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CNVreg Package

Introduction

The CNVreg package provides functions to perform copy number variants (CNV) association analysis with penalized regression model.

This package convert CNVs over a genomic region as a piecewise constant curve to capture the dosage and length of CNVs. The association analysis is then evaluated by regressing outcome traits on all CNV fragments in the region while adjusting for covariates. The corresponding CNV effects are obtained at each genome position. The penalized regression model with Lasso and weighted fusion penalties would perform variable selection and encourage adjacent CNVs to share similar effect size.

This package has 3 main functions:

  • prep(): Data preprocessing and format conversion.

  • cvfit_WTSMTH(): Model fitting and effect estimate with cross-validation(CV). The CV procedure is to tune an optimal model by selecting the best pair of candidate tuning parameters.

  • fit_WTSMTH(): Model fitting and effect estimate with a given pair of tuning parameters.

We have a more detailed tutorial for all functions using an example data included in the CNVreg package.

Please see the CNVreg vignette for a quick tour of the CNVreg package.

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Version

Install

install.packages('CNVreg')

Monthly Downloads

144

Version

1.0

License

GPL-3

Maintainer

Shannon Holloway

Last Published

March 10th, 2025

Functions in CNVreg (1.0)

prep

Prepare Data for Analysis
cvfit_WTSMTH

Penalized Regression with Lasso and Weighted Fusion Penalties with Cross-Validation
CNVCOVY

Simulated data with copy number variants (CNV), Covariates (Cov), and outcomes traits (Y_QT for a continuous outcome and Y_BT for a binary outcome)for the illustration of CNV association analysis with penalized regression in CNVreg.
fit_WTSMTH

Penalized Regression with Lasso and Weighted Fusion Penalties with Given Parameters