Softwares

I have developed and currently maintain the following software. You can get the latest version from my GitHub repository.

  • prclust: R package that provides two algorithms for fitting the penalized regression-based clustering (PRclust). The corresponding paper is Wu, Kwon, Shen and Pan, 2016.

  • MiSPU: R package that presents a novel global testing method called aMiSPU, that is highly adaptive and thus high powered across various scenarios, alleviating the issue with the choice of a phylogenetic distance. The corresponding paper is Wu, Chen, Kim and Pan, 2016.

  • GLMaSPU: R package that makes it incredibly easy to implement some testing methods under high-dimensional generalized linear models. The corresponding paper is our 2019 Stat Sinica paper.

  • glmtlp: R package that makes it easy to implement the truncated lasso penalty under a generalized linear model framework. This package is similar to glmnet but can be applied with a non-convex penalty.

To help researchers from other field use our newly developed method, we have created and maintained the following software and pipeline. We assume no prior knowledge in R and all the following software can be run easily and smoothly once the required packages are installed successfully. Please send me an email if you have any troubles when using them.

  • IWAS: A software for implementing Imaging-Wide Association Studies (IWAS). The corresponding paper is our 2017 NeuroImage paper.

  • TWAS-aSPU: A more powerful gene-based association test to integrate single set or multiple sets of eQTL data with GWAS individual-level data or summary statistics. The corresponding paper is our 2017 Genetics paper.

  • aSPUpath2: A new pathway-based method for integrating eQTL data with GWAS summary statistics. This can be viewed as an extension of TWAS to the pathway-based analysis. The corresponding paper is our 2018 Genetic Epidemiology paper.

  • egmethyl: A new gene-based test for integrating enhancer-promoter interactions and DNA methylation data with GWAS summary data. The corresponding paper is our 2019 Bioinformatics paper.