Lukas Mikolaj Simon

This study creates a single-nucleus multiome atlas of sEOAD brain regions, identifying key transcription factors and regulatory elements linked to disease.

FADVI integrates single-cell and spatial omics data by disentangling batch effects from biological signals, outperforming existing methods across multiple datasets.

This study identifies let-7 as a regulator of AT2 cell differentiation and fibrosis prevention by controlling oncogenic networks and epigenetic changes.

This study uses single-cell multi-omics to map regulatory programs during mouse secondary palate development, identifying key transcription factors involved in anterior and posterior palatal mesenchymal differentiation.

deCS improves cell type annotation in scRNA-seq data by using a comprehensive human cell type reference atlas, enhancing accuracy and reducing computation time.

EmptyNN improves scRNA-seq quality by removing cell-free droplets and recovering lost cells using positive-unlabeled learning, achieving high AUROC scores and outperforming existing methods.

INSCT integrates large scRNA-seq datasets across experiments, platforms, and species using a deep learning framework, outperforming existing methods in scalability and accuracy.

This study shows that SMARCA4 regulates spatially restricted metabolic changes in 3D multicellular tissues by altering DNA accessibility and gene expression.

The paper creates a comprehensive atlas of RNA transcriptomes across 20 human biofluids, revealing significant differences in RNA concentration and composition, and validates potential biomarkers in disease cohorts.

DrivAER identifies driving transcriptional programs in scRNA-seq data by scoring gene sets for their relevance to pseudotemporal ordering or disease status.

The paper uses single-cell RNA-seq to identify Krt8+ progenitor cells involved in lung regeneration and shows their role in epithelial differentiation and disease.

This paper introduces PAGA, a method that reconciles clustering with trajectory inference by creating a topology-preserving graph map of single-cell data, demonstrating its effectiveness across multiple datasets.

The paper maps aging in the lung using single cell transcriptomics and proteomics, identifying novel age-related changes in cell types and the extracellular matrix.

This paper introduces a deep count autoencoder (DCA) for denoising scRNA-seq data, showing it improves analysis quality and speed compared to existing methods.

MetaMap provides an interactive webtool to explore metatranscriptomic data from human disease-related RNA-seq studies, enabling visualization and statistical analysis of microbial and viral reads.

The paper develops a pipeline to detect microbial and viral reads in human RNA-seq data and creates the MetaMap resource for disease-related microbiome analysis.