PQLseq: Efficient Mixed Model Analysis of Count Data in Large-Scale Genomic Sequencing Studies

An efficient tool designed for differential analysis of large-scale RNA sequencing (RNAseq) data and Bisulfite sequencing (BSseq) data in the presence of individual relatedness and population structure. 'PQLseq' first fits a Generalized Linear Mixed Model (GLMM) with adjusted covariates, predictor of interest and random effects to account for population structure and individual relatedness, and then performs Wald tests for each gene in RNAseq or site in BSseq.

Version:	1.2.1
Depends:	R (≥ 2.10)
Imports:	Rcpp (≥ 0.12.14), foreach, doParallel, parallel, Matrix, methods
LinkingTo:	Rcpp, RcppArmadillo
Published:	2021-06-06
Author:	Shiquan Sun, Jiaqiang Zhu, Xiang Zhou
Maintainer:	Jiaqiang Zhu <jiaqiang at umich.edu>
License:	GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
Copyright:	see file COPYRIGHTS
NeedsCompilation:	yes
CRAN checks:	PQLseq results

Documentation:

Reference manual:

PQLseq.pdf

Downloads:

Package source:	PQLseq_1.2.1.tar.gz
Windows binaries:	r-devel: PQLseq_1.2.1.zip, r-release: PQLseq_1.2.1.zip, r-oldrel: PQLseq_1.2.1.zip
macOS binaries:	r-release (arm64): PQLseq_1.2.1.tgz, r-oldrel (arm64): PQLseq_1.2.1.tgz, r-release (x86_64): PQLseq_1.2.1.tgz, r-oldrel (x86_64): PQLseq_1.2.1.tgz
Old sources:	PQLseq archive

Linking:

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