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author | Ricardo Wurmus <rekado@elephly.net> | 2022-07-01 14:53:44 +0200 |
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committer | Ricardo Wurmus <rekado@elephly.net> | 2022-07-01 14:54:26 +0200 |
commit | 81cf674a5d5e3dc020df5d43ce62e1c98f2c9711 (patch) | |
tree | 02745fc23ee47c8242c0a933bbd084cb9c4e4a7d /gnu/packages/bioinformatics.scm | |
parent | 741895676b124a8a9a6656a54b5b58fd35d8e0f9 (diff) | |
download | guix-81cf674a5d5e3dc020df5d43ce62e1c98f2c9711.tar.gz guix-81cf674a5d5e3dc020df5d43ce62e1c98f2c9711.zip |
gnu: Add python-ikarus.
* gnu/packages/bioinformatics.scm (python-ikarus): New variable.
Diffstat (limited to 'gnu/packages/bioinformatics.scm')
-rw-r--r-- | gnu/packages/bioinformatics.scm | 47 |
1 files changed, 47 insertions, 0 deletions
diff --git a/gnu/packages/bioinformatics.scm b/gnu/packages/bioinformatics.scm index 75973b0dd8..be19658417 100644 --- a/gnu/packages/bioinformatics.scm +++ b/gnu/packages/bioinformatics.scm @@ -13520,6 +13520,53 @@ transcription factors, gene regulatory networks and cell types from single-cell RNA-seq data.") (license license:gpl3+))) +(define-public python-ikarus + (package + (name "python-ikarus") + (version "0.0.2") + (source + (origin + (method url-fetch) + (uri (pypi-uri "ikarus" version)) + (sha256 + (base32 + "086czpvj4yafz4vrq5rx2gy0bj2l8nzwnkk0gw8qvy4w133xjysy")))) + (build-system python-build-system) + (arguments + `(#:tests? #false + #:phases + (modify-phases %standard-phases + ;; See https://github.com/BIMSBbioinfo/ikarus/issues/12 + (add-after 'unpack 'fix-issue-12 + (lambda _ + (substitute* "ikarus/classifier.py" + (("pyscenic.genesig") "ctxcore.genesig")))) + ;; Numba needs a writable dir to cache functions. + (add-before 'check 'set-numba-cache-dir + (lambda _ + (setenv "NUMBA_CACHE_DIR" "/tmp")))))) + (propagated-inputs + (list python-numpy + python-pandas + python-scipy + python-scanpy + python-anndata + python-ctxcore ;because of issue 12 + pyscenic)) + (home-page "https://github.com/BIMSBbioinfo/ikarus") + (synopsis "Machine learning classifier of tumor cells") + (description + "ikarus is a stepwise machine learning pipeline that tries to cope with a task +of distinguishing tumor cells from normal cells. Leveraging multiple +annotated single cell datasets it can be used to define a gene set specific to +tumor cells. First, the latter gene set is used to rank cells and then to +train a logistic classifier for the robust classification of tumor and normal +cells. Finally, sensitivity is increased by propagating the cell labels based +on a custom cell-cell network. ikarus is tested on multiple single cell +datasets to ascertain that it achieves high sensitivity and specificity in +multiple experimental contexts.") + (license license:expat))) + (define-public vbz-compression (package (name "vbz-compression") |