Yi Zhang

Welcome to visit our lab’s 1st #AACR poster! STHD: ML/AI method for sub-cellular cell typing for spatial transcriptomics (eg. 2um spots in VisiumHD) by integrating scRNA reference. (Section45 Board27) #AACR2025 Recruiting-ML/AI for Omics at Duke!

We are recruiting Postdoc at Duke University! Join us to develop computational genomics & machine learning methods: www.linkedin.com/jobs/view/41...

We are recruiting postdocs and students! Interested in computational biology, machine learning, bioinformatics, cancer genomics, single-cell multi-omics, spatial transcriptomics, human genetics, and immunology? - Welcome to join us! yi-zhang-compbio-lab.github.io/join/ 10/

Our first lab preprint is impossible without the collaborative work with our amazing intern student Silas Chuhanwen Sun, and support from our Duke Departments of Neurosurgery and Biostatistics&Bioinformatics, Brain Tumor Center, and Brain Tumor Omics Program. 9/

STHD is scalable to millions of spots now common in high-resolution spatial omics; the algorithm design also works for other high-resolution, high gene-coverage spatial transcriptomics platforms - under development 8/

- Locally: identification of multi-cellular neighborhoods and cell-cell interactions, where gene expression mediating cell-cell communications are further revealed. E.g. Various immune hubs enriching different T-myeloid interactions; TLS structure seen in other tumor samples. 7/

The STHD cell labels enable many analyses- globally: segmentation of tissue structures and detection of cell type-specific differential genes/pathways. e.g. The inside tumor nodule expresses higher epithelial-specific stem genes, and higher macrophage-specific SPP1+ state. 6/

STHD enables scalable spatial inference and interactive exploration of cellular niches and immune hubs, facilitating genome-wide and cell type-specific spatial comparisons. One example full-size colon cancer VisiumHD generated by STHDviewer is at yi-zhang-compbio-lab.github.io/STHDviewer_c.... 5/

We show that STHD accurately infers cell type identities at subcellular level and guides cell type-specific binning, achieving high performance in benchmarks and preserving cell type-specific expression. Below is a small "tumor bagel" patch (~0.25%) from one colon cancer VisiumHD 4/

In details, STHD uses machine learning to model high-resolution spots with neighbor regularization, where the loss function addresses sparsity by modeling count statistics, incorporating neighbor similarities, and leveraging reference single-cell RNA-seq data. 3/

GitHub - yi-zhang/STHD: STHD: probabilistic cell typing of Single spots in whole Transcriptome spatial data with High Definition STHD: probabilistic cell typing of Single spots in whole Transcriptome spatial data with High Definition - yi-zhang/STHD

Current binning approaches create cell mixtures that complicate deconvolution. Instead, STHD uses ML/AI to achieve high-resolution cell typing and to discover cell type-specific neighborhoods and differential genes/pathways. STHD pipeline is available at github.com/yi-zhang/STHD/ 2/

STHD: probabilistic cell typing of single Spots in whole Transcriptome spatial data with High Definition Recent spatial transcriptomics (ST) technologies have enabled single- and sub-cellular resolution profiling of gene expression across the whole transcriptome. However, the transition to high-definition ST significantly increased data sparsity and dimensionality, posing computational challenges in identifying cell types, deciphering neighborhood structure, and detecting differential expression - all are crucial steps to study normal and disease ST samples. Here we present STHD, a novel machine learning method for probabilistic cell typing of single spots in whole-transcriptome, high-resolution ST data. Unlike the current binning-aggregation-deconvolution strategy, STHD directly models gene expression at single-spot level to infer cell type identities without cell segmentation or spot aggregation. STHD addresses sparsity by modeling count statistics, incorporating neighbor similarities, and leveraging reference single-cell RNA-seq data. We show in VisiumHD data that STHD accurately predicts cell type identities at single-spot level, which achieves precise segmentation of both global tissue architecture and local multicellular neighborhoods. The high-resolution labels facilitate various downstream analyses, including cell type-stratified bin aggregation, spatial compositional comparisons, and cell type-specific differential expression analyses. Moreover, STHD labels further reveal frontlines of inter-cell type interactions at immune hubs in cancer samples. STHD is scalable and generalizable across diverse samples, tissues, and diseases, facilitating genome-wide analyses in various spatial organization contexts. Overall, computational modeling of individual spots with STHD facilitates discoveries in cellular interactions and molecular mechanisms in whole-genome spatial technologies with high resolution. STHD is available at <https://github.com/yi-zhang/STHD>. ### Competing Interest Statement The authors have declared no competing interest.

Analyzing high-resolution spatial transcriptomics like VisiumHD? Sharing STHD, a machine learning algorithm for cell type mapping of every high-resolution (2um for VisiumHD) spot by integrating scRNA reference (Updated preprint from Version1 June 2024) : www.biorxiv.org/content/10.1.... 1/

High-resolution, noninvasive single-cell lineage tracing in mice and humans based on DNA methylation epimutations - Nature Methods This work presents a computational tool MethylTree to infer cell lineages based on epimutations on DNA methylation.

High-resolution, noninvasive single-cell lineage tracing in mice and humans based on DNA methylation epimutations

www.nature.com/articles/s41...

Very excited to announce that the single cell/nuc. RNA/ATAC/multi-ome resource from ENCODE4 is now officially public. This includes raw data, processed data, annotations and pseudobulk products. Covers many human & mouse tissues. 1/

www.encodeproject.org/single-cell/...

Excited to host Dr. Luca Pinello today at Duke to share insights on computational methods for single-cell genomics and CRISPR genome editing. Duke Single Cell Initiative seminar series: dmpi.duke.edu/duke-single-...

Excited to be visiting Duke University to share insights on computational methods for single-cell genomics and CRISPR genome editing! Thank you for hosting me
@yi-zhang-compbio.bsky.social 🙏