Fold change and p-value cutoffs significantly alter microarray interpretations

As context is important to gene expression, so is the preprocessing of microarray to transcriptomics. Microarray data suffers from several normalization and significance problems. Arbitrary fold change (FC) cut-offs of >2 and significance p-values of

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Bibliographic Details
Published in:BMC bioinformatics 2012-03, Vol.13 Suppl 2 (Suppl 2), p.S11-S11, Article S11
Main Authors: Dalman, Mark R, Deeter, Anthony, Nimishakavi, Gayathri, Duan, Zhong-Hui
Format: Article
Language:English
Subjects:
Angiogenesis
Bioinformatics
Conferences
Danio rerio
Data collections
Data Interpretation, Statistical
Data processing
Development
DNA microarrays
Gene expression
Gene Expression Profiling - methods
Gene Expression Regulation
Genomes
Heart
Hormones
Hypoxia
Leptin
Oligonucleotide Array Sequence Analysis - methods
Organisms
Proceedings
Statistics
Studies
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Description
Summary:As context is important to gene expression, so is the preprocessing of microarray to transcriptomics. Microarray data suffers from several normalization and significance problems. Arbitrary fold change (FC) cut-offs of >2 and significance p-values of
ISSN:1471-2105
1471-2105
DOI:10.1186/1471-2105-13-s2-s11