Zongming Ma

REDCAT maps single-cell type-specific metabolic activities in tissues using multimodal all-optical imaging, revealing metabolic specialization and reprogramming in immune and liver tissues.

This paper introduces 3D-Omics-Flow, a method that reconstructs 3D molecular volumes from 2D spatial proteomics sections by repairing damaged sections and interpolating between them.

SuperFocus integrates histopathology with single-cell spatial multi-omics data from spot-based measurements, improving accuracy by 28-73% over existing methods.

Thermostable RNaseH release of barcoded cDNA keeps sections intact for CODEX imaging, yielding same-section whole-transcriptome and protein maps plus CODEX-guided spot deconvolution in FFPE tissues.

This paper introduces CellSNAP, a self-supervised method that integrates spatial, transcriptomic, and proteomic data to improve cell population delineation in spatial-omics datasets.

This paper introduces CellANOVA, a statistical model to recover biological signals lost during single-cell data integration by distinguishing batch effects from true biological variation.

The paper introduces MaxFuse, a method for integrating spatial and single-cell omics data with weak linkage, demonstrating its effectiveness in consolidating proteomic, transcriptomic, and epigenomic information.

The paper introduces MARIO, a cell matching algorithm that integrates single-cell proteomic and multi-modal data, revealing orchestrated immune responses in lung tissue.

This paper introduces MSNR, a multi-scale network regression method that models brain networks at multiple scales, showing improved interpretability and performance in linking brain connectivity to phenotypes.

The paper identifies brain-based dimensions of psychopathology using sCCA in youths, finding four dimensions linked to functional dysconnectivity that replicate in an independent dataset.