@@ -10301,6 +10301,47 @@ (define-public r-sccustomize
visualization and analysis of single-cell data using R.")
(license license:gpl3+))))
+(define-public r-scgate
+ (package
+ (name "r-scgate")
+ (version "1.6.0")
+ (source
+ (origin
+ (method url-fetch)
+ (uri (cran-uri "scGate" version))
+ (sha256
+ (base32 "0h12d36zjc6fvxbhkxrzbpvw49z9fgyn1jc941q70ajw1yqi2hhh"))))
+ (properties `((upstream-name . "scGate")))
+ (build-system r-build-system)
+ (propagated-inputs
+ (list r-biocparallel
+ r-dplyr
+ r-ggplot2
+ r-ggridges
+ r-patchwork
+ r-reshape2
+ r-seurat
+ r-ucell))
+ (native-inputs (list r-knitr))
+ (home-page "https://github.com/carmonalab/scGate")
+ (synopsis
+ "Marker-based cell type purification for single-cell sequencing data")
+ (description
+ "This package provides a method to purify a cell type or cell population of
+interest from heterogeneous datasets. scGate package automatizes marker-based
+purification of specific cell populations, without requiring training data or
+reference gene expression profiles. scGate takes as input a gene expression
+matrix stored in a Seurat object and a @acronym{GM, gating model}, consisting of
+a set of marker genes that define the cell population of interest. It evaluates
+the strength of signature marker expression in each cell using the rank-based
+method UCell, and then performs @acronym{kNN, k-nearest neighbor} smoothing by
+calculating the mean UCell score across neighboring cells. kNN-smoothing aims
+at compensating for the large degree of sparsity in scRNAseq data. Finally, a
+universal threshold over kNN-smoothed signature scores is applied in binary
+decision trees generated from the user-provided gating model, to annotate cells
+as either “pure” or “impure”, with respect to the cell population of interest.")
+ (license license:gpl3)))
+
(define-public r-markdownhelpers
(let ((commit "793372d28ebed607cc1d35f909a1caedb2b41ffe")
(revision "1"))