An Evaluation of Gene Set Analysis for Biomarker Discovery with Applications to Myeloma Research

Pingping Qu (), Erming Tian (), Bart Barlogie (), Gareth Morgan () and John Crowley ()
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Pingping Qu: Cancer Research and Biostatistics
Erming Tian: Myeloma Institute at University of Arkansas for Medical Sciences
Bart Barlogie: Mt Sinai School of Medicine
Gareth Morgan: Myeloma Institute at University of Arkansas for Medical Sciences
John Crowley: Cancer Research and Biostatistics

A chapter in Frontiers of Biostatistical Methods and Applications in Clinical Oncology, 2017, pp 413-434 from Springer

Abstract: Abstract In this paper, we evaluate 15 methods for gene set analysis in microarrayMicroarray classificationClassification problems. We employ four datasets from myeloma research and three types of biological gene sets, encompassing a total of 12 scenarios. Taking a two-step approach, we first identify important genes within gene sets to create summary gene set scores, we then construct predictive models using the gene set scores as predictors. We propose two powerful linear methods in addition to the well-known SuperPC method for calculating scores. By comparing the 15 gene set methods with methods used in individual-gene analysis, we conclude that, overall, the gene set analysis approach provided more accurate predictions than the individual-gene analysis.

Keywords: Gene set analysis; Individual-gene analysis; Score; Classification; Microarray; Myeloma (search for similar items in EconPapers)
Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-981-10-0126-0_25

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DOI: 10.1007/978-981-10-0126-0_25

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