Also I forgot to add this but I am actively looking for postdocs in the general space of functional genomics, statistics, and genetics! Please reach out if you have open positions or interest starting around fall or winter of this year! (n+1/n)

I’d like to thank Adam Zahm and @justingenglish.bsky.social, who were great collaborators through this entire process, as well as my mentors @hjp.bsky.social and Sriram Sankararaman! (n/n)

keju has a whole suite of other features that account for multiple candidates targeting the same motif, infer effects from promoter composition, and generally improve inference. Read about them here: www.biorxiv.org/content/10.6... (7/n)

False Positive Rate, assessed in real data on negative controls with the negative control label masked.

keju’s improved sensitivity does not come with a corresponding trade-off in calibration. In fact, keju is also better calibrated than previous methods when benchmarking on negative controls with labels masked. (6/n)

Sensitivity in simulated data, compared to MPRAnalyze and BCalm.

Thanks primarily to these assumptions, keju shows substantial improvements in recovery of ground truth effects compared to previous methods (in simulated data). (5/n)

Batch-specific dispersion estimates show much less uncertainty than batch-agnostic dispersion estimates.

Another axis is batch structure. Different batches can be linked to different experimental conditions, treatments, or perturbations, and show substantial variation. Accordingly, keju further improves sensitivity through RNA batch-specific uncertainty estimation. (4/n)

Estimated dispersions in DNA counts are much higher than corresponding dispersions in RNA counts, indicating that uncertainty is much lower in the DNA counts. Grouping dispersion estimates (y=x line) further increases uncertainty in estimation.

One axis is modality. DNA counts in MPRAs are primarily functions of transfection, while RNA counts are downstream of many noisy biological processes. keju improves sensitivity by ignoring DNA count uncertainty, instead focusing on uncertainty estimation in the RNA counts. (3/n)

MPRA experimental designs can vary tremendously, which is challenging for statistical inference.

MPRAs offer exciting opportunities to link genetics to transcription through paired DNA and RNA observations. However, MPRAs vary in experimental design and batch structure, which each introduce different axes of uncertainty that complicate inference. (2/n)

My preprint on keju, a statistical tool for Massively Parallel Reporter Assay (MPRA) data, is out! keju improves sensitivity, calibration, and reliability over previous methods by closely modeling important uncertainty sources in MPRAs. Check it out: www.biorxiv.org/content/10.6... (1/n)

Happy to see this work out now in Genome Biology! Check out the final version here for your FACS DMS needs: link.springer.com/article/10.1...

go say hi to my friend Aditya!

Super excited to get this out. This collab started a few years ago and is the first paper from it. Here, with experimental and computational approaches we:

1. establish that cell villages can be just as accurate (one might argue more accurate!) than arrayed-based designs

bsky.app/profile/bior...

Cosmos: A Position-Resolution Causal Model for Direct and Indirect Effects in Protein Functions Multi-phenotype deep mutational scanning (DMS) experiments provide a powerful means to dissect how protein variants affect different layers of molecular function, such as abundance, surface expression...

How do we decouple the effects of two functional phenotypes in protein deep mutational scanning (DMS)?
Meet Cosmos, our new statistical framework for causal inference in multi-phenotype DMS.
www.biorxiv.org/content/10.1...
[1/n]

Accurate variant effect estimation in FACS-based deep mutational scanning data with Lilace Deep mutational scanning (DMS) experiments interrogate the effect of genetic variants on protein function, often using fluorescence-activated cell sorting (FACS) to quantitatively measure molecular ph...

Check out our new preprint on Lilace, a statistical tool for scoring FACS-based deep mutational scanning experiments! Lilace directly models the shift between variant fluorescence distributions and provides score uncertainty estimates to better assess reliability and reproducibility. (1/3)

Junior / Assistant Specialist: Kim Lab, Developmental & Cell Biology University of California, Irvine is hiring. Apply now!

I am hiring for a research technician role in my new lab at UC Irvine studying how transcription factors control cell identity. If you know any graduating undergrads or postbacs looking for a position, please spread the word! Apply by May 9 for full consideration. recruit.ap.uci.edu/JPF09594

Xue lab at UC Irvine Ecology and evolution in the human gut microbiome

Hi friends new and old! I study how microbes interact and evolve in complex communities like the human gut microbiome.🦠🧬💩 I'm thrilled to share that I'm starting a lab at UC Irvine in April 2025 and am recruiting at all levels - please spread the word! kxuelab.com More about my work below...🧵1/n

Rosace: a robust deep mutational scanning analysis framework employing position and mean-variance sh... bioRxiv - the preprint server for biology, operated by Cold Spring Harbor Laboratory, a research and educational institution

Meet Rosace, a robust deep mutational scanning analysis tool that incorporates positional information and mean-variance shrinkage. Check it out if you are running DMS experiments or handling DMS data! (1/n)
www.biorxiv.org/content/10.1...