Andreas Tjärnberg

This study uses single-nucleus RNA-seq, ATAC-seq, and Multiome to analyze Alzheimer's disease across multiple brain regions, revealing shared and region-specific cellular vulnerabilities.

scVIP models single-cell transcriptomes to predict phenotypes and infer trajectories in development and disease.

MapMyCells maps unlabeled single-cell datasets to reference brain taxonomies, enabling cross-study integration and reproducible cell-type annotation.

The paper shows that transcriptome maturation during the cell cycle enables multilineage competence in cardiopharyngeal progenitors by coupling fate decisions with cell cycle phases.

The paper introduces pseudovelocity, a method to estimate gene-specific RNA velocity using kNN-G and diffusion-based pseudotime, improving gene-based velocity estimation for downstream analysis.

TabVI improves cell type annotation and integration using a probabilistic transformer model, producing interpretable feature attention masks and robust performance across dataset sizes.

This study examines enhancer dynamics and spatial organization in the human spinal cord to understand cellular state transitions and identity.

This paper introduces a deep learning framework for gene regulatory network inference and latent transcription factor activity estimation, showing improved performance over existing methods.

This paper develops a deep learning model to estimate gene regulatory networks and RNA kinetics from single-cell RNA-seq data, showing improved recovery of known regulatory relationships and predictive power.

The paper introduces Inferelator 3.0, a tool for inferring gene regulatory networks from single-cell data, showing improved performance on yeast and mouse brain datasets.

This paper introduces DEWAKSS, a method for denoising single-cell genomics data by optimally tuning kNN- and diffusion-based approaches to preserve biological variance and improve clustering.

The paper infers a gene regulatory network (GRN) from perturbation experiments in cancer cells, identifying both known and novel regulatory interactions that contribute to oncogenic processes.

The paper uses a deep auto-encoder to discover disease-related gene modules from transcriptional data without relying on prior biological networks, finding significant enrichment of GWAS genes in the third layer.

LASSIM infers genome-wide gene regulatory networks using mechanistic ODE models, demonstrating its effectiveness in modeling Th2 differentiation with integrated data and siRNA experiments.