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@ -6106,6 +6106,60 @@ knowledge integration, designable W and H matrices and multiple forms of
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regularizations.")
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regularizations.")
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(license license:bsd-2))))
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(license license:bsd-2))))
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(define-public r-swne
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(let ((commit "05fc3ee4e09b2c34d99c69d3b97cece4c1c34143")
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(revision "1"))
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(package
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(name "r-swne")
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(version (git-version "0.6.20" revision commit))
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(source
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(origin
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(method git-fetch)
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(uri (git-reference
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(url "https://github.com/yanwu2014/swne")
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(commit commit)))
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(file-name (git-file-name name version))
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(sha256
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(base32 "0crlpg9kclbv4v8250p3086a3lk6f2hcq79psqkdylc1qnrx3kfx"))))
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(properties `((upstream-name . "swne")))
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(build-system r-build-system)
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(propagated-inputs
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(list r-fnn
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r-ggplot2
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r-ggrepel
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r-hash
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r-ica
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r-igraph
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r-irlba
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r-jsonlite
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r-liger
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r-mass
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r-matrix
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r-mgcv
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r-nnlm ;not listed but required at install time
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r-plyr
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r-proxy
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r-rcolorbrewer
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r-rcpp
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r-rcpparmadillo
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r-rcppeigen
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r-reshape
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r-reshape2
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r-snow
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r-umap
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r-usedist))
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(home-page "https://github.com/yanwu2014/swne")
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(synopsis "Visualize high dimensional datasets")
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(description
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"@dfn{Similarity Weighted Nonnegative Embedding} (SWNE) is a method for
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visualizing high dimensional datasets. SWNE uses Nonnegative Matrix
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Factorization to decompose datasets into latent factors, projects those
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factors onto 2 dimensions, and embeds samples and key features in 2 dimensions
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relative to the factors. SWNE can capture both the local and global dataset
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structure, and allows relevant features to be embedded directly onto the
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visualization, facilitating interpretation of the data.")
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(license license:gpl2))))
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(define-public python-rpy2
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(define-public python-rpy2
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(package
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(package
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(name "python-rpy2")
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(name "python-rpy2")
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