- recipe bioconductor-precisetadhub
Pre-trained random forest models obtained using preciseTAD
- Homepage:
https://bioconductor.org/packages/3.16/data/experiment/html/preciseTADhub.html
- License:
MIT + file LICENSE
- Recipe:
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¶
-
- Versions:
1.6.0-0
,1.2.0-1
,1.2.0-0
,1.0.0-0
- Depends:
bioconductor-data-packages
>=20221108
bioconductor-experimenthub
>=2.6.0,<2.7.0
r-base
>=4.2,<4.3.0a0
- Required By:
Installation
With an activated Bioconda channel (see 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>
)
Download stats¶
Link to this page¶
Render an badge with the following MarkDown:
[](http://bioconda.github.io/recipes/bioconductor-precisetadhub/README.html)