Package Recipe 'bioconductor-damirseq'

recipe bioconductor-damirseq

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

Homepage:: https://bioconductor.org/packages/3.20/bioc/html/DaMiRseq.html
License:: GPL (>= 2)
Recipe:: /bioconductor-damirseq/meta.yaml

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¶

Versions:

2.22.0-0, 2.18.0-0, 2.14.0-0, 2.12.0-0, 2.10.0-0, 2.6.0-0, 2.4.0-0, 2.2.0-1, 2.2.0-0,

2.22.0-0, 2.18.0-0, 2.14.0-0, 2.12.0-0, 2.10.0-0, 2.6.0-0, 2.4.0-0, 2.2.0-1, 2.2.0-0, 1.10.0-0, 1.8.0-1, 1.6.2-0

Depends:

on bioconductor-deseq2 >=1.50.0,<1.51.0
on bioconductor-edaseq >=2.44.0,<2.45.0
on bioconductor-edger >=4.8.0,<4.9.0
on bioconductor-limma >=3.66.0,<3.67.0
on bioconductor-summarizedexperiment >=1.40.0,<1.41.0
on bioconductor-sva >=3.58.0,<3.59.0
on r-arm
on r-base >=4.5,<4.6.0a0
on r-caret
on r-corrplot
on r-e1071
on r-factominer
on r-fselector
on r-ggplot2
on r-hmisc
on r-ineq
on r-kknn
on r-lubridate
on r-mass
on r-pheatmap
on r-pls
on r-plsvarsel
on r-plyr
on r-randomforest
on r-rcolorbrewer
on r-reshape2
on r-rsnns

Additional platforms:

Installation¶

You need a conda-compatible package manager (currently either pixi, conda, or micromamba) and the Bioconda channel already activated (see Usage). Below, we show how to install with either pixi or conda (for micromamba and mamba, commands are essentially the same as with conda).

Pixi¶

With pixi installed and the Bioconda channel set up (see Usage), to install globally, run:

pixi global install bioconductor-damirseq

to add into an existing workspace instead, run:

pixi add bioconductor-damirseq

In the latter case, make sure to first add bioconda and conda-forge to the channels considered by the workspace:

pixi workspace channel add conda-forge
pixi workspace channel add bioconda

Conda¶

With conda installed and the Bioconda channel set up (see Usage), to install into an existing and activated environment, run:

conda install bioconductor-damirseq

Alternatively, to install into a new environment, run:

conda create -n envname bioconductor-damirseq

with envname being the name of the desired environment.

Container¶

Alternatively, every Bioconda package is available as a container image for usage with your preferred container runtime. For e.g. docker, run:

docker pull quay.io/biocontainers/bioconductor-damirseq:<tag>

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

Integrated deployment¶

Finally, note that many scientific workflow management systems directly integrate both conda and container based software deployment. Thus, workflow steps can be often directly annotated to use the package, leading to automatic deployment by the respective workflow management system, thereby improving reproducibility and transparency. Check the documentation of your workflow management system to find out about the integration.

Download stats¶

Link to this page¶

Render an badge with the following MarkDown:

[![install with bioconda](https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg?style=flat)](http://bioconda.github.io/recipes/bioconductor-damirseq/README.html)