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 |
| In views: |
Omics |
| CRAN checks: |
PQLseq results [issues need fixing before 2025-10-17] |
Documentation:
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