Document Detail

Statistical analysis of global gene expression data: some practical considerations.
MedLine Citation:
PMID:  15102467     Owner:  NLM     Status:  MEDLINE    
Applying appropriate error models and conservative estimates to microarray data helps to reduce the number of false predictions and allows one to focus on biologically relevant observations. Several key conclusions have been drawn from the statistical analysis of global gene expression data: it is worth keeping core information for each experiment, including raw and processed data; biological and technical replicates are needed; careful experimental design makes the analysis simpler and more powerful; the choice of the similarity measure is nontrivial and depends on the goal of an experiment; array information must be complemented with other data; and gene expression studies are 'hypothesis generators'.
Ted Holzman; Eugene Kolker
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Publication Detail:
Type:  Evaluation Studies; Journal Article; Research Support, Non-U.S. Gov't; Validation Studies    
Journal Detail:
Title:  Current opinion in biotechnology     Volume:  15     ISSN:  0958-1669     ISO Abbreviation:  Curr. Opin. Biotechnol.     Publication Date:  2004 Feb 
Date Detail:
Created Date:  2004-04-22     Completed Date:  2004-09-28     Revised Date:  2006-11-15    
Medline Journal Info:
Nlm Unique ID:  9100492     Medline TA:  Curr Opin Biotechnol     Country:  England    
Other Details:
Languages:  eng     Pagination:  52-7     Citation Subset:  IM    
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MeSH Terms
Data Interpretation, Statistical*
Equipment Design
Gene Expression Profiling / instrumentation,  methods*,  trends
Models, Genetic
Models, Statistical*
Oligonucleotide Array Sequence Analysis / instrumentation,  methods*,  trends
Reproducibility of Results
Sensitivity and Specificity
Sequence Analysis, DNA / instrumentation,  methods*,  trends
Technology Assessment, Biomedical

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine

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