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BMCBI
2005

Dissecting systems-wide data using mixture models: application to identify affected cellular processes

13 years 3 months ago
Dissecting systems-wide data using mixture models: application to identify affected cellular processes
Background: Functional analysis of data from genome-scale experiments, such as microarrays, requires an extensive selection of differentially expressed genes. Under many conditions, the proportion of differentially expressed genes is considerable, making the selection criteria a balance between the inclusion of false positives and the exclusion of false negatives. Results: We developed an analytical method to determine a p-value threshold from a microarray experiment that is dependent on the quality and design of the data set. To this aim, populations of p-values are modeled as mathematical functions in which the parameters to describe these functions are estimated in an unsupervised manner. The strength of the method is exemplified by its application to a published gene expression data set of sporadic and familial breast tumors with BRCA1 or BRCA2 mutations. Conclusion: We present an objective and unsupervised way to set thresholds adapted to the quality and design of the experiment....
J. Peter Svensson, Renée X. de Menezes, Ing
Added 15 Dec 2010
Updated 15 Dec 2010
Type Journal
Year 2005
Where BMCBI
Authors J. Peter Svensson, Renée X. de Menezes, Ingela Turesson, Micheline Giphart-Gassler, Harry Vrieling
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