John C. Marioni

The paper compares using cell atlases versus matched controls for identifying disease-associated cell states in scRNA-seq data, finding that combining atlas data with matched controls improves accuracy.

The paper uses single-cell RNA-sequencing to study heterogeneity in mouse gastruloids over time, revealing distinct mesodermal and neural differentiation trajectories and their implications for perturbation studies.

CellRegMap identifies context-specific regulatory variants using scRNA-seq by analyzing allelic effects across cell types and states.

SageNet predicts spatial positions and cell-cell proximities of dissociated single-cell data using a graph neural network trained on spatial references.

StabMap integrates mosaic single cell data by using non-overlapping features to stabilize mapping onto a common embedding.

The paper uses a CRISPRi screen in human fetal lung organoids to identify SOX9 as a key regulator of progenitor self-renewal, showing it primes cells to receive WNT and RTK signals.

geneBasis selects an optimal gene panel from scRNA-seq data by iteratively adding genes that maximize manifold distance, outperforming existing methods in resolving cell types and states.

Milo tests differential abundance in single-cell data using k-NN graphs to detect subtle cell state changes without relying on discrete clusters.

This paper uses spatial transcriptomics to study mouse organogenesis, revealing spatial gene expression patterns and cell fate decisions not captured by single-cell RNA sequencing.

The paper identifies coordinated step-changes in gene expression kinetics during erythropoiesis in both mouse and human, challenging assumptions of RNA velocity analysis.

This study identifies a human fetal lung progenitor cell population with alveolar fate potential and develops an organoid system to study alveolar development mechanisms.

The paper identifies extrinsic cues from chondrogenic progression that inhibit AER cell formation and limit limb regeneration in Xenopus tadpoles.

The paper uses single-cell RNA sequencing to study genetic variant effects on dopaminergic neuron differentiation from iPSCs, identifying eQTL and cell fate markers.

The paper introduces CELLO-seq, a long-read RNA sequencing method for single cells, to study TE expression in mouse and human cells, finding locus-specific expression and regulatory heterogeneity.

The paper introduces scHOT, a method to detect changes in higher order gene interactions in single-cell data, demonstrating its application in liver development and spatial gene correlation patterns.

MOFA+ integrates structured single-cell multi-modal data by modeling group and view structures, revealing coordinated gene expression and epigenetic changes during mouse gastrulation.

The paper uses single-cell RNA-seq of differentiating iPS cells from 125 donors to identify expression quantitative trait loci influencing gene expression during endoderm differentiation.

This study creates a transcriptomic atlas of olfactory mucosae across species to show evolutionary patterns in odor detection capabilities.

The paper introduces a statistical method to distinguish real cells from empty droplets in single-cell RNA sequencing data, improving detection of low-UMI cell libraries while controlling false discovery rates.

The paper creates a single-cell transcriptomic atlas of mouse spermatogenesis, identifying key developmental stages and X chromosome transcriptional dynamics.

The paper uses single cell triple-omics analysis to reveal how epigenetic changes drive germ layer specification in mouse embryos, showing distinct epigenetic priming and remodeling in different lineages.

This study shows that ageing alters the composition of γδ T cells in lymph nodes, promoting tumour growth in mice.

The paper introduces a method to measure expression variability in single-cell RNA sequencing data, revealing synchronized translational machinery activity in immune cells and new patterns in CD4+ T cell differentiation.

The paper quantifies barcode swapping in single-cell RNA-seq data and develops an algorithm to remove swapped reads, reducing artifacts in droplet-based experiments.

MOFA, a sparse Bayesian factor model with mixed likelihoods, separates shared from modality-specific multi-omic variation, recovering IGHV, trisomy 12 and oxidative-stress axes in 200 leukaemia samples.

This study compares the off-target effects of RNAi, antisense oligonucleotides, and CRISPRi in transcriptomic analyses, showing that all methods have non-negligible off-target effects and recommend using multiple LOF methods for functional validation.

This paper introduces a batch correction method for scRNA-seq data that matches mutual nearest neighbours, showing improved performance over existing methods on simulated and real data.

This paper introduces scNMT-seq, a method for joint profiling of chromatin accessibility, DNA methylation, and transcription in single cells, and validates it in differentiating mouse embryonic stem cells.

This paper introduces a scalable latent-factor model for scRNA-seq data that separates biological drivers from confounding effects using pathway annotations and improves gene set annotations.