Multi-Study Factor Analysis
Copyright policy )Multi-Study Factor Analysis
AbstractWe introduce a novel class of factor analysis methodologies for the joint analysis of multiple studies. The goal is to separately identify and estimate (1) common factors shared across multiple studies, and (2) study-specific factors. We develop an Expectation Conditional-Maximization algorithm for parameter estimates and we provide a procedure for choosing the numbers of common and specific factors. We present simulations for evaluating the performance of the method and we illustrate it by applying it to gene expression data in ovarian cancer. In both, we clarify the benefits of a joint analysis compared to the standard factor analysis. We have provided a tool to accelerate the pace at which we can combine unsupervised analysis across multiple studies, and understand the cross-study reproducibility of signal in multivariate data. An R package (MSFA), is implemented and is available on GitHub.
- University of Padua Italy
- Sapienza University of Rome Italy
- Department of Computer Science Princeton University United States
- Princeton University United States
- College of New Jersey United States
FOS: Computer and information sciences, dimension reduction, Gene Expression, Statistics - Applications, Paired and multiple comparisons; multiple testing, cross-study analysis; Dimension reduction; ECM algorithm; gene expression; meta-analysis; reproducibility; Statistics and Probability; Biochemistry, Genetics and Molecular Biology (all); Immunology and Microbiology (all); Agricultural and Biological Sciences (all); Applied Mathematics, Applications of statistics to biology and medical sciences; meta analysis, Humans, Computer Simulation, Applications (stat.AP), reproducibility, Ovarian Neoplasms, Measures of association (correlation, canonical correlation, etc.), ECM algorithm, Reproducibility of Results, Factor analysis and principal components; correspondence analysis, meta-analysis, Immune System, gene expression, Female, cross-study analysis, Factor Analysis, Statistical, Algorithms
FOS: Computer and information sciences, dimension reduction, Gene Expression, Statistics - Applications, Paired and multiple comparisons; multiple testing, cross-study analysis; Dimension reduction; ECM algorithm; gene expression; meta-analysis; reproducibility; Statistics and Probability; Biochemistry, Genetics and Molecular Biology (all); Immunology and Microbiology (all); Agricultural and Biological Sciences (all); Applied Mathematics, Applications of statistics to biology and medical sciences; meta analysis, Humans, Computer Simulation, Applications (stat.AP), reproducibility, Ovarian Neoplasms, Measures of association (correlation, canonical correlation, etc.), ECM algorithm, Reproducibility of Results, Factor analysis and principal components; correspondence analysis, meta-analysis, Immune System, gene expression, Female, cross-study analysis, Factor Analysis, Statistical, Algorithms
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