Package: EBPRS 2.1.0

Shuang Song

EBPRS: Derive Polygenic Risk Score Based on Emprical Bayes Theory

EB-PRS is a novel method that leverages information for effect sizes across all the markers to improve the prediction accuracy. No parameter tuning is needed in the method, and no external information is needed. This R-package provides the calculation of polygenic risk scores from the given training summary statistics and testing data. We can use EB-PRS to extract main information, estimate Empirical Bayes parameters, derive polygenic risk scores for each individual in testing data, and evaluate the PRS according to AUC and predictive r2. See Song et al. (2020) <doi:10.1371/journal.pcbi.1007565> for a detailed presentation of the method.

Authors:Shuang Song [aut, cre], Wei Jiang [aut], Lin Hou [aut] and Hongyu Zhao [aut]

EBPRS_2.1.0.tar.gz
EBPRS_2.1.0.zip(r-4.7)EBPRS_2.1.0.zip(r-4.6)EBPRS_2.1.0.zip(r-4.5)
EBPRS_2.1.0.tgz(r-4.6-any)EBPRS_2.1.0.tgz(r-4.5-any)
EBPRS_2.1.0.tar.gz(r-4.7-any)EBPRS_2.1.0.tar.gz(r-4.6-any)
EBPRS_2.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html
card.svg |card.png
EBPRS/json (API)

# Install 'EBPRS' in R:
install.packages('EBPRS', repos = c('https://cranhaven.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/cranhaven/cranhaven.r-universe.dev/issues

Datasets:
  • traindat - Example data for training set

On CRAN:

Conda:

archivedpackagesr-universe

1.70 score 5 stars 10 scripts 207 downloads 4 exports 9 dependencies

Last updated from:443f8d554e (on package/EBPRS). Checks:9 OK. Indexed: no.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK108
source / vignettesOK131
linux-release-x86_64OK108
macos-release-arm64OK84
macos-oldrel-arm64OK107
windows-develOK71
windows-releaseOK73
windows-oldrelOK64
wasm-releaseOK88

Exports:EBPRSEBPRSpackageread_plinkvalidate

Dependencies:BEDMatrixbitopscaToolscrochetdata.tablegplotsgtoolsKernSmoothROCR