Regulatorymechanisms in complex tissues (Leader: Anjun Ma)

Our lab focuses on developing computational methods to discover heterogeneous transcriptional regulatory mechanisms from single-cell sequencing data. Our efforts are split between inferring cell-type-specific regulatory signals and constructing reliable gene regulatory networks via the integration of single-cell multi-omics. We are also going into the development of novel deep learning algorithms for enhancer-GRN construction and rare cell population discoveries in cases, such as aging cells and minimal residue diseases.

1. QUBIC2: a novel and robust biclustering algorithm for analyses and interpretation of large-scale RNA-Seq data. Bioinformatics (2019) [Abstract] [Full paper]
2. IRIS3: Integrated Cell-type-specific Regulon Inference Server from Single-cell RNA-Seq. Nucleic Acids Research (2020) [Abstract] [Full paper]
3. Single-cell biological network inference using a heterogeneous graph transformer. Nature Communications (2023) [Abstract] [Full paper]

Immuno informatics (Leader: Xiaoying Wang)

Single-cell multi-omics has brought transformative insights into immuno informatics, demonstrating success in describing novel immune subsets and defining important regulators of antitumor immunity. One significant challenge in immuno informatics is identifying the heterogeneity of immune cells in tumors and their differentiation process. To overcome these limitations, a scMulti-omics study can offer detailed identification of diverse immune subsets at a higher resolution and provide an opportunity to understand the contribution of immune cells to tumor progression. Our lab endeavors single-cell applications in immuno-oncological areas.

1. Androgen conspires with the CD8+ T cell exhaustion program and contributes to sex bias in cancer. Science Immunology (2022) [Abstract] [Full paper]
2. Selective targeting of GARP-LTGFβ axis in the tumor microenvironment augments PD-1 blockade via enhancing CD8+ T cell antitumor immunity. Journal for ImmunoTherapy of Cancer (2022) [Abstract] [Full paper]
3. Deep Transfer Learning of Cancer Drug Responses by Integrating Bulk and Single-cell RNA-seq data. Nature Communications (2022) [Abstract] [Full paper]

Graph representation learning of spatial omics data  (Leader: Yuzhou Chang)

Spatially resolved technologies (e.g., Visium or CosMx MSI) provide new ways to study cell-cell and cell-environment relations in human diseases at either the cellular or tissue level. It is challenging to understand how a tissue (or an organ) forms and organizes functional regions and the underlying cellular and molecular mechanism from spatial omics data. Our project aims to explore graph representation methods along with deep neural networks to investigate tissue structures and functional behaviors. The long-term goal is to harmonize cellular and tissue biology and demystify how Intra-/extra-cellular activity affects tissue development or disease progress from spatial and single-cell multi-omics.

1. Spatial omics representation and functional tissue module inference using graph Fourier transform. bioRxiv (2023) [Abstract] [Full paper]
2. Define and visualize pathological architectures of human tissues from spatially resolved transcriptomics using deep learning. Computational and Structural Biotechnology Journal (2022) [Abstract] [Full paper]
3. MAPLE: A Hybrid Framework for Multi-Sample Spatial Transcriptomics Data. bioRxiv (2023) [Abstract] [Full paper]

Neurodegenerative disease (Leader: Qi Guo)

Single-cell RNA-sequencing (scRNA-seq) and single-nucleus RNA-sequencing (snRNA-seq) studies have provided remarkable insights into understanding human brains. Our lab takes the advantage of single-cell multi-omics and spatial transcriptomic data to discover the mystery of the molecular mechanisms in neural systems and the pathogenesis of Alzheimer’s disease (AD).

1. scGNN is a novel graph neural network model for single-cell RNA-Seq analysis. Nature Communications (2021) [Abstract] [Full paper]
2. Microglia coordinate cellular interactions during spinal cord repair in mice. Nature Communications (2022) [Abstract] [Full paper]
3. Spatially resolved transcriptomics reveals unique gene signatures associated with human temporal cortical architecture and Alzheimer’s pathology. Acta Neuropathologica Communications (2022) [Abstract] [Full paper]

Microbiome and host interactions (Leader: Yingjie Li)

We are developing enabling tools and databases, using metagenomic and metatranscriptomic data, to elucidate microbial systems and their interactions with human diseases. Our goal is to deliver reproducible and integrated analyses of complex gut microbiome data, detect functional and taxonomic abundances (biomarkers) in a microbial community, and infer significant associations between clinical measurements of human diseases and transformed microbial biomarkers.

1. An explainable graph neural framework to identify cancer-associated intratumoral microbial communities. bioRxiv (2023) [Abstract] [Full paper]
2. MetaQUBIC: a computational pipeline for gene-level functional profiling of metagenome and metatranscriptome. Bioinformatics (2019) [Abstract] [Full paper]
3. Computational methods and challenges in analyzing intratumoral microbiome data. Trends in Microbiology (2023) [Abstract] [Full paper]

AI-enhanced analysis in kidney disease (Leader: Yi Jiang)

In kidney research, High-throughput Spatial Transcriptomics (HST) offers an unprecedented depth of understanding into tissue functionality, spatial organization, and the dynamics of disease progression. However, due to the complex heterogeneity of kidney tissues, it has significant analytical challenges to investigate certain kidney diseases. Our research endeavors are focused on the innovation and application of deep-learning algorithms to enhance the analysis of emerging HST datasets. This computational approach aims to yield more accurate and insightful biological interpretations, particularly in the fields of kidney diseases such as chronic kidney disease (CKD) and acute kidney injury (AKI).
1. Define and visualize pathological architectures of human tissues from spatially resolved transcriptomics using deep learning. Computational and Structural Biotechnology Journal (2022) [Abstract] [Full paper]
2. MAPLE: A Hybrid Framework for Multi-Sample Spatial Transcriptomics Data. bioRxiv (2023) [Abstract] [Full paper]
3. Dimension-agnostic and granularity-based spatially variable gene identification. Research Square (2023) [Abstract] [Full paper]