I hope that Nullstrap-DE can be a useful add-on to calibrate the FDR control of popular parametric DE methods, including DESeq2 and edgeR, while maintaining the strong power. Looking forward to feedback and comments from the community.

We will post the scDesignPop preprint soon.

3/3 With mcRigor, downstream analyses become more reliable:
✅ gene co-expression modules
✅ enhancer–gene associations
✅ temporal expression patterns

Benchmarking shows MetaCell & SEACells are better-performing methods.

2/3 At the core of mcRigor is a feature-correlation statistic with a null derived from a double permutation scheme — a brilliant idea by my postdoc Pan Liu jsb-lab.org/people/pan-l...

mcRigor detects dubious metacells and optimizes method & hyperparameter for a specific dataset.

mcRigor: a statistical method to enhance the rigor of metacell partitioning in single-cell data analysis - Nature Communications Aggregating similar single cells into metacells is a common heuristic for sparse data, but risks mixing dissimilar cells. Here, authors present mcRigor, which detects and filters heterogeneous metacells, optimizes metacell partitioning, and improves reliability in single-cell omics studies.

1/3 Metacells boost power in single-cell RNA-seq & multiome analysis. But without checking homogeneity, they risk forming dubious metacells that bias discoveries.

We introduce mcRigor: a statistical safeguard for rigorous metacell analysis.
👉 www.nature.com/articles/s41...

ClipperQTL: ultrafast and powerful eGene identification method - Genome Biology A central task in expression quantitative trait locus analysis is to identify cis-eGenes, i.e., genes whose expression levels are regulated by at least one local genetic variant. Existing cis-eGene id...

Excited to share our method ClipperQTL published in Genome Biology.
Built on our p-value-free FDR control framework Clipper, ClipperQTL performs on par with FastQTL and runs up to 500× faster.
Big thanks to my former PhD student Heather Zhou!
genomebiology.biomedcentral.com/articles/10....
#eQTL

I’m deeply thankful for the incredible support and opportunities at UCLA over the past 12 years—ever since finishing my PhD. Grateful for the journey so far and looking forward to this new chapter at the intersection of statistics and biomedical science.

I’m honored to join Fred Hutch as Professor and Program Head of Biostatistics, and as the Donald and Janet K. Guthrie Endowed Chair in Statistics. Excited to be part of a deeply collaborative and scientifically vibrant community with a rich legacy of impact. @fredhutchbiostat.bsky.social

Thanks, Emily! It is unfortunate that I couldn’t be there in person, but I’m glad that you found my talk useful.

I’ll speak on July 16 (2:30pm ET) at NSF@75: Advancing Statistical Science for a Data‑Driven World Conference, by ASA & Instats
My talk is about an info-theoretic criterion (ITCA) for combining ambiguous class labels: jmlr.org/papers/v23/2...
Free registration: instats.org/seminar/nsfa...

@guggfellows.bsky.social I’m deeply honored to be named a 2025 Guggenheim Fellow—especially as part of the Foundation’s historic 100th class. Grateful to be in the company of so many brilliant artists, scholars, and scientists. #guggfellows2025

Categorization of 34 computational methods to detect spatially variable genes from spatially resolved transcriptomics data - Nature Communications In spatial transcriptomics data analysis, identifying spatially variable genes (SVGs) is crucial for understanding tissue organization and function. The authors categorize 34 computational methods for...

How do we detect spatially variable genes (SVGs) in spatial transcriptomics?

In our Nature Communications review, we categorize 34 computational methods into three categories:
✅ Overall SVGs
✅ Cell-type-specific SVGs
✅ Spatial-domain-marker SVGs

www.nature.com/articles/s41...