recipe bioconductor-damirseq

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.



GPL (>= 2)



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-randomforest

Depends r-rcolorbrewer

Depends r-reshape2

Depends r-rsnns



With an activated Bioconda channel (see 2. Set up channels), install with:

conda install bioconductor-damirseq

and update with:

conda update bioconductor-damirseq

or use the docker container:

docker pull<tag>

(see bioconductor-damirseq/tags for valid values for <tag>)