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author | Ricardo Wurmus <rekado@elephly.net> | 2018-08-29 17:05:46 +0200 |
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committer | Ricardo Wurmus <rekado@elephly.net> | 2018-08-29 17:06:13 +0200 |
commit | c7fe888b424faec05a0d55e8b9321d6852ffb1f2 (patch) | |
tree | 803f5f067fa7c08a13943440ca91a954475a3848 | |
parent | 22b770ce0003b7d6fe98299292f8f5c0569f0712 (diff) | |
download | guix-c7fe888b424faec05a0d55e8b9321d6852ffb1f2.tar guix-c7fe888b424faec05a0d55e8b9321d6852ffb1f2.tar.gz |
gnu: Add python-scanpy.
* gnu/packages/bioinformatics.scm (python-scanpy): New variable.
-rw-r--r-- | gnu/packages/bioinformatics.scm | 36 |
1 files changed, 36 insertions, 0 deletions
diff --git a/gnu/packages/bioinformatics.scm b/gnu/packages/bioinformatics.scm index cb3c4bc1fd..3c300e48e9 100644 --- a/gnu/packages/bioinformatics.scm +++ b/gnu/packages/bioinformatics.scm @@ -13458,3 +13458,39 @@ conversions, region filtering, FASTA sequence extraction and more.") spliced (back-spliced) sequencing reads, indicative of circular RNA (circRNA) in RNA-seq data.") (license license:gpl3)))) + +(define-public python-scanpy + (package + (name "python-scanpy") + (version "1.2.2") + (source + (origin + (method url-fetch) + (uri (pypi-uri "scanpy" version)) + (sha256 + (base32 + "1ak7bxms5a0yvf65prppq2g38clkv7c7jnjbnfpkh3xxv7q512jz")))) + (build-system python-build-system) + (propagated-inputs + `(("python-anndata" ,python-anndata) + ("python-igraph" ,python-igraph) + ("python-numba" ,python-numba) + ("python-joblib" ,python-joblib) + ("python-natsort" ,python-natsort) + ("python-networkx" ,python-networkx) + ("python-statsmodels" ,python-statsmodels) + ("python-scikit-learn" ,python-scikit-learn) + ("python-matplotlib" ,python-matplotlib) + ("python-pandas" ,python-pandas) + ("python-scipy" ,python-scipy) + ("python-seaborn" ,python-seaborn) + ("python-h5py" ,python-h5py) + ("python-tables" ,python-tables))) + (home-page "http://github.com/theislab/scanpy") + (synopsis "Single-Cell Analysis in Python.") + (description "Scanpy is a scalable toolkit for analyzing single-cell gene +expression data. It includes preprocessing, visualization, clustering, +pseudotime and trajectory inference and differential expression testing. The +Python-based implementation efficiently deals with datasets of more than one +million cells.") + (license license:bsd-3))) |