gnu: Add python-bbknn.

* gnu/packages/bioinformatics.scm (python-bbknn): New variable.
This commit is contained in:
Ricardo Wurmus 2019-03-29 03:54:46 +01:00
parent c91ecf2db4
commit e9d4409bab
No known key found for this signature in database
GPG key ID: 197A5888235FACAC

View file

@ -13340,6 +13340,38 @@ (define-public python-scanpy
million cells.")
(license license:bsd-3)))
(define-public python-bbknn
(package
(name "python-bbknn")
(version "1.3.1")
(source
(origin
(method url-fetch)
(uri (pypi-uri "bbknn" version))
(sha256
(base32
"1qgdganvj3lyxj84v7alm23b9vqhwpn8z0115qndpnpy90qxynwz"))))
(build-system python-build-system)
(propagated-inputs
`(("python-annoy" ,python-annoy)
("python-cython" ,python-cython)
("python-faiss" ,python-faiss)
("python-numpy" ,python-numpy)
("python-scanpy" ,python-scanpy)))
(home-page "https://github.com/Teichlab/bbknn")
(synopsis "Batch balanced KNN")
(description "BBKNN is a batch effect removal tool that can be directly
used in the Scanpy workflow. It serves as an alternative to
@code{scanpy.api.pp.neighbors()}, with both functions creating a neighbour
graph for subsequent use in clustering, pseudotime and UMAP visualisation. If
technical artifacts are present in the data, they will make it challenging to
link corresponding cell types across different batches. BBKNN actively
combats this effect by splitting your data into batches and finding a smaller
number of neighbours for each cell within each of the groups. This helps
create connections between analogous cells in different batches without
altering the counts or PCA space.")
(license license:expat)))
(define-public gffcompare
(let ((commit "be56ef4349ea3966c12c6397f85e49e047361c41")
(revision "1"))