Assessing reliability of learning Gaussian graphical models from microbial abundances

Published

January 18, 2024

As part of my Master’s program at BiRC, I proposed to explore the reliability of microbial network inference. This topic, which is gaining popularity, has many overlooked issues and open challenges. In particular, I dug into the usage of Gaussian graphical models. I evaluated the performance of SPIEC-EASI (a popular method with more than 1300 citations at the time of writing) and a novel Bayesian method based on BDgraph.

The report, slides and all source code are publicly available on GitHub if you want to know more!