ProbCD: enrichment analysis accounting for categorization uncertainty View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2007-10-12

AUTHORS

Ricardo ZN Vêncio, Ilya Shmulevich

ABSTRACT

BACKGROUND: 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. RESULTS: 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: http://xerad.systemsbiology.net/ProbCD/. CONCLUSION: 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. More... »

PAGES

383-383

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/1471-2105-8-383

DOI

http://dx.doi.org/10.1186/1471-2105-8-383

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1012073658

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/17935624


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