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MixTwice: large-scale hypothesis testing for peptide arrays by variance mixing
- Source :
- Bioinformatics
- Publication Year :
- 2021
- Publisher :
- Oxford University Press (OUP), 2021.
-
Abstract
- Summary Peptide microarrays have emerged as a powerful technology in immunoproteomics as they provide a tool to measure the abundance of different antibodies in patient serum samples. The high dimensionality and small sample size of many experiments challenge conventional statistical approaches, including those aiming to control the false discovery rate (FDR). Motivated by limitations in reproducibility and power of current methods, we advance an empirical Bayesian tool that computes local FDR statistics and local false sign rate statistics when provided with data on estimated effects and estimated standard errors from all the measured peptides. As the name suggests, the MixTwice tool involves the estimation of two mixing distributions, one on underlying effects and one on underlying variance parameters. Constrained optimization techniques provide for model fitting of mixing distributions under weak shape constraints (unimodality of the effect distribution). Numerical experiments show that MixTwice can accurately estimate generative parameters and powerfully identify non-null peptides. In a peptide array study of rheumatoid arthritis, MixTwice recovers meaningful peptide markers in one case where the signal is weak, and has strong reproducibility properties in one case where the signal is strong. Availabilityand implementation MixTwice is available as an R software package https://cran.r-project.org/web/packages/MixTwice/. Supplementary information Supplementary data are available at Bioinformatics online.
- Subjects :
- FOS: Computer and information sciences
Statistics and Probability
False discovery rate
AcademicSubjects/SCI01060
Computer science
Bayesian probability
Gene Expression
01 natural sciences
Biochemistry
Methodology (stat.ME)
010104 statistics & probability
03 medical and health sciences
0101 mathematics
Molecular Biology
Statistics - Methodology
Mixing (physics)
030304 developmental biology
Statistical hypothesis testing
0303 health sciences
Constrained optimization
Variance (accounting)
Original Papers
Unimodality
3. Good health
Computer Science Applications
Computational Mathematics
Standard error
Computational Theory and Mathematics
Algorithm
Subjects
Details
- ISSN :
- 13674811 and 13674803
- Volume :
- 37
- Database :
- OpenAIRE
- Journal :
- Bioinformatics
- Accession number :
- edsair.doi.dedup.....8dea87f430b06f1b285cb84fbde043a6