I develop and maintain freely-available, open-source software packages in R/Bioconductor/CRAN/GitHub to analyze high-throughput genomics data. They are available through R cran, Bioconductor or GitHub.

• DSS Usage Stats
  (Dispersion Shrinakge for Sequencing): differential analysis for count-based sequencing data. It detectes differentially expressed genes (DEGs) from RNA-seq, and differentially methylated regions (DMRs) from bisulfite sequencing (BS-seq) data. DSS is one of the mostly cited packages for DMR detection, with >10,000 downloads each year. Available on Bioconductor. Associated publications on Nucleic Acids Research, Nucleic Acids Research, and Quantitative Biology.

• ISLET Usage Stats
  ISLET (Individual-Specific ceLl typE referencing Tool) can deconvolute mixture samples and obtain the individual-specific and cell-type-specific reference panels, for repeatedly measured subjects’ bulk data. Available on Bioconductor. Associated publications on Genome Biology and Genome Medicine.

• magpie Usage Stats
  magpie (m6A genome-wide power inference) can perform statistical power analysis for the RNA methylation (m6A MeRIP-seq) study. It evaluates FDR, FDC, power, and precision under various study design parameters, including sample size, sequencing depth, and testing method. It can also produce power evaluation results into .xlsx files and generate power figures. Available on Bioconductor. Associated publication on PLOS Computational Biology.

• cypress Usage Stats
  cypress is the first experimental design and statistical power evaluation tool in cell-type-specific Differential Expression analysis. This tool can reliably model purified cell-type-specific (CTS) profiles, cell-type compositions, biological and technical variations, offering a high-fidelity simulator for bulk RNA-seq convolution and deconvolution. cypress conducts simulation and evaluates the impact of influencing factors, to help researchers optimize experimental design and conduct power analysis. Associated publication on Bioinformatics.

• NeuCA Usage Stats
  (Neural-network based Cell type Annotation): R/Bioconductor package for single-cell RNA-seq data cell type annotation, using neural-network approaches. NeuCA is flexible and adjust the classification method it will adopt, depending on cell types’ correlation level. Available on Bioconductor. Associated publications on Scientific Reports and Bioinformatics.

• InfiniumPurify
  R CRAN package for the estimation and adjustment for tumor purity in cancer methylation data analysis, available on R CRAN. Associated publications on Bioinformatics and Genes & Diseases.

• cfDNAmethy
  Reference-free and reference-based models for disease prediction by cell-free DNA methylation, available on GitHub. Associated publication on Briefings in Bioinformatics.