Document Detail


Statistically invalid classification of high throughput gene expression data.
MedLine Citation:
PMID:  23346359     Owner:  NLM     Status:  In-Data-Review    
Abstract/OtherAbstract:
Classification analysis based on high throughput data is a common feature in neuroscience and other fields of science, with a rapidly increasing impact on both basic biology and disease-related studies. The outcome of such classifications often serves to delineate novel biochemical mechanisms in health and disease states, identify new targets for therapeutic interference, and develop innovative diagnostic approaches. Given the importance of this type of studies, we screened 111 recently-published high-impact manuscripts involving classification analysis of gene expression, and found that 58 of them (53%) based their conclusions on a statistically invalid method which can lead to bias in a statistical sense (lower true classification accuracy then the reported classification accuracy). In this report we characterize the potential methodological error and its scope, investigate how it is influenced by different experimental parameters, and describe statistically valid methods for avoiding such classification mistakes.
Authors:
Shahar Barbash; Hermona Soreq
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Publication Detail:
Type:  Journal Article     Date:  2013-01-22
Journal Detail:
Title:  Scientific reports     Volume:  3     ISSN:  2045-2322     ISO Abbreviation:  Sci Rep     Publication Date:  2013  
Date Detail:
Created Date:  2013-01-24     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  101563288     Medline TA:  Sci Rep     Country:  England    
Other Details:
Languages:  eng     Pagination:  1102     Citation Subset:  IM    
Affiliation:
The Edmond & Lily Safra Center for Brain Sciences and the Department of Biological Chemistry at the Hebrew University of Jerusalem.
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