Description

Algorithmic Advances for Single-Cell and Spatial Omics Data Analysis With recent technological breakthroughs in single-cell and spatial transcriptomics, as well as spatial proteomics and other spatially resolved omics platforms, researchers now have access to molecular data at unprecedented resolution in both cellular identity and spatial context. These technologies have opened new frontiers in understanding tissue organization, developmental biology, and disease pathology. However, extracting meaningful insights from these complex, high-dimensional, and often noisy datasets requires the development of novel computational methods and algorithmic frameworks. To date, a wide array of algorithms has been proposed for tasks such as cell type deconvolution, spatial domain detection, gene-gene interaction modeling, cell-cell communications, and integrative multi-modal analysis. As spatial and single-cell data types become increasingly diverse, scalable and robust computational approaches are needed to bridge modalities, leverage spatial information, and reveal previously inaccessible layers of biological regulation. We invite investigators to contribute Original Research articles focused on designing, applying, or benchmarking algorithms for analyzing single-cell and spatial omics data. Topics of interest include, but are not limited to, the following:

● Computational methods for analyzing single-cell and spatial transcriptomics data

● Algorithms for spatial domain and tissue architecture identification

● Methods for inferring cell-cell communication and signaling networks in spatial context

● Methods for exploring the tumor microenvironment from spatial transcriptomics data

● Joint analysis of spatial transcriptomics, images and spatial proteomics data, or other omics data

● Machine learning and deep learning approaches for multi-omics integration at single-cell resolution

● New tools for trajectory inference and lineage tracing with spatial information

● Data-driven frameworks for 3D reconstruction and spatial modeling of tissues

● Denoising, imputation, deconvolution and normalization methods tailored to spatial and single-cell data

● Algorithmic innovations for cross-sample alignment, batch correction, and reference mapping

● Applications of advanced computational methods in developmental biology, neuroscience, immunology, and cancer research

This special issue aims to highlight the state-of-the-art in algorithmic development for spatial and single-cell omics, and to foster a community of researchers pushing the boundaries of computational biology through innovative tools and integrative analysis.

Important Dates

Oct 15, 2025: Due date for workshop paper submission

Nov 10, 2025: Notification of paper acceptance to authors

Nov 23, 2025: Camera-ready of accepted papers

Dec 15-18, 2025: Workshops

Program Chairs

Xin Maizie Zhou, Department of Biomedical Engineering and Computer Science, Vanderbilt University
Eric Lu Zhang, Department of Computer Science, Hong Kong Baptist University
Wenji Ma, Shanghai Jiao Tong University School of Medicine
Xiaoqi Zheng, Shanghai Jiao Tong University School of Medicine

Program Committee Members

Zixuan Cang, Department of Mathematics, North Carolina State University
Yanxiang Zhao, Department of Mathematics, George Washington University
Xian Fan Mallory, Department of Computer Science, Florida State University
Yunfei Hu, Department of Computer Science, Vanderbilt University
Wenjun Shen, Department of Bioinformatics, Shantou University
Zhenmiao Zhang, Department of Computer Science, UCSD
Chao Yang, Department of Computer Science, Hong Kong Baptist University
Xikang Feng, School of Software, Northwestern Polytechnic University
Dan Wang, Department of Data Science, Beijing Normal-Hong Kong Baptist University
Jiaxing Chen, Department of Computer Science, Beijing Normal-Hong Kong Baptist University
Xiaofei Zhang, Department of Mathematics, Central China Normal University
Qihuang Zhang, Department of Epidemiology, Biostatistics and Occupational Health, McGill College
Can Luo, Department of Biomedical Engineering, Vanderbilt University
Zhenhan Lin, Department of Computer Science, Vanderbilt University

Workshop Submission Requirement

Please submit a full-length paper (up to 8 page IEEE 2-column format ) through the online submission system (you can download the format instruction here. Electronic submissions (in PDF or Postscript format) are required. Selected participants will be asked to submit their revised papers in a format to be specified at the time of acceptance.

Workshop Online Submission:

Submit a paper Click here