Email: huang.tin@husky.neu.edu
Advisor: Olga Vitek
Venue: Molecular & Cellular Proteomics
Impact factor: 4.828 (2018)
Additional Information:
The paper was published on Molecular & Cellular Proteomics (MCP). MCP is a monthly peer-reviewed scientific journal. It is considered as a top journal in proteomics area and its impact factor in 2018 is 4.828. More detailed information about my proposed model is in the Supplemental Fig. S1.
Contribution statement from Advisor:
Ting's paper "Combining Precursor and Fragment Information for Improved Detection of Differential Abundance in Data Independent Acquisition" was accepted to Molecular & Cellular Proteomics (MCP) on December 30, 2019. MCP is a premier journal in proteomics, with impact factor 4.828 in 2018. MCP publishes novel experimental and computational methods, and is the leader in setting standards for reproducible research.
This work was part of Ting's summer internship at Biognosis in 2017. Ting was responsible for the vast majority of the work in this paper. She developed a specialized instance of the linear mixed-effect models for interpreting these data (the core novel contribution of the manuscript). She developed a general implementation of the approach in R. She evaluated the proposed model on seven experimental datasets, which were generated by her internship hosts at Biognosis. The workflow is published open-source, and is now in the process of being included in Spectronaut, a commercial software package from Biognosis. Ting wrote the majority of the manuscript (with the specific focus on the statistical methods and evaluation), which was then edited by all the other co-authors.
Overall, this work demonstrated that an appropriate statistical modeling of heterogeneous mass spectrometry measurements, and it open-source implementation, allow us to maximize the information content extracted from quantitative mass spectrometry-based proteomic experiments.