Research Interests

Artificial intelligence (AI) and single-cell studies have been making waves in the science and technology communities. AI offers a broad range of methods that can be used to investigate diverse data- and hypothesis-driven questions in single-cell biology. The highly heterogeneous nature of single-cell data can be analyzed across a wide range of research topics by generalizing deep-learning model design and optimization in a hypothesis-free manner. Our lab focuses on the research of single-cell multi-omics data, aiming to develop cutting-edge computational tools to discover underlying mechanisms in diverse biological systems.
Ma, Q., Xu, D. Deep learning shapes single-cell data analysis. Nat Rev Mol Cell Biol (2022)

Highlighted Projects



1. Regulatory mechanisms in complex tissues. 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.
GrantsR01GM131399-01 (PI), U54AG075931 (Core PI), NSF1945971 (PI)
2. Immuno-oncology. Single-cell multi-omics has brought transformative insights into immuno-oncology, demonstrating success in describing novel immune subsets and defining important regulators of antitumor immunity. One significant challenge in immuno-oncology 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.
GrantsR01CA262069-01 (Co-I), R01AI162779-02 (Co-I), U24CA252977 (MPI)
3. Tissue module in human diseases. 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.
GrantsR21HG012482 (MPI)
4. Neurodegenerative disease. 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).
Grants: R01MH124870-02 (Co-I), R01AG075092 (Co-I), R01DK126008-02 (Co-I)
5. Microbiome and host interactions. 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.
Grants: R01MH129589-01 (Co-I), CCTS Pilot (PI; 2020 completed)

Recent news

[Feb-6-2023] The manuscript “Single-cell biological network inference using a heterogeneous graph transformer” is officially accepted for publication in Nature Communications!

[Jan-28-2023] The manuscript “Explainable deep hypergraph learning modeling the peptide secondary structure prediction” has been accepted for publication in Advanced Science!

[Jan-25-2023] Mrs. Jia Qu has joined BMBL as a Student Research Assistant. Welcome!

[Jan-25-2023] The manuscript “Computational methods and challenges in analyzing intratumoral microbiome data” has been accepted for publication in Trends in Microbiology!

[Jan-6-2023] The manuscript “Promoting patient engagement in cancer genomics research programs: An environmental scan” has now been accepted for publication in Frontiers in Genetics!

[Jan-5-2023] Dr. Qin Ma gave an invited presentation “Graph Fourier Transform for tissue module identification from spatial multi-omics” at the Pelotonia Institute for Immuno-Oncology (PIIO) experimental IO group meeting.

[Dec-16-2022] Dr. Anjun Ma gave an invited presentation “Predicting Drug Response at the Single-Cell Level in Cancer Drug Therapy Using Deep Transfer Learning” at the School of Artificial Intelligence in Jilin University.

[Dec-13-2022] The manuscript “Spatially resolved transcriptomics reveals genes associated with the vulnerability of middle temporal gyrus in Alzheimer’s disease” has now been accepted for publication in Acta Neuropathologica Communications!

[Dec-9-2022] Dr. Qin Ma was awarded Excellence in Faculty Research in the Department of Biomedical Informatics for the year 2022. Congratulations!

[Dec-9-2022] Cankun Wang was awarded Excellence in Team Science (staff) in the Department of Biomedical Informatics for the year 2022. Congratulations!

[Dec-8-2022] Our collaborative study “Single-cell transcriptomics reveals impaired human cardiac cell lineage determination and cardiomyocyte proliferation due to NOTCH1 deficiency” has been officially accepted by Circulation Research, featuring Cankun Wang as the co-first author. Congratulations!

[Dec-6-2022] Dr. Qin Ma will give an invited presentation “Graph Representation Learning of Gene Expression Data” at the Nankai University International E-Forum on Artificial Intelligence and Robotics on Dec 9, 2022. Register now!

[Nov-16-2022] Jianying Li presented “Harnessing Anti-Tumor Metabolic Sensing Switch GPR84 on Macrophages for Cancer Immunotherapy” and received the second place poster award at the Pelotonia Institute for Immuno-Oncology (PIIO) Fourth Annual Immuno-Oncology Symposium. Congratulations!

[Nov-16-2022] Dr. Qin Ma gave an invited presentation “The use of single-cell multi-omics in immuno-oncology” at the Pelotonia Institute for Immuno-Oncology (PIIO) Fourth Annual Immuno-Oncology Symposium.

[Nov-4-2022] Dr. Qin Ma presented “The Immuno-Oncology Informatics Group” at OSU Comprehensive Cancer Center IMDP/SR directors meeting.

[Nov-1-2022] Dr. Qin Ma gave an invited presentation “AI methods development in multi-omics” for OSU AI retreat.

[Oct-28-2022] Dr. Qin Ma will give an invited presentation “Deep learning shapes single-cell and spatial transcriptomics data analysis” at the 10x Genomics Workshop on November 1, 2022. Register now!

[Oct-28-2022] The manuscript “NIH SenNet Consortium: Mapping the landscape of senescent cells throughout the human lifespan to understand physiological health” has now been accepted for publication in Nature Aging!

[Oct-20-2022] Dr. Qin Ma gave an invited presentation “Bioinformatics Research Development and Updates” at the OSU College of Medicine Grants Management Office.

[Oct-19-2022] The manuscript “Deep Transfer Learning of Cancer Drug Responses by Integrating Bulk and Single-cell RNA-seq data” has been accepted for publication in Nature Communications and will be available online soon!

[Oct-18-2022] Yuzhou Chang gave an invited talk “Spatial transcriptomics algorithms and the trend of spatial omics” at MWACD 2022 annual meeting.

[Oct-10-2022] The manuscript “scGNN 2.0: a graph neural network tool for imputation and clustering of single-cell RNA-Seq data” has been accepted for publication in Bioinformatics and will be available online soon!

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