ProbCD: enrichment analysis accounting for categorization uncertainty

• Main Authors: Shmulevich Ilya, Vêncio Ricardo ZN
• Format: Article
• Language: English
• Published: BMC 2007-10-01
• Series: BMC Bioinformatics
• Online Access: http://www.biomedcentral.com/1471-2105/8/383

<p>Abstract</p> <p>Background</p> <p>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.</p> <p>Results</p> <p>We developed an open-source R-based software 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: <url>http://xerad.systemsbiology.net/ProbCD/</url>.</p> <p>Conclusion</p> <p>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.</p>