ProbCD: enrichment analysis accounting for categorization uncertainty.
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/.


CONCLUSIONS
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.
Vêncio
Ricardo Z N
RZ
Institute for Systems Biology, 1441 North 34th street, Seattle, WA 98103-8904, USA. rvencio@gmail.com
Shmulevich
Ilya
I
D000465:Algorithms; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D020869:Gene Expression Profiling / Q000379:methods*; D008954:Models, Biological; D015233:Models, Statistical; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012984:Software