Analysis of gene expression in pathophysiological states: balancing false discovery and false negative rates

Andrew W Norris; C Ronald Kahn

doi:10.1073/pnas.0510115103

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Analysis of gene expression in pathophysiological states: balancing false discovery and false negative rates

Journal article

Open access

Peer reviewed

Analysis of gene expression in pathophysiological states: balancing false discovery and false negative rates

Andrew W Norris and C Ronald Kahn

Proceedings of the National Academy of Sciences - PNAS, Vol.103(3), pp.649-653

01/17/2006

DOI: 10.1073/pnas.0510115103

PMCID: PMC1334678

PMID: 16407153

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https://europepmc.org/articles/pmc1334678View

Published (Version of record) Open Access

Abstract

Nucleotide-microarray technology, which allows the simultaneous measurement of the expression of tens of thousands of genes, has become an important tool in the study of disease. In disorders such as malignancy, gene expression often undergoes broad changes of sizable magnitude, whereas in many common multifactorial diseases, such as diabetes, obesity, and atherosclerosis, the changes in gene expression are modest. In the latter circumstance, it is therefore challenging to distinguish the truly changing from non-changing genes, especially because statistical significance must be considered in the context of multiple hypothesis testing. Here, we present a balanced probability analysis (BPA), which provides the biologist with an approach to interpret results in the context of the total number of genes truly differentially expressed and false discovery and false negative rates for the list of genes reaching any significance threshold. In situations where the changes are of modest magnitude, sole consideration of the false discovery rate can result in poor power to detect genes truly differentially expressed. Concomitant analysis of the rate of truly differentially expressed genes not identified, i.e., the false negative rate, allows balancing of the two error rates and a more thorough insight into the data. To this end, we have developed a unique, model-based procedure for the estimation of false negative rates, which allows application of BPA to real data in which changes are modest.

False Negative Reactions

False Positive Reactions

Gene Expression Profiling - statistics & numerical data

Gene Expression Regulation - physiology

Humans

Models, Genetic

Obesity, Morbid - genetics

Obesity, Morbid - physiopathology

Precursor Cell Lymphoblastic Leukemia-Lymphoma - genetics

Precursor Cell Lymphoblastic Leukemia-Lymphoma - physiopathology

Probability

Details

Title: Subtitle: Analysis of gene expression in pathophysiological states: balancing false discovery and false negative rates
Creators: Andrew W Norris - Joslin Diabetes Center
C Ronald Kahn
Resource Type: Journal article
Publication Details: Proceedings of the National Academy of Sciences - PNAS, Vol.103(3), pp.649-653
DOI: 10.1073/pnas.0510115103
PMID: 16407153
PMCID: PMC1334678
NLM abbreviation: Proc Natl Acad Sci U S A
ISSN: 0027-8424
eISSN: 1091-6490
Publisher: National Academy of Sciences
Grant note: K08 DK064906 / NIDDK NIH HHS K08-DK064906 / NIDDK NIH HHS R01 DK060837 / NIDDK NIH HHS R01-DK060837 / NIDDK NIH HHS
Language: English
Date published: 01/17/2006
Academic Unit: Endocrinology and Diabetes; Stead Family Department of Pediatrics; Biochemistry and Molecular Biology
Record Identifier: 9984293085802771

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