Huang Ting

440 Huntington Avenue · 310C West Village H · Boston, MA 02115 · huang.tin@northeastern.edu

I'm a PhD student from Northeastern University Khoury College of Computer Sciences, advised by Olga Vitek. I received my B.S. and M.S. degree in Software Engineering from Dalian University of Technology. My research focuses on experimental design and statistical analysis of mass spectrometry-based protein quantification experiments. I'm the main developer and maintainer of R/Bioconductor pacakge MSstatsTMT.

More information can be found in my CV.

Experience

Graduate Research Assistant

Olga Vitek's Lab, Northeastern University, USA

Develop statistical methods and open-source softwares for relative protein quantification and group comparison in mass spectrometry-based experiments with isobaric labeling (iTRAQ and TMT)

Develop simulation-based methods and open-source software for sample size estimation and optimal design of mass spectrometry-based proteomic experiments

05/2015-present

Summer intern

Research and development team, Biognosys AG, Switzerland

Design benchmarking experiments for relative protein quantification in data-independent acquisition (DIA).

Combine precursor and fragment information for improved detection of differential abundance in data independent acquisition (DIA).

05/2017-08/2017

Graduate Research Assistant

Olga Vitek's Lab, Purdue University, USA

Develop, evaluate and implement a statistical approach for identifying multivariate but robust, interpretable and accurate panels of gene biomarkers of drug therapy response.

08/2014-05/2015

Graduate Research Assistant

Zengyou He's Lab, Dalian University of Technology, China

Develop a linear programming model for protein inference problem in shotgun proteomics

Develop a constrained Lasso regression approach to utilize peptide detectability for protein inference problem in shotgun proteomics

09/2011-06/2014

Publications

  • T. Maculins, E. Verschueren, T. Hinkle, P. Chang, C. Chalouni, J. Lim, A. Katakam, R. Kunz, B. Erickson, T. Huang*, et al. Proteomics of autophagy deficient macrophages reveals enhanced antimicrobial immunity via the oxidative stress response. eLife, 2021.
  • T. Huang*, M. Choi, M. Tzouros, S. Golling, N. Pandya, B. Banfai, T. Dunkley and O. Vitek. MSstatsTMT: Statistical detection of differentially abundant proteins in experiments with isobaric labeling and multiple mixtures. Molecular & Cellular Proteomics, 19:1706, 2020.
  • T. Huang*, R. Bruderer, J. Muntel, Y. Xuan, O. Vitek and L. Reiter. Combining precursor and fragment information for improved detection of differential abundance in data independent acquisition. Molecular & Cellular Proteomics, 19:421, 2020.
  • M. Choi, J. Carver, C. Chiva, M. Tzouros, T. Huang*, et al. MassIVE.quant: A community resource of curated quantitative mass spectrometry-based proteomics datasets. Nature Methods, 17:981, 2020.
  • J. Muntel, J. Kirkpatrick, R. Bruderer, T. Huang*, O. Vitek, A. Ori and L. Reiter. Comparison of protein quantification in a complex background by DIA and TMT workflows with fixed instrument time. Journal of Proteome Research, 18:1340, 2019.
  • Z. He, T. Huang*, X. Liu, P. Zhu, B. Teng and S. Deng. Protein inference: A protein quantification perspective. Computational Biology and Chemistry, 63:21, 2016.
  • Z. He, T. Huang*, C. Zhao, B. Teng. Protein Inference. Modern Proteomics–Sample Preparation, Analysis and Practical Applications, 237-42, 2016.
  • B. Teng, T. Huang* and Z. He. Decoy-free protein-level false discovery rate estimation. Bioinformatics, 30:675, 2013.
  • T. Huang*, H. Gong, C. Yang and Z. He. ProteinLasso: a Lasso regression approach to protein inference problem in shotgun proteomics. Computational Biology and Chemistry, 43:46, 2013.
  • T. Huang* and Z. He. A linear programming model for protein inference problem in shotgun proteomics. Bioinformatics, 28:2956, 2012.
  • T. Huang*, J. Wang, W. Yu and Z. He. Protein inference: A review. Briefings in Bioinformatics, 13:586, 2012.

Software

MSstatsTMT

An open-source R/Bioconductor package for protein significance analysis in shotgun mass spectrometry-based proteomic experiments with tandem mass tag (TMT) labeling

Website, Bioconductor, Github

MSstatsSampleSize

An open-source R/Bioconductor package for optimal design of high-dimensional MS-based proteomics experiments

Website, Bioconductor, Github

Teaching

  • May Institute-Computation and statistics for mass spectrometry and proteomics. Two-week short course, Northeastern University, Boston MA. 2017(onsite), 2018(onsite), 2019(onsite), 2020(virtual), 2021(virtual).
  • Short course at the US HUPO conference about "Statistical Design and Analysis of Quantitative Proteomics Experiments with TMT Labeling: Case Studies with MSstatsTMT", virtual, 2021.
  • Short course at the Annual Conference of American Society for Mass Spectrometry (ASMS) about "Quantitative proteomics: case Studies", Atlanta GA, 2019.