Emma Dann

Genome-scale CRISPRi perturb-seq in 22 million primary human CD4+ T cells maps stimulation-specific regulators and links perturbation signatures to T cell states and autoimmune genetics.

This paper develops a virus-like particle-based system for CRISPR gene editing and screening in primary human myeloid cells, identifying TNFAIP3 as a key regulator of inflammatory polarization.

The paper identifies off-target effects in CRISPRi Perturb-seq experiments by analyzing transcriptional similarity between on-target and off-target guide effects.

Pertpy provides a modular framework for analyzing large-scale perturbation single-cell experiments, offering tools for metadata annotation and perturbation distance calculations.

The paper creates a regulon atlas mapping TF activity across human cell types and uses it to link disease signatures to cell types and TFs, improving disease risk locus analysis.

The paper integrates genetic associations with Perturb-seq data to build causal graphs linking genes to traits through regulatory programs.

The paper maps the spatial organization of the human thymus across developmental stages using a multimodal atlas and identifies key cellular and molecular features of T cell development.

multiDGD is a deep generative model for multi-omics data that learns shared representations and enables data integration and association detection.

The paper integrates single-cell RNA-seq datasets across the GI tract to identify epithelial metaplasia in inflammatory diseases, showing its role in inflammation through immune cell interactions.

Genes2Genes aligns single-cell trajectories using a Bayesian DP framework, revealing gene-level mismatches and highlighting differences in T cell differentiation between in vitro and in vivo states.

This study investigates how scRNA-seq data can predict the clinical success of drug targets by analyzing cell type and disease-specific expression patterns.

The paper introduces miloDE, a cluster-free method for differential expression testing in single-cell RNA sequencing data, improving sensitivity and specificity by avoiding discrete clustering.

The paper constructs a human fetal lung cell atlas from 5-22 post conception weeks, identifying new cell states and regulators of epithelial fates.

The paper introduces a method to determine X-inactivation states in single-cell transcriptomes and uses it to infer cellular phylogenies in human females.

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 maps the multi-organ functions of the human yolk sac during early development, revealing its roles in hematopoiesis, nutrition, and oxygen regulation.

cell2location uses a Bayesian model to integrate single-cell and spatial transcriptomics, mapping cell types in situ across mouse brain regions and human lymph node immune compartments.

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

This study uses single-cell RNA-sequencing to analyze fetal bone marrow development and identifies disruptions in hematopoiesis in Down syndrome.