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new c10ca0d37a gnu: Add r-bayesprism.
c10ca0d37a is described below
commit c10ca0d37a640000d09e42766123088041431e6c
Author: Navid Afkhami <[email protected]>
AuthorDate: Thu Mar 6 15:17:18 2025 +0000
gnu: Add r-bayesprism.
* gnu/packages/bioinformatics.scm (r-bayesprism): New variable.
Change-Id: I0eaefbaee0de6143fb892dda9c41459a5a5d30d6
---
gnu/packages/bioinformatics.scm | 45 +++++++++++++++++++++++++++++++++++++++++
1 file changed, 45 insertions(+)
diff --git a/gnu/packages/bioinformatics.scm b/gnu/packages/bioinformatics.scm
index f7f39f525d..d2959e1401 100644
--- a/gnu/packages/bioinformatics.scm
+++ b/gnu/packages/bioinformatics.scm
@@ -24342,6 +24342,51 @@ copy number estimation, as described by
@url{doi:10.1016/j.cell.2012.04.023,Nik-Zainal et al.}")
(license license:gpl3)))
+(define-public r-bayesprism
+ (package
+ (name "r-bayesprism")
+ (version "2.2.2")
+ (source
+ (origin
+ (method git-fetch)
+ (uri (git-reference
+ (url "https://github.com/Danko-Lab/BayesPrism";)
+ ;; The version string in the DESCRIPTION file was changed to
+ ;; 2.2.2 in this commit.
+ (commit "5d43190d5fdfc900571ae1b05b9dcad9ee6b8b2b")))
+ (file-name (git-file-name name version))
+ (sha256
+ (base32 "0rlq6nv1adc9w1ync8834kv59ksixxzbpf3xlbxi5s8x54gv4q9y"))))
+ (properties `((upstream-name . "BayesPrism")))
+ (build-system r-build-system)
+ (arguments
+ (list
+ #:phases
+ '(modify-phases %standard-phases
+ (add-after 'unpack 'chdir
+ (lambda _ (chdir "BayesPrism"))))))
+ (propagated-inputs (list r-biocparallel
+ r-gplots
+ r-matrix
+ r-nmf
+ r-scran
+ r-snowfall
+ r-r-utils))
+ (native-inputs (list r-knitr))
+ (home-page "https://github.com/Danko-Lab/BayesPrism";)
+ (synopsis "Bayesian cell type and gene expression deconvolution")
+ (description
+ "BayesPrism includes deconvolution and embedding learning modules.
+The deconvolution module models a prior from cell type-specific expression
+profiles from scRNA-seq to jointly estimate the posterior distribution of cell
+type composition and cell type-specific gene expression from bulk RNA-seq
+expression of tumor samples. The embedding learning module uses
+@dfn{Expectation-maximization} (EM) to approximate the tumor expression using
+a linear combination of malignant gene programs while conditional on the
+inferred expression and fraction of non-malignant cells estimated by the
+deconvolution module.")
+ (license license:gpl3)))
+
(define-public r-catch
(let ((commit "196ddd5a51b1a5f5daa01de53fdaad9b7505e084")
(revision "1"))
