Bioinformatics

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As part of our mission, we create quite a bit of software and tools for researchers. Here’s what we have:

R code from “Adding Confidence to Gene Expression Clustering,” by B. Munneke, K.A. Schlauch, K.L. Simonsen, W.D. Beavis, and R.W. Doerge. Genetics. 2005 August; 170(4)

Abstract: It has been well established that gene expression data contain large amounts of random variation that affects both the analysis and the results of microarray experiments. Typically, microarray data are either tested for differential expression between conditions, or grouped together based on profiles that are assessed temporally, or across genetic or environmental conditions. While testing differential expression relies on levels of certainty to evaluate the relative worth of various analyses, cluster analysis is exploratory in nature and has not had the benefit of any judgement of statistical inference. By using a novel dissimilarity function to ascertain gene expression clusters and condi- tional randomization of the data space to illuminate distinctions between statistically significant clusters of gene expression patterns, we aim to provide a level of confidence to inferred clusters of gene expression data. We apply both permutation and convex hull approaches for randomization of the data space, and show that both methods can provide an effective assessment of gene expression profiles whose co-regulation is statistically different than expected from random chance alone.

• PDF of article
• permcluster-2005.r
• permcluster-convexhull-2005.r