Chong Wu
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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. Please send me an email （cwu3@fsu.edu) if you have find any bugs 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.

We create a GitHub lab page for the software written by the group members

  • CMO: Cross Methylome Omnibus (CMO) integrates genetically regulated DNAm in enhancers, promoters, and the gene body to identify additional disease-associated genes.

  • FOGS: FOGS is a powerful fine-mapping method that prioritizes putative causal genes by accounting for local LD in TWAS results