This is the released version of preciseTADhub; for the devel version, see preciseTADhub.
Pre-trained random forest models obtained using preciseTADBioconductor version: Release (3.21)
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.
Author: Spiro Stilianoudakis [aut], Mikhail Dozmorov [aut, cre]
Maintainer: Mikhail Dozmorov <mikhail.dozmorov at gmail.com>
Citation (from within R, entercitation("preciseTADhub")): Installation
To install this package, start R (version "4.5") and enter:
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("preciseTADhub")
For older versions of R, please refer to the appropriate Bioconductor release.
DocumentationTo view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("preciseTADhub")Follow Installation instructions to use this package in your R session.
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