1/5 New in Cell Systems: our commentary revisits a recent Nature debate over memory-associated gene-expression changes in single-cell data. The debate centered on whether reported differential-expression (DE) signals were statistically reliable.

I am deeply honored by this recognition from the ISCB, which would not have been possible without the immense trust and hard work of my trainees. I am also thankful for the strong support of my colleagues and administrative team.

Fred Hutch Biostatistics Program@fredhutchbiostat.bsky.social · 4mo ago

Congratulations to @jsb-ucla.bsky.social on joining the 2026 Class of @iscb.bsky.social Fellows! 🎉 Her work on single-cell & spatial omics benchmarking (scDesign), FDR methods, and the central dogma has reshaped the field. A truly deserved honor. #ISCBFellows #Bioinformatics #Genomics

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.

Nullstrap-DE: A General Framework for Calibrating FDR and Preserving Power in DE Methods, with Applications to DESeq2 and edgeR

Differential expression (DE) analysis is a key task in RNA-seq studies, aiming to identify genes with expression differences across conditions. A central challenge is balancing false discovery rate (F...

arxiv.org

Fred Hutch Biostatistics Program@fredhutchbiostat.bsky.social · 7mo ago

🔥 Congrats to @jsb-ucla.bsky.social lab for two papers accepted to the highly selective #RECOMB2026 🎉 🧪 Nullstrap-DE — FDR calibration while preserving power in DE methods 🧬 scDesignPop — population-scale scRNA-seq data generation for power + benchmarking + privacy #ComputationalBiology #Genomics

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...

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.

nature.com

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

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...

genomebiology.biomedcentral.com

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

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

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...

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...

nature.com