Can Ergen

The paper introduces CSDE, a method that improves differential expression analysis in spatial transcriptomics by incorporating expert feedback to mitigate preprocessing biases.

The paper presents the Human Gut Cell Atlas v1.0, a unified reference of gut cell diversity, revealing how tissue structure influences cellular composition and enabling disease and organoid analysis.

ResolVI improves spatial transcriptomics by correcting molecule misassignment and batch effects, enhancing cell state distinction and cross-dataset integration.

immgenT creates a comprehensive T-cell molecular atlas across mouse organs and disease states, identifying 8 lineages and 107 clusters using deep generative models and integrating external datasets.

MrVI is a deep generative model that identifies sample-level stratifications and cellular differences without prior cell-type grouping, applied to COVID-19 and IBD data.

AMICI estimates cell-cell interaction length scales and links communication to gene programs in spatial transcriptomic data.

CytoVI is a deep generative model for antibody-based single cell technologies that improves integration, imputation, and analysis across different platforms and antibody panels.

Scvi-hub provides a platform for sharing and analyzing single-cell omics datasets using pre-trained models, enabling efficient tasks like visualization and annotation with reduced computational demands.

PEtracer maps cell state and lineage dynamics in vivo with high spatial resolution using prime editing and multimodal imaging.

scVIVA is a deep generative model that integrates cell-intrinsic and neighboring gene expression to produce embeddings and gene profiles, revealing spatially restricted tumor-promoting endothelial cells and niche-associated T cell states in breast cancer.

This paper introduces VI-VS, a framework for identifying conditionally dependent features in multiomic data while controlling false discovery rates.

DISCERN improves cell clustering and subtype detection by reconstructing missing gene expression, identifying novel T cell types in COVID-19 and predicting disease severity.

popV improves cell-type annotation by providing uncertainty scores and reducing manual inspection through ensemble voting.

The paper introduces DestVI, a method for multi-resolution spatial transcriptomics analysis that captures continuous cell state variation, and applies it to study inflammation in lymph nodes and tumors.

This study uses CITE-seq to analyze thymocyte development and identifies TCR signaling waves and the TCR/calcineurin/NFAT/GATA3 axis as drivers of CD4/CD8 lineage commitment.