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PQLseq  

Efficient Mixed Model Analysis of Count Data in Large-Scale Genomic Sequencing Studies
View on CRAN: Click here


Download and install PQLseq package within the R console
Install from CRAN:
install.packages("PQLseq")

Install from Github:
library("remotes")
install_github("cran/PQLseq")

Install by package version:
library("remotes")
install_version("PQLseq", "1.2.1")



Attach the package and use:
library("PQLseq")
Maintained by
Jiaqiang Zhu
[Scholar Profile | Author Map]
First Published: 2018-06-10
Latest Update: 2021-06-06
Description:
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.
How to cite:
Jiaqiang Zhu (2018). PQLseq: Efficient Mixed Model Analysis of Count Data in Large-Scale Genomic Sequencing Studies. R package version 1.2.1, https://cran.r-project.org/web/packages/PQLseq. Accessed 10 Apr. 2025.
Previous versions and publish date:
1.0 (2018-06-10 17:22), 1.1 (2018-08-06 06:20), 1.2 (2020-05-02 17:00)
Other packages that cited PQLseq R package
View PQLseq citation profile
Other R packages that PQLseq depends, imports, suggests or enhances
Complete documentation for PQLseq
Functions, R codes and Examples using the PQLseq R package
Some associated functions: ExampleBSseq . ExampleRNAseq . PQLseq-package . 
Some associated R codes: PQLseq.R . RcppExports.R . hello.R .  Full PQLseq package functions and examples
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