ArticleBiomarker insights2010
LSOSS: Detection of Cancer Outlier Differential Gene Expression.
Article in Biomarker insights, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Clear cell renal cell carcinoma associated microRNA expression signatures identified by an integrated bioinformatics analysis.Journal of translational medicine · 2013Pooled it
- Integrated Gene Expression Data-Driven Identification of Molecular Signatures, Prognostic Biomarkers, and Drug Targets for Glioblastoma.BioMed research international · 2024Article
- A comprehensive survey of the approaches for pathway analysis using multi-omics data integration.Briefings in bioinformatics · 2022Review
- DECO: decompose heterogeneous population cohorts for patient stratification and discovery of sample biomarkers using omic data profiling.Bioinformatics (Oxford, England) · 2019Article
- Detection and analysis of stable and flexible genes towards a genome signature framework in cancer.Bioinformation · 2019Article
- Identification of Differentially Expressed Genes in Kawasaki Disease Patients as Potential Biomarkers for IVIG Sensitivity by Bioinformatics Analysis.Pediatric cardiology · 2016Article
- Identification of microRNAs as potential biomarker for gastric cancer by system biological analysis.BioMed research international · 2014Article
- Identification of novel microRNA regulatory pathways associated with heterogeneous prostate cancer.BMC systems biology · 2013Article
- Prediction of heterogeneous differential genes by detecting outliers to a Gaussian tight cluster.BMC bioinformatics · 2013Article
- Comparison of methods to identify aberrant expression patterns in individual patients: augmenting our toolkit for precision medicine.Genome medicine · 2013Article
- Outlier-Based Differential Expression Analysis in Proteomics Studies.Journal of proteomics & bioinformatics · 2011Article
- A comparison of methods for data-driven cancer outlier discovery, and an application scheme to semisupervised predictive biomarker discovery.Cancer informatics · 2011Article
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2 authors.
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Abstract
Detection of differential gene expression using microarray technology has received considerable interest in cancer research studies. Recently, many researchers discovered that oncogenes may be activated in some but not all samples in a given disease group. The existing statistical tools for detecting differentially expressed genes in a subset of the disease group mainly include cancer outlier profile analysis (COPA), outlier sum (OS), outlier robust t-statistic (ORT) and maximum ordered subset t-statistics (MOST). In this study, another approach named Least Sum of Ordered Subset Square t-statistic (LSOSS) is proposed. The results of our simulation studies indicated that LSOSS often has more power than previous statistical methods. When applied to real human breast and prostate cancer data sets, LSOSS was competitive in terms of the biological relevance of top ranked genes. Furthermore, a modified hierarchical clustering method was developed to classify the heterogeneous gene activation patterns of human breast cancer samples based on the significant genes detected by LSOSS. Three classes of gene activation patterns, which correspond to estrogen receptor (ER)+, ER- and a mixture of ER+ and ER-, were detected and each class was assigned a different gene signature.
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