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-rw-r--r--gnu/packages/bioconductor.scm59
1 files changed, 59 insertions, 0 deletions
diff --git a/gnu/packages/bioconductor.scm b/gnu/packages/bioconductor.scm
index 45711ab6b6..251ad1e1ac 100644
--- a/gnu/packages/bioconductor.scm
+++ b/gnu/packages/bioconductor.scm
@@ -2424,3 +2424,62 @@ variance stabilization, normalization and gene annotation at the probe level.
It also includes the functions of processing Illumina methylation microarrays,
especially Illumina Infinium methylation microarrays.")
(license license:lgpl2.0+)))
+
+(define-public r-linnorm
+ (package
+ (name "r-linnorm")
+ (version "2.6.1")
+ (source
+ (origin
+ (method url-fetch)
+ (uri (bioconductor-uri "Linnorm" version))
+ (sha256
+ (base32
+ "1qgk8m5kc409flqxs3vnf228v3z0112q8py9hgfgyiwvi6yzdbp6"))))
+ (properties `((upstream-name . "Linnorm")))
+ (build-system r-build-system)
+ (propagated-inputs
+ `(("r-amap" ,r-amap)
+ ("r-apcluster" ,r-apcluster)
+ ("r-ellipse" ,r-ellipse)
+ ("r-fastcluster" ,r-fastcluster)
+ ("r-fpc" ,r-fpc)
+ ("r-ggdendro" ,r-ggdendro)
+ ("r-ggplot2" ,r-ggplot2)
+ ("r-gmodels" ,r-gmodels)
+ ("r-igraph" ,r-igraph)
+ ("r-limma" ,r-limma)
+ ("r-mass" ,r-mass)
+ ("r-mclust" ,r-mclust)
+ ("r-rcpp" ,r-rcpp)
+ ("r-rcpparmadillo" ,r-rcpparmadillo)
+ ("r-rtsne" ,r-rtsne)
+ ("r-statmod" ,r-statmod)
+ ("r-vegan" ,r-vegan)
+ ("r-zoo" ,r-zoo)))
+ (home-page "http://www.jjwanglab.org/Linnorm/")
+ (synopsis "Linear model and normality based transformation method")
+ (description
+ "Linnorm is an R package for the analysis of RNA-seq, scRNA-seq, ChIP-seq
+count data or any large scale count data. It transforms such datasets for
+parametric tests. In addition to the transformtion function (@code{Linnorm}),
+the following pipelines are implemented:
+
+@enumerate
+@item Library size/batch effect normalization (@code{Linnorm.Norm})
+@item Cell subpopluation analysis and visualization using t-SNE or PCA K-means
+ clustering or hierarchical clustering (@code{Linnorm.tSNE},
+ @code{Linnorm.PCA}, @code{Linnorm.HClust})
+@item Differential expression analysis or differential peak detection using
+ limma (@code{Linnorm.limma})
+@item Highly variable gene discovery and visualization (@code{Linnorm.HVar})
+@item Gene correlation network analysis and visualization (@code{Linnorm.Cor})
+@item Stable gene selection for scRNA-seq data; for users without or who do
+ not want to rely on spike-in genes (@code{Linnorm.SGenes})
+@item Data imputation (@code{Linnorm.DataImput}).
+@end enumerate
+
+Linnorm can work with raw count, CPM, RPKM, FPKM and TPM. Additionally, the
+@code{RnaXSim} function is included for simulating RNA-seq data for the
+evaluation of DEG analysis methods.")
+ (license license:expat)))