Genevieve Stein-O’Brien

The paper shows that prematurity disrupts cerebellar development by altering developmental trajectories and causing lineage-specific asynchrony in cell maturation.

CMAC estimates absolute transcriptional age of cardiomyocytes using deep learning, achieving high accuracy across multiple datasets and platforms.

The paper identifies 17 consensus transcriptomic programs in ccRCC using NMF and links them to cellular sources and clinical outcomes, revealing converging trajectories toward aggressive disease.

The paper introduces the Neural Impact Score (NIS) to evaluate drug effects on brain organoids across multiple biological categories, demonstrating its utility in cross-species drug benchmarking.

This study evaluates spatial transcriptomics foundation models' ability to capture multi-scale spatial variation using SAFFRON, finding that one model better captures global gradients but not local patterns.

The paper compiles a large transcriptomic dataset of neocortical development and uses joint matrix decomposition to identify conserved transcriptome dynamics across mammals and developmental stages.

The paper compares Wasserstein critics and discriminators for single-cell data integration, finding Wasserstein critics offer better mixing but risk over-correction, while discriminators provide more stable results.

CycleGRN infers gene regulatory networks from single-cell RNA-seq data by modeling cell cycle dynamics as a stochastic differential equation, achieving strong performance on synthetic and biological datasets.

The paper improves gene regulatory network inference by integrating cell cycle positions into oscillatory network analysis, showing better performance for early progenitor cells.

The paper introduces a framework for mathematical modeling of multicellular systems using interpretable cell behavior rules derived from biological knowledge and multi-omics data.

This study models prematurity-associated insults in mice to show how different insults and their sequence alter cerebellar development and lead to distinct behavioral outcomes.

This paper introduces mNSF, a framework for analyzing multi-sample spatial transcriptomics data that identifies factors, shared regions, and biological functions without requiring spatial alignment.

This study uses spatial multi-omics to identify TLS-associated immune niches in PDAC that correlate with improved survival in immunotherapy responders.

The paper introduces computational tools for NMF analysis of single-cell RNA-seq data, demonstrating their use in quantifying cell state transitions in pancreatic cancer.

This study identifies two transcription factors that regulate neocortical size by controlling progenitor cell behavior and gene expression, with implications for microcephaly.

This paper analyzes the limitations of RNA velocity estimates by examining how different workflow steps affect the vector field and concludes that RNA velocity should not be interpreted as predicting future cellular states.

This paper introduces SpaceMarkers, a method to infer molecular changes from cell-cell interactions in spatial transcriptomics data, applied to tumor-immune interactions in metastasis and immunotherapy.

This paper simulates molecular cell states over time to study gene and protein expression dynamics and their regulatory relationships.

SJD identifies shared molecular variation across multi-omics datasets by decomposing matrices with hierarchical structure, applied to neurogenesis RNA-seq data.

RNAForecaster uses neural ODEs to predict future gene expression states in single cells from temporal transcriptomic data.

The paper introduces tricycle, a method to predict cell cycle position from single-cell RNA-seq data using transfer learning, showing it generalizes across datasets and species.

The paper introduces 2s-LCA, a method for joint analysis of multiple biological data sets, and applies it to human brain development data to identify shared gene expression patterns.

The paper introduces scProject and projectionDrivers to identify gene-wise differences in latent space projections across cell types and species in single-cell data, validated on mouse and human retinal development.

The paper identifies transcriptomic differences in human pluripotent stem cells that correlate with retinoic acid response and neural fate bias.

The paper identifies that parvocellular oxytocin neurons are critical for autism-related social reward behavior and show enrichment of autism risk genes in these neurons.

The paper models the progression of human neural stem cells in vitro, showing they transition through organizer states before committing to cortical excitatory or inhibitory neuronal fates.

projectR enables transfer learning for genomics data analysis using PCA, NMF, correlation, and clustering, with an example in spatial single-cell analysis.

The paper introduces scCoGAPS and projectR to decompose cell identity into shared and species-specific factors, enabling transfer learning across datasets and species.

CancerInSilico simulates bulk and single-cell gene expression data from mathematical models to enable benchmarking of time course analysis methods with known ground truth.

This study uses scRNA-Seq to analyze human and mouse retinal development, identifying conserved and species-specific gene expression patterns and a novel role for ATOH7 in photoreceptor specification.