title: Differential distribution improves gene selection stability and has competitive classification performance for patient survival.
abstract: A consistent difference in average expression level, often referred to as differential expression (DE), has long been used to identify genes useful for classification. However, recent cancer studies have shown that when transcription factors or epigenetic signals become deregulated, a change in expression variability (DV) of target genes is frequently observed. This suggests that assessing the importance of genes by either differential expression or variability alone potentially misses sets of important biomarkers that could lead to improved predictions and treatments. Here, we describe a new approach for assessing the importance of genes based on differential distribution (DD), which combines information from differential expression and differential variability into a unified metric. We show that feature ranking and selection stability based on DD can perform two to three times better than DE or DV alone, and that DD yields equivalent error rates to DE and DV. Finally, assessing genes via differential distribution produces a complementary set of selected genes to DE and DV, potentially opening up new categories of biomarkers.
authors.affiliation: School of Mathematics and Statistics, University of Sydney, NSW 2006, Australia.
authors.forename: Dario
authors.initials: D
authors.lastname: Strbenac
authors.affiliation: Melanoma Institute Australia, University of Sydney, NSW 2060, Australia Centre for Cancer Research, Westmead Millennium Institute, University of Sydney, Westmead NSW 2145, Australia.
authors.forename: Graham J
authors.initials: GJ
authors.lastname: Mann
authors.affiliation: School of Mathematics and Statistics, University of Sydney, NSW 2006, Australia.
authors.forename: Jean Y H
authors.initials: JY
authors.lastname: Yang
authors.affiliation: School of Mathematics and Statistics, University of Sydney, NSW 2006, Australia ARC Centre of Excellence for Mathematical & Statistical Frontiers, University of Melbourne, Parkville VIC 3010, Australia john.ormerod@sydney.edu.au.
authors.forename: John T
authors.initials: JT
authors.lastname: Ormerod
mesh_terms: D000230:Adenocarcinoma / Q000235:genetics / Q000473:pathology; D000077192:Adenocarcinoma of Lung; D000465:Algorithms; D014408:Biomarkers, Tumor / Q000096:biosynthesis / Q000235:genetics*; D005260:Female; D020869:Gene Expression Profiling / Q000379:methods; D015972:Gene Expression Regulation, Neoplastic / Q000235:genetics*; D006801:Humans; D008175:Lung Neoplasms / Q000235:genetics / Q000473:pathology; D008545:Melanoma / Q000235:genetics* / Q000473:pathology; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D010051:Ovarian Neoplasms / Q000235:genetics / Q000473:pathology
chemical_list: D014408:Biomarkers, Tumor