Charity W. Law

The paper develops automated immunohistochemistry protocols to detect necroptotic pathway proteins in mouse and human tissues, revealing heterogeneity and disease-related changes in expression.

The paper benchmarks spatial transcriptomics technologies using a multi-sample dataset from mouse spleen tissue, finding improved data quality with probe-based capture methods and CytAssist.

The paper introduces two methods to handle group heteroscedasticity in pseudo-bulk single-cell RNA-seq data, showing improved error control and power compared to existing methods.

The paper benchmarks long-read RNA-sequencing tools using synthetic data to evaluate isoform detection and differential expression analysis performance.

The paper develops FLT-seq and FLAMES to characterize full-length isoforms in single cells of human and mouse, identifying thousands of unannotated isoforms and linking them to functional modules and drug resistance mutations.

Glimma 2.0 enhances interactive RNA-seq analysis with reproducible R Markdown integration and improved visualization tools.

This study analyzes nanopore long-read RNA-seq data using short-read tools to assess differential gene and isoform expression in synthetic and mouse datasets.

The paper demonstrates that intron reads from RNA-seq are informative and introduces methods to analyze them for transcriptional changes and intron retention.

The paper benchmarks gene set enrichment methods using a curated compendium of disease datasets, identifying performance differences and providing recommendations for improved analysis.

Glimma provides interactive graphics for gene expression analysis to enhance data exploration and sharing between bioinformaticians and biologists.

limma couples gene-wise linear models with empirical Bayes variance moderation and weights; voom extends it to RNA-seq, and added functions test differential splicing and gene sets.