Lingting Shi

The paper uses in vivo single-cell CRISPR screens to study how tumor suppressor loss and genetic perturbations affect glioma states and immune recruitment in mice.

SHERLOCK is a deep generative framework for single-cell perturbation analysis that learns interpretable perturbation representations and enables causal analysis of downstream effects.

The paper identifies an endodermal subpopulation in the chick embryo that transitions into neuromesodermal progenitors, contributing to multiple germ layers.

The paper identifies a T-cell receptor structural signature that predicts acute GVHD risk by analyzing alloreactive clonotypes and introduces a metric to quantify clonal dominance.

The paper develops a CRISPR screening framework to map kinase-dependent immune evasion mechanisms in glioblastoma by integrating single-cell transcriptomics and deep generative models.

Decipher is a deep generative model that integrates single-cell RNA-seq data to visualize and characterize transitions between normal and perturbed cell states across different diseases.

This study uses spatiotemporal single-cell analysis to track T cell clonal dynamics and phenotypic plasticity in GVHD, revealing links between TCR features, clonal expansion, and disease severity.

This paper introduces RefCM, a method that improves cell type annotation in scRNA-seq data using optimal transport and integer programming, achieving accurate cross-technology, cross-tissue, and cross-species mappings.

This study uses SCHEMATIC to analyze the molecular response of glioblastoma to EGFR inhibitors, identifying chemotype-specific programs and showing that combined EGFR/PI3K inhibition reduces adaptive transcription.

Starfysh integrates spatial transcriptomics and histology to analyze tumor microenvironments, identifying disease-specific spatial hubs and immune interactions in breast cancers.