recipe bioconductor-dmcfb

Differentially Methylated Cytosines via a Bayesian Functional Approach

Homepage:

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

License:

GPL-3

Recipe:

/bioconductor-dmcfb/meta.yaml

DMCFB is a pipeline for identifying differentially methylated cytosines using a Bayesian functional regression model in bisulfite sequencing data. By using a functional regression data model, it tries to capture position-specific, group-specific and other covariates-specific methylation patterns as well as spatial correlation patterns and unknown underlying models of methylation data. It is robust and flexible with respect to the true underlying models and inclusion of any covariates, and the missing values are imputed using spatial correlation between positions and samples. A Bayesian approach is adopted for estimation and inference in the proposed method.

package bioconductor-dmcfb

Versions:

1.12.0-0,  1.8.0-0,  1.6.0-0,  1.4.0-1,  1.4.0-0,  1.2.0-0,  1.0.0-0

Depends:

• bioconductor-biocparallel >=1.32.0,<1.33.0

• bioconductor-genomicranges >=1.50.0,<1.51.0

• bioconductor-iranges >=2.32.0,<2.33.0

• bioconductor-rtracklayer >=1.58.0,<1.59.0

• bioconductor-s4vectors >=0.36.0,<0.37.0

• bioconductor-summarizedexperiment >=1.28.0,<1.29.0

• r-arm

• r-base >=4.2,<4.3.0a0

• r-benchmarkme

• r-data.table

• r-fastdummies

• r-mass

• r-matrixstats

• r-speedglm

• r-tibble

Required By:

Installation

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

conda install bioconductor-dmcfb

and update with:

conda update bioconductor-dmcfb

or use the docker container:

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

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

Download stats

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