Belinda Phipson

Binning transcript and cell coordinates into spatial vectors lets jazzPanda call smaller, spatially concordant marker sets than Wilcoxon or t-tests on Xenium/CosMx data, while modelling replicates and negative-control background.

The paper identifies CD14+CD4+ T cells in NSCLC tumor microenvironment driven by TNF, linked to poor patient survival.

This study uses single-cell RNA sequencing to show that focal neuroendocrine differentiation (Focal NED) in prostate cancer is transcriptionally distinct, characterized by androgen receptor expression and unique transcriptional sub-populations.

This study shows that library size in spatial transcriptomics data confounds biological signals and recommends against normalizing it unless necessary.

SuperCellCyto efficiently analyzes large cytometry datasets by grouping similar cells into supercells, avoiding the need for random subsampling.

GLMsim simulates single cell RNA-seq data incorporating library size, biology, and batch effects, enabling benchmarking of integration methods.

The paper introduces NextClone and CloneDetective for extracting and quantifying lineage-tracing barcodes from scRNA-seq and DNA sequencing data.

propeller tests for differences in cell type proportions between experimental conditions using scRNA-seq data, showing significant changes in heart development, ageing, and COVID-19 severity.

The paper introduces two new methods for gene set testing in DNA methylation data and shows they outperform existing approaches.

The paper uses single-cell RNA-seq to analyze developing mouse kidney and human kidney organoids, identifying cell clusters and revealing insights into kidney development and organoid maturation.

STRetch detects pathogenic short tandem repeat expansions in whole genome sequencing data, revealing novel disease-causing STRs with low false discovery rate.

The paper introduces the scRNA-tools database to catalog and curate scRNA-seq analysis tools, highlighting trends in tool development and open-source practices.

Splatter simulates single-cell RNA-seq data using a gamma-Poisson model, offering reproducible and well-documented simulations for multiple cell types and differentiation paths.

This paper investigates gene length bias in scRNA-seq across different protocols and datasets, finding that UMI-based protocols reduce gene length bias compared to full-length transcript protocols.

The paper provides a Bioconductor workflow for analysing methylation array data, covering quality control, normalization, differential methylation analysis, and visualization.

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.

DiffVar detects differential variability in methylation data across sample groups, applied to cancer and aging datasets.