ArticleBMC bioinformatics2004
Tests for finding complex patterns of differential expression in cancers: towards individualized medicine.
Article in BMC bioinformatics, 2004. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
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23 citing papers in PubMed, 1 synthesis or guideline pooled it, 41 citations in OpenAlex.
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- Weighted change-point method for detecting differential gene expression in breast cancer microarray data.PloS one · 2012Article
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- Biomarkers in the age of omics: time for a systems biology approach.Omics : a journal of integrative biology · 2011Article
- Endometriosis gene expression heterogeneity and biosignature: a phylogenetic analysis.Obstetrics and gynecology international · 2011Article
- Article
- Dual KS: Defining Gene Sets with Tissue Set Enrichment Analysis.Cancer informatics · 2010Article
- LC-MS Based Detection of Differential Protein Expression.Journal of proteomics & bioinformatics · 2009Article
- Genomic outlier profile analysis: mixture models, null hypotheses, and nonparametric estimation.Biostatistics (Oxford, England) · 2009Article
- SLEPR: a sample-level enrichment-based pathway ranking method -- seeking biological themes through pathway-level consistency.PloS one · 2008Article
- A Population Proportion approach for ranking differentially expressed genes.BMC bioinformatics · 2008Article
- Phylogenetic modeling of heterogeneous gene-expression microarray data from cancerous specimens.Omics : a journal of integrative biology · 2008Article
- Application of microarray outlier detection methodology to psychiatric research.BMC psychiatry · 2008Article
- Evolutionary medicine: A meaningful connection between omics, disease, and treatment.Proteomics. Clinical applications · 2008Article
- A practical multifaceted approach to selecting differentially expressed genes.Cancer informatics · 2008Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMicroarray studies in cancer compare expression levels between two or more sample groups on thousands of genes. Data analysis follows a population-level approach (e.g., comparison of sample means) to identify differentially expressed genes. This leads to the discovery of 'population-level' markers, i.e., genes with the expression patterns A > B and B > A. We introduce the PPST test that identifies genes where a significantly large subset of cases exhibit expression values beyond upper and lower thresholds observed in the control samples.
resultsInterestingly, the test identifies A > B and B < A pattern genes that are missed by population-level approaches, such as the t-test, and many genes that exhibit both significant overexpression and significant underexpression in statistically significantly large subsets of cancer patients (ABA pattern genes). These patterns tend to show distributions that are unique to individual genes, and are aptly visualized in a 'gene expression pattern grid'. The low degree of among-gene correlations in these genes suggests unique underlying genomic pathologies and high degree of unique tumor-specific differential expression. We compare the PPST and the ABA test to the parametric and non-parametric t-test by analyzing two independently published data sets from studies of progression in astrocytoma.
conclusionsThe PPST test resulted findings similar to the nonparametric t-test with higher self-consistency. These tests and the gene expression pattern grid may be useful for the identification of therapeutic targets and diagnostic or prognostic markers that are present only in subsets of cancer patients, and provide a more complete portrait of differential expression in cancer.
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