ProbCD: enrichment analysis accounting for categorization uncertainty

TL;DR

ProbCD is an open-source R package that advances enrichment analysis by incorporating probabilistic annotations and uncertainty, moving beyond traditional static contingency tables and Fisher's Exact Test.

Contribution

It introduces a novel method for probabilistic categorical data analysis in enrichment studies, including an accessible online interface for broader use.

Findings

Enables analysis with probabilistic gene annotations.

Accounts for uncertainty in high-throughput data.

Provides a flexible framework for enrichment analysis.

Abstract

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.