recipe bioconductor-lineagepulse

Differential expression analysis and model fitting for single-cell RNA-seq data






LineagePulse is a differential expression and expression model fitting package tailored to single-cell RNA-seq data (scRNA-seq). LineagePulse accounts for batch effects, drop-out and variable sequencing depth. One can use LineagePulse to perform longitudinal differential expression analysis across pseudotime as a continuous coordinate or between discrete groups of cells (e.g. pre-defined clusters or experimental conditions). Expression model fits can be directly extracted from LineagePulse.

package bioconductor-lineagepulse

(downloads) docker_bioconductor-lineagepulse



depends bioconductor-biocparallel:


depends bioconductor-complexheatmap:


depends bioconductor-singlecellexperiment:


depends bioconductor-summarizedexperiment:


depends r-base:


depends r-circlize:

depends r-ggplot2:

depends r-gplots:

depends r-knitr:

depends r-matrix:

depends r-rcolorbrewer:



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-lineagepulse

and update with::

   mamba update bioconductor-lineagepulse

To create a new environment, run:

mamba create --name myenvname bioconductor-lineagepulse

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-lineagepulse/tags`_ for valid values for ``<tag>``)

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