Martin Treppner

Enhancement of network architecture alignment in comparative single-cell studies

This paper proposes a deep learning method to align neural network architectures across species for single-cell data, revealing commonalities and differences in gene expression.

Multi-omic Analysis of Primary Human Kidney Tissues Identifies Medulla-Specific Gene Expression Patterns

The paper identifies medulla-specific gene expression patterns in adult human kidney tissues using multi-omic data and corrects misassignments in the GTEx database.

The performance of deep generative models for learning joint embeddings of single-cell multi-omics data

This study evaluates deep generative models for integrating single-cell multi-omics data across varying sample sizes and provides recommendations for experimental design.

Designing Single-Cell RNA-Sequencing Experiments for Learning Latent Representations

This paper introduces scVIDE, a method to determine statistical power for cell group detection in scRNA-seq data by generating synthetic datasets and using permutation tests.

DOT1L activity affects cell lineage progression in the developing brain by controlling metabolic programs

This study shows that DOT1L activity regulates AP lineage progression in the developing mouse neocortex by controlling metabolic programs and gene expression.

Single-cell transcriptomic resolution of stem cells and their developmental trajectories in the hippocampus reveals epigenetic control of cell state perseverance

The paper uses single-cell RNA sequencing to study hippocampal development in DOT1L-mutant mice, identifying distinct neural stem cell populations and their developmental trajectories, revealing DOT1L's role in cell state preservation and hippocampal maturation.

Making many out of few: deep generative models for single-cell RNA-sequencing data

This study evaluates deep generative models for scRNA-seq data, finding that they can support experimental design but have limitations in capturing cell type proportions and bimodal gene distributions.