Gregor Sturm

Rectangle provides a scalable deconvolution framework for bulk RNA-seq using single-cell data, achieving high accuracy and robustness across diverse benchmarks.

This paper benchmarks second-generation single-cell-informed deconvolution tools for bulk RNA-seq, revealing performance differences based on data characteristics and providing an ecosystem called omnideconv.

The paper integrates single-cell data from CRC patients to identify immune subtypes and neutrophil phenotypes, revealing their spatial organization and tumor-induced changes.

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

The paper compares 13 mouse models of intestinal inflammation with human IBD datasets to identify translatable pathways and introduces a web tool for analyzing these models.

This study integrates multi-omic and multimodal data from IBD patients to identify molecular, histopathological, and clinical features associated with inflammatory bowel diseases.

This study benchmarks differential expression methods for single-cell data, finding no performance advantage for single-cell-specific methods over pseudobulk approaches like DESeq2.

This paper develops a signature, iHet, to capture tumor microenvironment heterogeneity linked to immune activity and evasion using transcriptomic data from lung cancer.

SimBu simulates bulk RNA-seq data with variable cell type composition to create in silico gold standards for evaluating deconvolution methods.

The paper analyzes 2,692 gene expression datasets to show tissue heterogeneity affects 5-15% of samples, advocating for quality control methods to address this issue.

Scirpy provides a Python toolkit for analyzing single-cell T-cell receptor data and integrating it with transcriptomic data.

The paper evaluates computational methods for estimating immune-cell composition from bulk RNA-seq data using simulated and real datasets, finding high accuracy for well-defined cell-type signatures.