recipe bioconductor-precisetadhub

Pre-trained random forest models obtained using preciseTAD

Homepage

https://bioconductor.org/packages/3.13/data/experiment/html/preciseTADhub.html

License

MIT + file LICENSE

Recipe

/bioconductor-precisetadhub/meta.yaml

An experimentdata package to supplement the preciseTAD package containing pre-trained models and the variable importances of each genomic annotation used to build the model parsed into list objects and available in ExperimentHub. In total, preciseTADhub provides access to n=84 random forest classification models optimized to predict TAD/chromatin loop boundary regions and stored as .RDS files. The value, n, comes from the fact that we considered l=2 cell lines {GM12878, K562}, g=2 ground truth boundaries {Arrowhead, Peakachu}, and c=21 autosomal chromosomes {CHR1, CHR2, …, CHR22} (omitting CHR9). Furthermore, each object is itself a two-item list containing: (1) the model object, and (2) the variable importances for CTCF, RAD21, SMC3, and ZNF143 used to predict boundary regions. Each model is trained via a "holdout" strategy, in which data from chromosomes {CHR1, CHR2, …, CHRi-1, CHRi+1, …, CHR22} were used to build the model and the ith chromosome was reserved for testing. See https://doi.org/10.1101/2020.09.03.282186 for more detail on the model building strategy.

package bioconductor-precisetadhub

(downloads) docker_bioconductor-precisetadhub

Versions

1.0.0-0

Depends
Required By

Installation

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

conda install bioconductor-precisetadhub

and update with:

conda update bioconductor-precisetadhub

or use the docker container:

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

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

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