Document Detail


Efficient two-sample designs for microarray experiments with biological replications.
MedLine Citation:
PMID:  15506995     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
In the last years, biostatistical research has begun to apply linear models and design theory to develop efficient experimental designs and analysis tools for gene expression microarray data. With two-colour microarrays, direct comparisons of RNA-targets are possible and lead to incomplete block designs. In this setting, efficient designs for simple and factorial microarray experiments have mainly been proposed for technical replicates. But for biological replicates, which are crucial to obtain inference that can be generalised to a biological population, this question has only been discussed recently and is not fully solved yet. In this paper, we propose efficient designs for independent two-sample experiments using two-colour microarrays enabling biologists to measure their biological random samples in an efficient manner to draw generalisable conclusions. We give advice for experimental situations with differing group sizes and show the impact of different designs on the variance and degrees of freedom of the test statistics. The designs proposed in this paper can be evaluated using SAS PROC MIXED or S+/R lme.
Authors:
Jobst Landgrebe; Frank Bretz; Edgar Brunner
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Publication Detail:
Type:  Journal Article; Research Support, Non-U.S. Gov't    
Journal Detail:
Title:  In silico biology     Volume:  4     ISSN:  1386-6338     ISO Abbreviation:  In Silico Biol. (Gedrukt)     Publication Date:  2004  
Date Detail:
Created Date:  2005-03-08     Completed Date:  2005-06-30     Revised Date:  2006-11-15    
Medline Journal Info:
Nlm Unique ID:  9815902     Medline TA:  In Silico Biol     Country:  Netherlands    
Other Details:
Languages:  eng     Pagination:  461-70     Citation Subset:  IM    
Affiliation:
Abteilung Medizinische Statistik, Universität Göttingen, Heinrich-Döker-Weg 12, D-37073 Göttingen, Germany.
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MeSH Terms
Descriptor/Qualifier:
Data Interpretation, Statistical
Gene Expression Profiling / methods*,  standards
Models, Theoretical
Oligonucleotide Array Sequence Analysis / methods*,  standards
RNA, Messenger / analysis
Research Design
Chemical
Reg. No./Substance:
0/RNA, Messenger

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