New Paper! Machine learning models that attempt to predict microbial load collapse outside of their training context with an R2<0! In contrast, our Bayesian Partially Identified Models embrace uncertainty in unmeasured microbial load and consistently outpreform. www.biorxiv.org/content/10.1...

Excited to summarize our most recent paper, "Explicit Scale Simulation for analysis of RNA-sequencing count data with ALDEx2" on controlling the false discovery rate (FDR) when analyzing high throughput sequencing (HTS) data. This has been an open problem since the dawn of HTS.

New preprint! PCR bias doesn’t just distort relative abundances—it reshapes microbiome ecological analyses. We show that commonly used diversity metrics (e.g., UniFrac or Shannon) are not robust to amplification bias, while perturbation-invariant alternatives are. www.biorxiv.org/content/10.1...

New Paper: We relax normalizations to produce statistical methods for bioinformatics that are much more robust and powerful. We see FDR drop from 45% to 5% with increases in power! This adds to our ongoing work on Scale Reliant Inference. link.springer.com/article/10.1...

Our paper explaining why Gihawi et al. failed to prove an error in the normalization used by the 2020 cancer #microbiome analysis now out as a Matters Arising in @asm.org #mSystems (w/ @george-austin.bsky.social) 🖥️ 🧬 Thread explaining the key points below. journals.asm.org/doi/10.1128/...

New paper in Genome Biology! genomebiology.biomedcentral.com/articles/10.... We introduce scale models, a generalization of normalizations that explciitly account for uncertainty in biological system scale (e.g., microbial load).

🚨PA colleagues: "Senator Fetterman wants to hear from you about how the federal funding freeze is affecting Pennsylvania." "If your project has been impacted, please fill out our constituent impact form:" forms.office.com/g/mFv2JAPxpC Get out your Other Support and share that info!

The National Institutes of Health had to stop considering new grant applications, delaying funding for research into diseases ranging from heart disease and cancer to Alzheimer's and allergies.

Non-linear additive regression (using scalable Bayesian Multinomial Logistic Normal models) is now available in fido (on CRAN)! neurips.cc/virtual/2024... Also includes extreemly fast marginal likelihood estimation for hyperparameter tuning. cran.r-project.org/web/packages...

New paper was recently accepted to AIStats arxiv.org/abs/2410.05548 Flexible Multinomial Logistic-Normal time series models (state space models) that scale to extreemly large datasets. Inference is 5-6 orders of magnitude faster than alternatives. R package will soon be released.

Just saw this paper saying compositional data methods don't help in differential abudnance analysis. www.biorxiv.org/content/10.1.... We know, we have better methods that have been validated against datasets with ground truth. www.biorxiv.org/content/10.1... (thread)

Nice paper by Nishijima et al, from Bork's lab using machine learning to show that the absolute bacterial load is a major confounder in most human microbiome studies (www.cell.com/cell/fulltex.... Now seems a good time to outline the approaches that we have been developing.

@claesengroup.bsky.social I would love to join the starter pack -- I am just transitioning form twitter. Thanks!