ProbCD: enrichment analysis accounting for categorization uncertainty

Details ProbCD: enrichment analysis accounting for categorization uncertainty ProbCD: enrichment analysis accounting for categorization uncertainty

2007-07-10

arxiv.org/abs/0707.1503v1

BMC Bioinformatics 2007, 8:383

Biology

Quantitative Biology

Quantitative Methods

16 pages, 3 figures, submitted to a journal in the Bioinformatics field

Scientific paper

10.1186/1471-2105-8-383

As in many other areas of science, systems biology makes extensive use of statistical association and significance estimates in contingency tables, a type of categorical data analysis known in this field as enrichment (also over-representation or enhancement) analysis. In spite of efforts to create probabilistic annotations, especially in the Gene Ontology context, or to deal with uncertainty in high throughput-based datasets, current enrichment methods largely ignore this probabilistic information since they are mainly based on variants of the Fisher Exact Test. We developed an open-source R package to deal with probabilistic categorical data analysis, ProbCD, that does not require a static contingency table. The contingency table for the enrichment problem is built using the expectation of a Bernoulli Scheme stochastic process given the categorization probabilities. An on-line interface was created to allow usage by non-programmers and is available at: http://xerad.systemsbiology.net/ProbCD/ . We present an analysis framework and software tools to address the issue of uncertainty in categorical data analysis. In particular, concerning the enrichment analysis, ProbCD can accommodate: (i) the stochastic nature of the high-throughput experimental techniques and (ii) probabilistic gene annotation.

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