available at Bioinformatics online.

Journal Article.  5276 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

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