research-article

Leveraging Mutual Information for Functional Annotation Analysis of Microglia in Alzheimer's Disease

Authors:

Seung-Hyun Hong,

Dong-Guk ShinAuthors Info & Claims

BCB '24: Proceedings of the 15th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics

Article No.: 14, Pages 1 - 10

https://doi.org/10.1145/3698587.3701468

Published: 16 December 2024 Publication History

BCB '24: Proceedings of the 15th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics

Leveraging Mutual Information for Functional Annotation Analysis of Microglia in Alzheimer's Disease

Pages 1 - 10

Abstract
Supplemental Material
References

Abstract

Single-cell sequencing technologies have been advancing rapidly, enabling scientists to identify cell subtypes in tissues and study their interrelationships at the single-cell level. Current algorithms can effectively differentiate cell subgroups when gene expression values of known markers vary greatly, yet they are inadequate for classifying cells based on functionality because functionality analysis cannot be achieved by examining a handful number of marker genes. To overcome this limitation, we propose a novel clustering method specifically designed to identify functional subtypes. This method employs mutual information to dissect interrelationships among gene expression patterns of functionally aggregated groups of genes, known as Biological Processes. We applied our clustering method to 12 publicly available Alzheimer's Disease (AD) related single-cell mRNA datasets and evaluated if our method can identify additional functional roles of various subtypes of cells such as resting microglia, transitional microglia, disease associated microglia type 1 and type 2---a key research topics in current AD studies. Our method not only confirms previously known functional relationships among these subtypes but also discovers new and more specific ones. This method offers a new way of subgrouping cells, and it can aid scientists in labeling cell subtypes with functional nomenclature.

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References

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Cited By

Zhang CWang HHong SYan RShin D(2024)FuncNet: A Machine Learning Framework Capable of Enhancing Neural Cell Type Classification via Functional Subtype Clustering2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)10.1109/BIBM62325.2024.10821858(1758-1762)Online publication date: 3-Dec-2024
https://doi.org/10.1109/BIBM62325.2024.10821858

Index Terms

Leveraging Mutual Information for Functional Annotation Analysis of Microglia in Alzheimer's Disease
1. Applied computing
  1. Life and medical sciences
    1. Computational biology
      1. Computational genomics

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cover image ACM Conferences

BCB '24: Proceedings of the 15th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics

November 2024

614 pages

ISBN:9798400713026

DOI:10.1145/3698587

Copyright © 2024 ACM.

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Published: 16 December 2024

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NIH (National Institutes of Health)

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BCB '24: 15th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics

November 22 - 25, 2024

Shenzhen, China

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Cited By

Zhang CWang HHong SYan RShin D(2024)FuncNet: A Machine Learning Framework Capable of Enhancing Neural Cell Type Classification via Functional Subtype Clustering2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)10.1109/BIBM62325.2024.10821858(1758-1762)Online publication date: 3-Dec-2024
https://doi.org/10.1109/BIBM62325.2024.10821858

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