ProbCD: enrichment analysis accounting for categorization uncertainty. 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: this http URL . 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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  1. Da Silva, Israel T.; Vêncio, Ricardo Z. N.; Oliveira, Thiago Y. K.; Molfetta, Greice A.; Jr., Wilson A. Silva: Probfast: probabilistic functional analysis system tool (2010) ioport
  2. Ackermann, Marit; Strimmer, Korbinian: A general modular framework for gene set enrichment analysis (2009) ioport
  3. Den Berg, Bart H. J. Van; Thanthiriwatte, Chamali; Manda, Prashanti; Bridges, Susan M.: Comparing gene annotation enrichment tools for functional modeling of agricultural microarray data (2009) ioport
  4. Ricardo Vencio, Ilya Shmulevich: ProbCD: enrichment analysis accounting for categorization uncertainty (2007) arXiv
  5. Vêncio, Ricardo Zn; Shmulevich, Ilya: Probcd: Enrichment analysis accounting for categorization uncertainty (2007) ioport