Valentine Svensson

The paper curates a single-cell RNA-seq catalog with known coregulation relationships to evaluate normalization methods for identifying gene coregulation.

The paper introduces Rhaister, a perturbation-response predictor that uses summary statistics to predict drug responses, outperforming virtual-cell models in speed and accuracy.

The paper improves SCVI by addressing low-UMI cell representation issues through self-supervised augmentation, preserving cell identity and variation at lower UMI depths.

The paper introduces Tahoe-x1, a large-scale perturbation-trained single-cell foundation model with 3 billion parameters, achieving state-of-the-art performance in cancer-related tasks.

A 100-million-cell atlas: 50 cancer lines pooled as spheroid villages, treated with 379 drugs at several doses, profiled by combinatorial-barcoding scRNA-seq and embedded with a 10-dimensional scVI model.

scvi-tools provides a Python package for probabilistic analysis of single-cell omics data, offering standardized interfaces and extensible tools for various analysis tasks.

The paper creates a comprehensive atlas of mouse primary motor cortex cell types using integrated transcriptomic and epigenomic data from over 600,000 samples.

The paper introduces an interpretable variational autoencoder for single-cell RNA-seq to identify gene programs in large datasets.

The paper extends RNA velocity to incorporate protein data, enabling temporal cell state inference from single-cell multiomics experiments.

The paper identifies CD44 as a marker and regulator of haematopoietic stem cell development through single-cell transcriptomics and functional experiments.

The paper investigates zero-values in droplet-based scRNA-seq data and finds they align with count statistics, suggesting biological variation rather than technical limitations.

The paper curates a database of single-cell transcriptomics studies to analyze trends in the field and provides insights into cell type identification.

The paper quantifies the tradeoff between sequencing depth and cell number in single-cell RNA-seq, finding that deeper sequencing per cell provides greater benefits than increasing cell numbers beyond 15,000 reads per cell.

SpatialDE identifies spatially variable genes from spatial transcriptomics and single-cell data, revealing genes with disease implications missed by conventional methods.

The paper uses single-cell RNA sequencing and computational modeling to resolve CD4+ T cell fate bifurcation during Plasmodium infection, showing external influence on fate decisions by monocytes.

This paper evaluates the sensitivity and accuracy of scRNA-seq protocols using spike-ins and a standardized pipeline, comparing sequencing depth trade-offs and RNA degradation effects.