ArticleJournal of biopharmaceutical statistics2010
Discrete nonparametric algorithms for outlier detection with genomic data.
Article in Journal of biopharmaceutical statistics, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- OutSplice: A Novel Tool for the Identification of Tumor-Specific Alternative Splicing Events.BioMedInformatics · 2023Article
- aTEMPO: Pathway-Specific Temporal Anomalies for Precision Therapeutics.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2020Article
- Integrative computational analysis of transcriptional and epigenetic alterations implicates DTX1 as a putative tumor suppressor gene in HNSCC.Oncotarget · 2017Article
- CSAX: Characterizing Systematic Anomalies in eXpression Data.Journal of computational biology : a journal of computational molecular cell biology · 2015Article
- Outlier Analysis Defines Zinc Finger Gene Family DNA Methylation in Tumors and Saliva of Head and Neck Cancer Patients.PloS one · 2015Article
- Unsupervised outlier profile analysis.Cancer informatics · 2014Review
- Comparison of methods to identify aberrant expression patterns in individual patients: augmenting our toolkit for precision medicine.Genome medicine · 2013Article
- Article
- Outlier Analysis and Top Scoring Pair for Integrated Data Analysis and Biomarker Discovery.IEEE/ACM transactions on computational biology and bioinformaticsArticle
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Authors and funding
1 author.
Funding
Abstract
In high-throughput studies involving genetic data such as from gene expression microarrays, differential expression analysis between two or more experimental conditions has been a very common analytical task. Much of the resulting literature on multiple comparisons has paid relatively little attention to the choice of test statistic. In this article, we focus on the issue of choice of test statistic based on a special pattern of differential expression. The approach here is based on recasting multiple-comparison procedures for assessing outlying expression values. A major complication is that the resulting p values are discrete; some theoretical properties of sequential testing procedures in this context are explored. We propose the use of q value estimation procedures in this setting. Data from a gene expression profiling experiment in prostate cancer are used to illustrate the methodology.
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Registered trials
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