A comparison of parametric versus permutation methods with applications to general and temporal microarray gene expression data | Zendy

Ronghui Xu | Zendy; Xiaochun Li | Zendy

Open Access

A comparison of parametric versus permutation methods with applications to general and temporal microarray gene expression data

Author(s) -

Ronghui Xu,

Xiaochun Li

Publication year - 2003

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btg155

Subject(s) - permutation (music) , outlier , resampling , parametric statistics , ranking (information retrieval) , robustness (evolution) , metric (unit) , computer science , statistical hypothesis testing , data mining , microarray analysis techniques , sample size determination , mathematics , statistics , algorithm , biology , gene , artificial intelligence , genetics , gene expression , engineering , physics , operations management , acoustics

In analyses of microarray data with a design of different biological conditions, ranking genes by their differential 'importance' is often desired so that biologists can focus research on a small subset of genes that are most likely related to the experiment conditions. Permutation methods are often recommended and used, in place of their parametric counterparts, due to the small sample sizes of microarray experiments and possible non-normality of the data. The recommendations, however, are based on classical knowledge in the hypothesis test setting.

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