recipe bioconductor-xina

Multiplexes Isobaric Mass Tagged-based Kinetics Data for Network Analysis






The aim of XINA is to determine which proteins exhibit similar patterns within and across experimental conditions, since proteins with co-abundance patterns may have common molecular functions. XINA imports multiple datasets, tags dataset in silico, and combines the data for subsequent subgrouping into multiple clusters. The result is a single output depicting the variation across all conditions. XINA, not only extracts coabundance profiles within and across experiments, but also incorporates protein-protein interaction databases and integrative resources such as KEGG to infer interactors and molecular functions, respectively, and produces intuitive graphical outputs.

package bioconductor-xina

(downloads) docker_bioconductor-xina



depends bioconductor-stringdb:


depends r-alluvial:

depends r-base:


depends r-ggplot2:

depends r-gridextra:

depends r-igraph:

depends r-mclust:

depends r-plyr:



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-xina

and update with::

   mamba update bioconductor-xina

To create a new environment, run:

mamba create --name myenvname bioconductor-xina

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

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