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  "Title": "Two-Way Latent Structure Clustering Model",
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  "Date": "2018-08-17",
  "Author": "Michael Swanson",
  "Maintainer": "Michael Swanson <dms866@mail.harvard.edu>",
  "Description": "Implementation of a Bayesian two-way latent structure\nmodel for integrative genomic clustering.  The model clusters\nsamples in relation to distinct data sources, with each\nsubject-dataset receiving a latent cluster label, though\ncluster labels have across-dataset meaning because of the model\nformulation.  A common scaling across data sources is unneeded,\nand inference is obtained by a Gibbs Sampler.  The model can\nfit multivariate Gaussian distributed clusters or a\nheavier-tailed modification of a Gaussian density.  Uniquely\namong integrative clustering models, the formulation makes no\nnestedness assumptions of samples across data sources -- the\nuser can still fit the model if a study subject only has\ninformation from one data source. The package provides a\nvariety of post-processing functions for model examination\nincluding ones for quantifying observed alignment of\nclusterings across genomic data sources.  Run time is optimized\nso that analyses of datasets on the order of thousands of\nfeatures on fewer than 5 datasets and hundreds of subjects can\nconverge in 1 or 2 days on a single CPU.  See \"Swanson DM, Lien\nT, Bergholtz H, Sorlie T, Frigessi A, Investigating Coordinated\nArchitectures Across Clusters in Integrative Studies: a\nBayesian Two-Way Latent Structure Model, 2018,\n<doi:10.1101/387076>, Cold Spring Harbor Laboratory\" at\n<https://www.biorxiv.org/content/early/2018/08/07/387076.full.pdf>\nfor model details.",
  "License": "GPL (>= 2)",
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  "Date/Publication": "2018-08-24 10:00:03 UTC",
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