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| -rw-r--r-- | gnu/packages/bioinformatics.scm | 30 |
1 files changed, 30 insertions, 0 deletions
diff --git a/gnu/packages/bioinformatics.scm b/gnu/packages/bioinformatics.scm index d6db02188f..933b8b9adf 100644 --- a/gnu/packages/bioinformatics.scm +++ b/gnu/packages/bioinformatics.scm @@ -14497,3 +14497,33 @@ designed for use with hybrid capture, including both whole-exome and custom target panels, and short-read sequencing platforms such as Illumina and Ion Torrent.") (license license:asl2.0))) + +(define-public python-pyfit-sne + (package + (name "python-pyfit-sne") + (version "1.0.1") + (source + (origin + (method git-fetch) + (uri (git-reference + (url "https://github.com/KlugerLab/pyFIt-SNE.git") + (commit version))) + (file-name (git-file-name name version)) + (sha256 + (base32 "13wh3qkzs56azmmgnxib6xfr29g7xh09sxylzjpni5j0pp0rc5qw")))) + (build-system python-build-system) + (propagated-inputs + `(("python-numpy" ,python-numpy))) + (inputs + `(("fftw" ,fftw))) + (native-inputs + `(("python-cython" ,python-cython))) + (home-page "https://github.com/KlugerLab/pyFIt-SNE") + (synopsis "FFT-accelerated Interpolation-based t-SNE") + (description + "t-Stochastic Neighborhood Embedding (t-SNE) is a highly successful +method for dimensionality reduction and visualization of high dimensional +datasets. A popular implementation of t-SNE uses the Barnes-Hut algorithm to +approximate the gradient at each iteration of gradient descent. This package +is a Cython wrapper for FIt-SNE.") + (license license:bsd-4))) |