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| -rw-r--r-- | gnu/packages/bioinformatics.scm | 32 |
1 files changed, 32 insertions, 0 deletions
diff --git a/gnu/packages/bioinformatics.scm b/gnu/packages/bioinformatics.scm index a0b44e5bbb..c647d48aec 100644 --- a/gnu/packages/bioinformatics.scm +++ b/gnu/packages/bioinformatics.scm @@ -13340,6 +13340,38 @@ Python-based implementation efficiently deals with datasets of more than one 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")) |