Dr. Hao Feng’s research interests include biostatistics, bioinformatics and computational biology. Dr. Feng’s focus is to develop and apply novel biostatistics and bioinformatics methods to better understand high-throughput omics data, with an emphasis on epigenomics. Previously, Dr. Feng has developed methods to identify differential epigenetics biomarkers, retrieve personalized genomics deconvolution signals, classify cancer subtypes using epigenetics biomarkers, etc. Dr. Feng developed a number of open-source software tools that are freely available in R/Bioconductor, with over 10,000 downloads annually. These tools have been widely used in studies on various diseases. Dr. Feng also collaborate closely with physicians and wet-lab researchers to decipher signals from cancer genomics and epigenomics data.
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Teaching Information
Courses Taught
Research Information
Research Interests
- Biostatistics
- Bioinformatics
- Statistical methods development in -omics data
- Applications in computational biology
Professional Memberships
Publications
Publishing impact
Metrics from Web of Science/publons and/or Scopus/SciVal:
- H-index: 14
- Total publications from CV: 37
- Total citations: 1,701
Editorial roles
Reviewer for publications including:
- Annals of Applied Statistics
- Bioinformatics
- Biometrics
- BMC Bioinformatics
- Briefings in Bioinformatics
- Genome Biology
- Nucleic Acids Research
Education
Additional Information
Grants:
- NIH/NIGMS Maximizing Investigators' Research Award (MIRA)
1R35GM154862 (PI: Hao Feng)
Personalized genomics signal deconvolution to improve cell-type level inference.
Total cost: $1,925,620. Direct cost: $1,250,000.
09/01/2024 — 06/30/2029
Software Packages:
- ISLET: Individual-Specific cell type referencing tool. An R/Bioconductor package deconvolute mixture samples and obtain the individual-specific and cell-type-specific reference panels. Available on Bioconductor: https://bioconductor.org/packages/ISLET/.
- magpie: This package aims to perform power analysis for the MeRIP-seq study. It calculates FDR, FDC, power, and precision under various study design parameters. Available on Bioconductor: https://bioconductor.org/packages/magpie/.
- NeuCA: Neural network-based cell type annotation tool. Available at https://bioconductor.org/packages/NeuCA/.
- cypress: (cell-type-specific differential expression power assessment) is the first experimental design and statistical power evaluation tool in cell-type-specific Differential Expression analysis. Available at https://www.bioconductor.org/packages/cypress/.
- DSS: Dispersion Shrinkage for Sequencing. An R/Bioconductor package for differential analysis from high-throughput sequencing data, including differential expression for RNA-seq and differential methylation for bisulfite-sequencing data. Available at https://bioconductor.org/packages/DSS/.