recipe bioconductor-irisfgm

Comprehensive Analysis of Gene Interactivity Networks Based on Single-Cell RNA-Seq






Single-cell RNA-Seq data is useful in discovering cell heterogeneity and signature genes in specific cell populations in cancer and other complex diseases. Specifically, the investigation of functional gene modules (FGM) can help to understand gene interactive networks and complex biological processes. QUBIC2 is recognized as one of the most efficient and effective tools for FGM identification from scRNA-Seq data. However, its availability is limited to a C implementation, and its applicative power is affected by only a few downstream analyses functionalities. We developed an R package named IRIS-FGM (integrative scRNA-Seq interpretation system for functional gene module analysis) to support the investigation of FGMs and cell clustering using scRNA-Seq data. Empowered by QUBIC2, IRIS-FGM can identify co-expressed and co-regulated FGMs, predict types/clusters, identify differentially expressed genes, and perform functional enrichment analysis. It is noteworthy that IRIS-FGM also applies Seurat objects that can be easily used in the Seurat vignettes.

package bioconductor-irisfgm

(downloads) docker_bioconductor-irisfgm



depends bioconductor-annotationdbi:


depends bioconductor-clusterprofiler:


depends bioconductor-desingle:






depends bioconductor-scater:


depends bioconductor-scran:


depends bioconductor-singlecellexperiment:


depends libblas:


depends libgcc-ng:


depends liblapack:


depends libstdcxx-ng:


depends r-adaptgauss:

depends r-anocva:

depends r-base:


depends r-colorspace:

depends r-drimpute:

depends r-ggplot2:

depends r-ggpubr:

depends r-ggraph:

depends r-igraph:

depends r-knitr:

depends r-matrix:

depends r-mcl:

depends r-mixtools:

depends r-pheatmap:

depends r-polychrome:

depends r-rcolorbrewer:

depends r-rcpp:


depends r-seurat:



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-irisfgm

and update with::

   mamba update bioconductor-irisfgm

To create a new environment, run:

mamba create --name myenvname bioconductor-irisfgm

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

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