Single-cell stochastic gene silencing pattern-guided differential expression analysis uncovers regulatory relationships and predicts gene function.

This study introduces miTEA-HiRes, a method designed to facilitate the evaluation of miRNA activity at high resolution. The method applies to single cell transcriptomics, type-specific single cell populations, and spatial transcriptomics data.

A Bayesian inference framework improves RNA velocity estimates to make them more coherent and biologically interpretable.

In this Journal Club, Shila Ghazanfar highlights a seminal paper by Levsky et al. that paved the way for contemporary single-cell spatial transcriptomics.

Julie Chih-yu Chen is the head of Data Sciences at the National Microbiology Laboratory, Public Health Agency of Canada, and an Assistant Professor at the University of Manitoba, Canada. She develops and applies statistical and machine learning techniques to diverse omics data related to infectious disease.

FICTURE software addresses a critical challenge in spatial omics analysis: making high-resolution inference with only a few molecules per square micron. This tool fully realizes the potential of contemporary spatial platforms by learning latent spatial factors from the whole transcriptome while preserving the resolution of each technology at scale.

Single-cell bisulfite sequencing enables the genome-wide quantification of DNA methylation at single-cell resolution, but methods to analyze the resulting data are lacking. The MethSCAn software accurately distinguishes cell types and states by scanning the genome for informative regions and providing a robust approach to quantifying methylation within these regions.