recipe bioconductor-damirseq

Data Mining for RNA-seq data: normalization, feature selection and classification



GPL (>= 2)



The DaMiRseq package offers a tidy pipeline of data mining procedures to identify transcriptional biomarkers and exploit them for both binary and multi-class classification purposes. The package accepts any kind of data presented as a table of raw counts and allows including both continous and factorial variables that occur with the experimental setting. A series of functions enable the user to clean up the data by filtering genomic features and samples, to adjust data by identifying and removing the unwanted source of variation (i.e. batches and confounding factors) and to select the best predictors for modeling. Finally, a "stacking" ensemble learning technique is applied to build a robust classification model. Every step includes a checkpoint that the user may exploit to assess the effects of data management by looking at diagnostic plots, such as clustering and heatmaps, RLE boxplots, MDS or correlation plot.

package bioconductor-damirseq

(downloads) docker_bioconductor-damirseq



depends bioconductor-deseq2:


depends bioconductor-edaseq:


depends bioconductor-edger:


depends bioconductor-limma:


depends bioconductor-summarizedexperiment:


depends bioconductor-sva:


depends r-arm:

depends r-base:


depends r-caret:

depends r-corrplot:

depends r-e1071:

depends r-factominer:

depends r-fselector:

depends r-ggplot2:

depends r-hmisc:

depends r-ineq:

depends r-kknn:

depends r-lubridate:

depends r-mass:

depends r-pheatmap:

depends r-pls:

depends r-plsvarsel:

depends r-plyr:

depends r-randomforest:

depends r-rcolorbrewer:

depends r-reshape2:

depends r-rsnns:



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-damirseq

and update with::

   mamba update bioconductor-damirseq

To create a new environment, run:

mamba create --name myenvname bioconductor-damirseq

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

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