Superposition of Transcriptional Behaviors Determines Gene State
Open accessAbstract
We introduce a novel technique to determine the expression state of a gene from quantitative information measuring its expression. Adopting a productive abstraction from current thinking in molecular biology, we consider two expression states for a gene--Up or Down. We determine this state by using a statistical model that assumes the data behaves as a combination of two biological distributions. Given a cohort of hybridizations, our algorithm predicts, for the single reading, the probability of each gene's being in an Up or a Down state in each hybridization. Using a series of publicly available gene expression data sets, we demonstrate that our algorithm outperforms the prevalent algorithm. We also show that our algorithm can be used in conjunction with expression adjustment techniques to produce a more biologically sound gene-state call. The technique we present here enables a routine update, where the continuously evolving expression level adjustments feed into gene-state calculations. The technique can be applied in almost any multi-sample gene expression experiment, and holds equal promise for protein abundance experiments.
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A number of recent papers have used pathway analysis workflow to highlight genes and pathways active in specific model organisms or diseased tissues.
“A number of recent papers have used this pathway analysis workflow to highlight genes and pathways that are active in specific model organisms or diseased tissues, such as breast cancer, using gene and protein expression, copy number variants (CNVs) and SNPs19,44-49.”
From The BioPAX community standard for pathway data sharing (2010) · cites this paper as [45]
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Self-citation
- Authors
- Efroni, S., Carmel, L., Schaefer, C. G., & Buetow, K. H.
- Affiliations
- National Institutes of Health (US)
- Corresponding author
- Buetow, Kenneth H.
- Venue
- PLoS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2008-08-06
- Volume
- 3
- Issue
- 8
- Pages
- e2901
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC2488367
- OpenAlex
- W2123724449
- Cited by
- 13
- References
- 16
- Citations by year
- 2019: 2 · 2018: 1 · 2015: 1 · 2014: 3 · 2013: 1 · 2012: 2
- FWCI
- 0.41
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS ONE (publishedVersion, public-domain)
- Europe PMC (PubMed Central) (publishedVersion, public-domain)
- PubMed Central (publishedVersion, public-domain)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Keywords
- gene expression classification, mixture models, two-state model, microarray hybridization, protein abundance
- MeSH terms
- Algorithms, Breast Neoplasms, Chromosome Mapping, Female, Genes, Humans, Nucleic Acid Hybridization, Probability, Transcription, Genetic, Gene Expression, Gene Expression Regulation, Neoplastic
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