Rosetta error model for gene expression analysis | Zendy

Lee Weng | Zendy; Hongyue Dai | Zendy; Yihui Zhan | Zendy; Yudong D. He | Zendy; S. Stepaniants | Zendy; Douglas E. Bassett | Zendy

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Rosetta error model for gene expression analysis

Author(s) -

Lee Weng,

Hongyue Dai,

Yihui Zhan,

Yudong D. He,

S. Stepaniants,

Douglas E. Bassett

Publication year - 2006

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/btl045

Subject(s) - dna microarray , variance (accounting) , computer science , statistics , expression (computer science) , statistical model , sensitivity (control systems) , data mining , pattern recognition (psychology) , algorithm , artificial intelligence , mathematics , biology , gene expression , genetics , gene , engineering , accounting , electronic engineering , business , programming language

In microarray gene expression studies, the number of replicated microarrays is usually small because of cost and sample availability, resulting in unreliable variance estimation and thus unreliable statistical hypothesis tests. The unreliable variance estimation is further complicated by the fact that the technology-specific variance is intrinsically intensity-dependent.

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