STAT 548 PhD Qualifying Papers.
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Table of Contents

Introduction

I am interested in almost all problems in computational biology and genomics. I expect a student to propose novel statistical approaches that can address challenges in data analysis and modelling of high-dimensional, large-volume biological problems.

Feel free to contact me (ypp@stat.ubc.ca).

Format

You may organize your report including the following sections.

  • Problem definition (1 page): Extract mathematical/statistical problems from the paper and organize them. What are the input data? What is the expected output?

  • Significance (1 paragraph): Why is this an interesting problem? What can be learned by studying this problem? Why is it exciting for you? Author contribution: How did the author(s) find the solution? What was a novel contribution beyond traditional approaches?

  • Limitations/challenges (1 paragraph): What are the assumptions? Are they realistic? What are the technical limitations that the authors acknowledge or not?

  • Novel idea/methods (1-2 pages): Propose your idea and statistical methods. You could interpret the underlying problem in a different formulation. What are related problems/frameworks, but not adopted by the authors?

  • Results (1-2 pages): Include one figure that sketches your approaches. Show tables and figures that clearly demonstrate your methods.

  • Discussion (1 page): Briefly discuss what you have learned and what you would achieve if you were to develop this to a full paper. How would you validate your findings in independent studies, including wet-lab experiments?

Available Papers

  1. Madrigal, A., Lu, T., Soto, L. M., & Najafabadi, H. S. (2024). A unified model for interpretable latent embedding of multi-sample, multi-condition single-cell data. Nature Communications, 15(1), 6573.

  2. Qiu, Y., Sun, J., & Zhou, X.-H. (2023). Unveiling the unobservable: Causal inference on multiple derived outcomes. Journal of the American Statistical Association, 1–12.

Papers claimed by other students

  1. Bridgeford, E. W., Powell, M., Kiar, G., Noble, S., Chung, J., Panda, S., Lawrence, R., Xu, T., Milham, M., Caffo, B., & Vogelstein, J. T. (2024). When no answer is better than a wrong answer: a causal perspective on batch effects. In bioRxiv (p. 2021.09.03.458920).

  2. Demirel, I., Alaa, A., Philippakis, A., & Sontag, D. (2024). Prediction-powered Generalization of Causal Inferences. International Conference on Machine Learning, 10385–10408.