Email: huang.tin@northeastern.edu
Advisor: Olga Vitek
Thesis Committee:
- Olga Vitek (Advisor, Northeastern University): https://www.khoury.northeastern.edu/people/olga-vitek/
- Predrag Radivojac (Internal committee member, Northeastern University):https://www.ccs.neu.edu/home/radivojac/
- Kylie Bemis (Internal committee member, Northeastern University):https://kuwisdelu.github.io/
- Nuno Bandeira (External committee member, University of California, San Diego):https://cseweb.ucsd.edu/~nbandeir/
Thesis proposal:
- Proposal Proposal of Ting Huang
- Title: Statistical Design and Analysis of Mass Spectrometry-Based Proteomic Experiments with Isobaric Labeling
- Abstract: Tandem mass tag (TMT) is a multiplexing technology widely-used in proteomic research. It enables relative quantification of proteins from multiple biological samples in a single mass spectrometry run with high efficiency and high throughput. However, experiments often require more biological replicates or conditions than can be accommodated by a single run, and involve multiple TMT mixtures and multiple runs. Such larger-scale experiments combine sources of biological and technical variation in patterns that are complex, unique to TMT-based workflows, and challenging for the downstream statistical analysis. These patterns cannot be adequately characterized by statistical methods designed for other technologies, such as label-free proteomics or transcriptomics. Therefore, there is a need for flexible statistical tools, which reflect diverse and complex designs of large-scale TMT experiments, and have good statistical performance. We first develop a general statistical approach for relative protein quantification in mass spectrometry-based experiments with TMT labeling and group comparison design. It is applicable to experiments with multiple conditions, multiple biological replicate runs, multiple technical replicate runs, and unbalanced designs. It is based on a flexible family of linear mixed-effects models that handle complex patterns of technical artifacts and missing values. The approach is implemented in {\it MSstatsTMT}, a freely available open-source R/Bioconductor package compatible with data processing tools such as Proteome Discoverer, MaxQuant, OpenMS and SpectroMine. We propose to extend the existing approach for group comparison design to more complex designs. In particular, we are interested in the repeated measures designs where protein abundance in a same subject is repeatedly measured for each condition in the TMT experiment.
A Statement from the Advisor:
Olga Vitek is Ting’s Ph.D. advisor, so Olga is the chair of Ting’s committee. Ting’s research interest is the experimental design and statistical analysis of mass spectrometry-based quantitative proteomics experiments. Her dissertation focuses on statistical and machine learning methods, and their open-source implementation for isobaric labeling-based multiplexed quantitative proteomics. Ting’s work relies on the peptide and protein identification from raw tandem mass spectra, an area that Predrag Radivojac works on and has several publications. Kylie Bemis’s research interests include machine learning and large-scale statistical computing for mass spectrometry imaging experiments so she can provide meaningful feedback at each defense stage of the dissertation process. The statistical models proposed by Ting needs to be evaluated on a variety of datasets. Nuno Bandeira’s team and Olga Vitek’s team collaborate to develop a database for large-scale deposition of data from quantitative mass spectrometry-based proteomic experiments, where Ting may find datasets for her model evaluation.