Tianming Zhou

POPARI: Modeling multisample variation in spatial transcriptomics

This paper introduces a probabilistic model for analyzing multisample spatial transcriptomics data, identifying spatial metagenes and interactions across tissues.

Genetically Defined Organoid Models Reveal Mechanisms Driving Squamous Cell Neoplastic Evolution and Identify Potential Therapeutic Vulnerabilities

Concurrent profiling of multiscale 3D genome organization and gene expression in single mammalian cells

This paper introduces GAGE-seq, a single-cell method that simultaneously profiles 3D genome structure and gene expression, revealing their relationship in defining cell identity and lineage commitment.

Ultrafast and interpretable single-cell 3D genome analysis with Fast-Higashi

Fast-Higashi improves scHi-C analysis by jointly identifying cell identities and chromatin meta-interactions with tensor decomposition and a novel algorithm, enhancing rare cell type detection and developmental trajectory reconstruction.

Multiscale and integrative single-cell Hi-C analysis with Higashi

Higashi improves scHi-C data analysis by enhancing quality and extracting 3D genome features, revealing cell-to-cell variability and new gene regulation connections in human prefrontal cortex cells.

SPICEMIX: Integrative single-cell spatial modeling of cell identity

The paper introduces a new method for analyzing spatial transcriptome data to improve cell type inference and spatial pattern analysis.