Supplementary
website for
“Reconstruction of
genetic association networks from microarray data:
A partial least squares
approach”
Vasyl Pihur, Somnath Datta, Susmita
Datta*
*e-mail:
susmita.datta_AT_louisville.edu
Abstract
Motivation: Gene association networks provide vast amounts of information
about essential processes inside the cell. A complete picture of gene-gene
interactions would open new horizons for biologists, ranging from pure
appreciation to successful manipulation of biological pathways for therapeutic
purposes. Therefore, identification of important biological complexes whose
members (genes and their products proteins) interact with each other is of
prime importance. Numerous experimental methods exist but, for the most part,
they are costly and labor-intensive. Computational techniques, such as the one
proposed in this work, provide a quick “budget” solution that can be used as a
screening tool before more expensive techniques are attempted. Here, we
introduce a novel computational method based on the Partial Least Squares (PLS)
regression technique for reconstruction of genetic networks from microarray data.
Results: The proposed PLS
based method is shown to be an effective screening procedure for the detection
of gene-gene interactions from microarray data. Both
simulated and real microarray experiments show that
the PLS based approach is superior to its competitors both in terms of
performance and applicability.
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Download
R-Code (distributed as is without warrantee;
needs the base distribution of R and the R package locfdr from
http://www.r-project.org/)
Additional Results for the Simulated Data
Additional Results for the Real Data
Distribution function of the overall scores for two groups
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Effect of
the number of PLS components on the performance of the genetic network
procedure
|
# of components
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