Cross-study Bayesian Factor Regression in Heterogeneous High-dimensional Data

Add to your list(s) Download to your calendar using vCal

• Alejandra Avalos-Pacheco (University of Florence)
• Tuesday 24 May 2022, 14:00-15:00
• Room 119, Arts Building (R16) or zoom https://bham-ac-uk.zoom.us/j/95645638485?pwd=eDh3dlR3QjRlVWRHdExvaDBWdEo4UT09 Meeting ID: 956 4563 8485 Passcode: 190953.

If you have a question about this talk, please contact Hong Duong.

Analyses that integrate multiple studies are crucial to understand and gain knowledge in high-dimensional statistical research. However, such data are often not collected all at once but in batches. These batch effects can be complex, leading to systematic biases, causing distortions in both mean and variance. In this talk I will present a novel sparse latent factor regression model to integrate such heterogeneous data. The model provides a tool for data exploration via dimensionality reduction and sparse low-rank covariance estimation while correcting for a range of batch effects. I will discuss the use of several sparse priors (local and non-local) to learn the dimension of the latent factors. Our approach provides a flexible methodology for sparse factor regression which is not limited to data with batch effects. Our model is fitted in a deterministic fashion by means of an EM algorithm for which we derive closed-form updates, contributing a novel scalable algorithm for non-local priors of interest beyond the immediate scope of this talk. I will present several examples, with a focus on bioinformatics applications. Our results show an increase in the accuracy of the dimensionality reduction, with non-local priors substantially improving the reconstruction of factor cardinality. The results of our analyses illustrate how failing to properly account for batch effects can result in unreliable inference. Our model provides a novel approach to latent factor regression that balances sparsity with sensitivity in scenarios both with and without batch effects and is highly computationally efficient. Finally, I will discuss some interesting extensions and open questions of cross-study Bayesian latent factor models.

This talk is part of the Data Science and Computational Statistics Seminar series.

This talk is included in these lists:

• Data Science and Computational Statistics Seminar
• Room 119, Arts Building (R16) or zoom https://bham-ac-uk.zoom.us/j/95645638485?pwd=eDh3dlR3QjRlVWRHdExvaDBWdEo4UT09 Meeting ID: 956 4563 8485 Passcode: 190953
• School of Mathematics events

Note that ex-directory lists are not shown.