MicroMultitest: Ranking differentially-expressed genes in microarray data

Li Xiao, Linfeng Cao, Javeed Iqbal, Guimei Zhou, Wing C. Chan, Simon Sherman

Research output: Contribution to journalConference articlepeer-review


The important purpose of the microarray gene expression data analysis is to identify significantly differentially-expressed genes between two groups of samples which are in two different experimental states. In this work, we propose to use several statistical test methods for a given microarray data set and cross-refer the results of different statistical test methods. The accuracy of different statistical methods is estimated by Receiver Operation Characteristic (ROC) technique. A new software tool, MicroMultitest, was developed. A number of statistical testing methods (such as t-test, adapted SAM method, p-value adjustments), as well as the ROC analysis technique were implemented in this software. Using the MicroMultitest one has the ability to evaluate the performance of different statistical testing methods by applying each to the same given microarray data set, optimize the cutoff values and permutation times for these statistical testing methods, and select relative reliable differentially-expressed gene set.

Original languageEnglish (US)
Pages (from-to)279
Number of pages1
JournalProceedings of the Annual Hawaii International Conference on System Sciences
StatePublished - 2005
Externally publishedYes
Event38th Annual Hawaii International Conference on System Sciences - Big Island, HI, United States
Duration: Jan 3 2005Jan 6 2005

ASJC Scopus subject areas

  • Engineering(all)


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