Author

Mauminah Raina

Indiana University Indianapolis

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Bioinformatics· 4

Signature Assays, Organisms & Methods

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Assay:spatial transcriptomics· 4 Approach:statistical model· 3 Research Focus:differential expression· 2 Organism:human· 2 Research Focus:spatial domain detection· 2 Research Focus:benchmarking· 1 Research Focus:cell type annotation· 1 Research Focus:clustering· 1

Frequent Co-Authors

Juexin WangiD· 4 shared Qin MaiD· 4 shared Dong He XuiD· 3 shared Jinpu LiiD· 3 shared Michael T. EadoniD· 3 shared Yiqing WangiD· 3 shared Chunhui XuiD· 2 shared Li SuiD· 2 shared

Activity by Year

2023-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

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4 shared papersBioinformatics

Juexin WangORCID

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4 papers

Recent·Most cited

A Principled Statistical Framework for Analyzing Spatial Patterns in Spatially Resolved Multi-Omics

Jinpu Li, Mauminah Raina, Yiqing Wang, Shuai Zeng, Yang Yu, Xiufan Yu, Xiaojie Jin, Yuzhou Chang, Daniel Feliciano, Jonathan Himmelfarb, Ana C. Ricardo, Patrick H. Nachman, Miguel Vazquez, M. Luiza Caramori, Laura Barisoni, , Dong Xu

This paper introduces STORM, a statistical framework for analyzing spatial patterns in multi-omics data, demonstrating reliable performance in detecting spatial structures and supporting study design decisions.

bioRxiv · Aug 10, 2026 · PreprintBioinformatics

scBSP: A fast and accurate tool for identifying spatially variable features from high-resolution spatial omics data

Jinpu Li, Mauminah Raina, Yiqing Wang, Chunhui Xu, Li Su, Qi Guo, Ricardo Melo Ferreira, Michael T. Eadon, Qin Ma, Juexin Wang, Dong He Xu

Bioinformatics · Sep 29, 2025Peer-reviewedBioinformatics

scBSP: A fast and accurate tool for identifying spatially variable genes from spatial transcriptomic data

Jinpu Li, Yiqing Wang, Mauminah Raina, Chunhui Xu, Li Su, Qi Guo, Qin Ma, Juexin Wang, Dong He Xu

scBSP identifies spatially variable genes from spatial transcriptomic data efficiently and accurately across different sequencing techniques and resolutions.

bioRxiv · May 6, 2024 · PreprintBioinformatics

Exploring the Mosaic-like Tissue Architecture of Kidney Diseases Using Relation Equivariant Graph Neural Networks on Spatially Resolved Transcriptomics

Mauminah Raina, Hao Cheng, Hari Naga Sai Kiran Suryadevara, Treyden Stransfield, Dong He Xu, Qin Ma, Michael T. Eadon, Juexin Wang

This paper introduces REGNN, a graph deep learning framework for analyzing spatially resolved transcriptomics data, showing improved performance in identifying mosaic-like tissue architectures in kidney diseases.

bioRxiv · Nov 9, 2023 · PreprintBioinformatics