recipe bioconductor-linnorm

Linear model and normality based normalization and transformation method (Linnorm)






Linnorm is an algorithm for normalizing and transforming RNA-seq, single cell RNA-seq, ChIP-seq count data or any large scale count data. It has been independently reviewed by Tian et al. on Nature Methods ( Linnorm can work with raw count, CPM, RPKM, FPKM and TPM.

package bioconductor-linnorm

(downloads) docker_bioconductor-linnorm



depends bioconductor-limma:


depends bioconductor-limma:


depends libblas:


depends libgcc-ng:


depends liblapack:


depends libstdcxx-ng:


depends r-amap:

depends r-apcluster:

depends r-base:


depends r-ellipse:

depends r-fastcluster:

depends r-fpc:

depends r-ggdendro:

depends r-ggplot2:

depends r-gmodels:

depends r-igraph:

depends r-mass:

depends r-mclust:

depends r-rcpp:


depends r-rcpparmadillo:


depends r-rtsne:

depends r-statmod:

depends r-vegan:

depends r-zoo:



You need a conda-compatible package manager (currently either micromamba, mamba, or conda) and the Bioconda channel already activated (see set-up-channels).

While any of above package managers is fine, it is currently recommended to use either micromamba or mamba (see here for installation instructions). We will show all commands using mamba below, but the arguments are the same for the two others.

Given that you already have a conda environment in which you want to have this package, install with:

   mamba install bioconductor-linnorm

and update with::

   mamba update bioconductor-linnorm

To create a new environment, run:

mamba create --name myenvname bioconductor-linnorm

with myenvname being a reasonable name for the environment (see e.g. the mamba docs for details and further options).

Alternatively, use the docker container:

   docker pull<tag>

(see `bioconductor-linnorm/tags`_ for valid values for ``<tag>``)

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