recipe bioconductor-omada

Machine learning tools for automated transcriptome clustering analysis

Homepage:

https://bioconductor.org/packages/3.18/bioc/html/omada.html

License:

GPL-3

Recipe:

/bioconductor-omada/meta.yaml

Symptomatic heterogeneity in complex diseases reveals differences in molecular states that need to be investigated. However, selecting the numerous parameters of an exploratory clustering analysis in RNA profiling studies requires deep understanding of machine learning and extensive computational experimentation. Tools that assist with such decisions without prior field knowledge are nonexistent and further gene association analyses need to be performed independently. We have developed a suite of tools to automate these processes and make robust unsupervised clustering of transcriptomic data more accessible through automated machine learning based functions. The efficiency of each tool was tested with four datasets characterised by different expression signal strengths. Our toolkit’s decisions reflected the real number of stable partitions in datasets where the subgroups are discernible. Even in datasets with less clear biological distinctions, stable subgroups with different expression profiles and clinical associations were found.

package bioconductor-omada

versions:

1.4.0-1,  1.4.0-0,  1.1.0-0,  1.0.0-0

depends libblas:

>=3.9.0,<4.0a0

depends libgcc-ng:

>=12

depends liblapack:

>=3.9.0,<4.0a0

depends libstdcxx-ng:

>=12

depends r-base:

>=4.3,<4.4.0a0

depends r-clvalid:

>=0.7

depends r-dicer:

>=0.6.0

depends r-dplyr:

>=1.0.7

depends r-fpc:

>=2.2-9

depends r-genieclust:

>=1.1.3

depends r-ggplot2:

>=3.3.5

depends r-glmnet:

>=4.1.3

depends r-kernlab:

>=0.9-29

depends r-pdfcluster:

>=1.0-3

depends r-rcpp:

>=1.0.7

depends r-reshape:

>=0.8.8

requirements:

Installation

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-omada

and update with::

   mamba update bioconductor-omada

To create a new environment, run:

mamba create --name myenvname bioconductor-omada

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 quay.io/biocontainers/bioconductor-omada:<tag>

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

Download stats

Link to this page

Render an badge with the following MarkDown:

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