recipe bioconductor-microbiomemarker

microbiome biomarker analysis toolkit

Homepage:

https://bioconductor.org/packages/3.16/bioc/html/microbiomeMarker.html

License:

GPL-3

Recipe:

/bioconductor-microbiomemarker/meta.yaml

To date, a number of methods have been developed for microbiome marker discovery based on metagenomic profiles, e.g. LEfSe. However, all of these methods have its own advantages and disadvantages, and none of them is considered standard or universal. Moreover, different programs or softwares may be development using different programming languages, even in different operating systems. Here, we have developed an all-in-one R package microbiomeMarker that integrates commonly used differential analysis methods as well as three machine learning-based approaches, including Logistic regression, Random forest, and Support vector machine, to facilitate the identification of microbiome markers.

package bioconductor-microbiomemarker

Versions:

1.4.0-0,  1.0.0-0

Depends:

• bioconductor-aldex2 >=1.30.0,<1.31.0

• bioconductor-ancombc >=2.0.0,<2.1.0

• bioconductor-biobase >=2.58.0,<2.59.0

• bioconductor-biocgenerics >=0.44.0,<0.45.0

• bioconductor-biocparallel >=1.32.0,<1.33.0

• bioconductor-biomformat >=1.26.0,<1.27.0

• bioconductor-biostrings >=2.66.0,<2.67.0

• bioconductor-complexheatmap >=2.14.0,<2.15.0

• bioconductor-deseq2 >=1.38.0,<1.39.0

• bioconductor-edger >=3.40.0,<3.41.0

• bioconductor-ggtree >=3.6.0,<3.7.0

• bioconductor-iranges >=2.32.0,<2.33.0

• bioconductor-limma >=3.54.0,<3.55.0

• bioconductor-metagenomeseq >=1.40.0,<1.41.0

• bioconductor-multtest >=2.54.0,<2.55.0

• bioconductor-phyloseq >=1.42.0,<1.43.0

• bioconductor-s4vectors >=0.36.0,<0.37.0

• r-base >=4.2,<4.3.0a0

• r-caret

• r-coin

• r-dplyr

• r-ggplot2

• r-ggsignif

• r-magrittr

• r-mass

• r-patchwork

• r-plotroc

• r-proc

• r-purrr

• r-rlang

• r-tibble

• r-tidyr

• r-tidytree

• r-vegan

• r-yaml

Required By:

Installation

With an activated Bioconda channel (see set-up-channels), install with:

conda install bioconductor-microbiomemarker

and update with:

conda update bioconductor-microbiomemarker

or use the docker container:

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

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

Download stats

Link to this page

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