MetaKTSP: a meta-analytic top scoring pair method for robust cross-study validation of omics prediction analysis. Bioinformatics 2016 Jul 01;32(13):1966-73
Date
05/07/2016Pubmed ID
27153719Pubmed Central ID
PMC6280887DOI
10.1093/bioinformatics/btw115Scopus ID
2-s2.0-85007227258 (requires institutional sign-in at Scopus site) 32 CitationsAbstract
MOTIVATION: Supervised machine learning is widely applied to transcriptomic data to predict disease diagnosis, prognosis or survival. Robust and interpretable classifiers with high accuracy are usually favored for their clinical and translational potential. The top scoring pair (TSP) algorithm is an example that applies a simple rank-based algorithm to identify rank-altered gene pairs for classifier construction. Although many classification methods perform well in cross-validation of single expression profile, the performance usually greatly reduces in cross-study validation (i.e. the prediction model is established in the training study and applied to an independent test study) for all machine learning methods, including TSP. The failure of cross-study validation has largely diminished the potential translational and clinical values of the models. The purpose of this article is to develop a meta-analytic top scoring pair (MetaKTSP) framework that combines multiple transcriptomic studies and generates a robust prediction model applicable to independent test studies.
RESULTS: We proposed two frameworks, by averaging TSP scores or by combining P-values from individual studies, to select the top gene pairs for model construction. We applied the proposed methods in simulated data sets and three large-scale real applications in breast cancer, idiopathic pulmonary fibrosis and pan-cancer methylation. The result showed superior performance of cross-study validation accuracy and biomarker selection for the new meta-analytic framework. In conclusion, combining multiple omics data sets in the public domain increases robustness and accuracy of the classification model that will ultimately improve disease understanding and clinical treatment decisions to benefit patients.
AVAILABILITY AND IMPLEMENTATION: An R package MetaKTSP is available online. (http://tsenglab.biostat.pitt.edu/software.htm).
CONTACT: ctseng@pitt.edu
SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Author List
Kim S, Lin CW, Tseng GCAuthor
Chien-Wei Lin PhD Associate Professor in the Data Science Institute department at Medical College of WisconsinMESH terms used to index this publication - Major topics in bold
AlgorithmsBreast Neoplasms
DNA Methylation
Gene Expression Profiling
Genomics
Humans
Idiopathic Pulmonary Fibrosis
Meta-Analysis as Topic
Prognosis
Research Design
Supervised Machine Learning
