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A Windows command-line application that implements a Bayesian wavelet-based functional mixed model methodology for functional data analysis. WFMM can be generally applied to any complex functional data sampled on a fine grid, not just methylation data, and so can be readily applied to other genome-wide data including copy number and tiling transcriptome arrays. The method is computationally intensive, but the software is optimized so that it can handle very large data sets.

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WFMM versioning

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WFMM classification

WFMM specifications

Software type:
Package
Restrictions to use:
None
Programming languages:
C++
Version:
WFMM version 3.1
Interface:
Command line interface
Operating system:
Unix/Linux, Windows
Computer skills:
Advanced
Stability:
Stable

WFMM support

Maintainer

Credits

Publications

  • (Lee and Morris, 2015) Identification of Differentially Methylated Loci Using Wavelet-Based Functional Mixed Models. Bioinformatics.
    PMID: 26559505
  • (Morris and Carroll, 2006) Wavelet-based functional mixed models. Journal of the Royal Statistical Society Series B.
    PMID: 19759841
  • (Morris et al., 2006) Using Wavelet-Based Functional Mixed Models to Characterize Population Heterogeneity in Accelerometer Profiles: A Case Study. Journal of the American Statistical Association.
    PMID: 19169424

Institution(s)

Department of Biostatistics, The University of Texas M.D. Anderson Cancer Center, USA

Funding source(s)

National Institutes of Health (R01ES017646, R01CA107304, R01CA160736, and R01CA178744); Johns Hopkins Bloomberg School of Public Health; the Maryland Cigarette Restitution Program Research Grant; National Institute of Environmental Health Sciences (1R01ES015445); Heinz Family Foundation

Link to literature

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