@@ -1616,6 +1616,54 @@ (define-public r-adam
functionally associated genes} (GFAG).")
(license license:gpl2+)))
+(define-public r-adimpute
+ (package
+ (name "r-adimpute")
+ (version "1.4.0")
+ (source (origin
+ (method url-fetch)
+ (uri (bioconductor-uri "ADImpute" version))
+ (sha256
+ (base32
+ "1bkq1hd8sqg9r28r70a9vd3gb2nsmg6dybf002d621p88cdfjib2"))))
+ (properties `((upstream-name . "ADImpute")))
+ (build-system r-build-system)
+ (propagated-inputs (list r-biocparallel
+ r-checkmate
+ r-data-table
+ r-drimpute
+ r-kernlab
+ r-mass
+ r-matrix
+ r-rsvd
+ r-s4vectors
+ r-saver
+ r-singlecellexperiment
+ r-summarizedexperiment))
+ (native-inputs (list r-knitr))
+ (home-page "https://bioconductor.org/packages/ADImpute")
+ (synopsis "Adaptive computational prediction for dropout imputations")
+ (description
+ "@dfn{Single-cell RNA sequencing} (scRNA-seq) methods are typically unable
+to quantify the expression levels of all genes in a cell, creating a need for
+the computational prediction of missing values (dropout imputation). Most
+existing dropout imputation methods are limited in the sense that they
+exclusively use the scRNA-seq dataset at hand and do not exploit external
+gene-gene relationship information. The @code{ADImpute} package proposes two
+novel methods to address this issue:
+
+@enumerate
+@item a gene regulatory network-based approach using gene-gene relationships
+learnt from external data;
+@item a baseline approach corresponding to a sample-wide average.
+@end enumerate
+
+@code{ADImpute} implements these novel methods and also combines them with
+existing imputation methods like @code{DrImpute} and @code{SAVER}.
+@code{ADImpute} can learn the best performing method per gene and combine the
+results from different methods into an ensemble.")
+ (license license:gpl3+)))
+
(define-public r-aneufinder
(package
(name "r-aneufinder")