Application of Dimension Reduction Methods to High-Dimensional Single-Cell 3D Genomic Contact Data

Zilin Wang; Ping Zhang; Weicheng Sun; Dongxu Li

doi:10.62762/TIOT.2024.186430

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Volume 2, Issue 1, IECE Transactions on Internet of Things

Volume 2, Issue 1, 2024

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Jinchao Chen

School of Computer Science, Northwestern Polytechnical University, Xi’an 710129, China

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IECE Transactions on Internet of Things, 2024, Volume 2, Issue 1: 20-25

Free Access | Research Article | 12 February 2024

Application of Dimension Reduction Methods to High-Dimensional Single-Cell 3D Genomic Contact Data

Zilin Wang 1 *

Ping Zhang 1,2

Weicheng Sun 1

Dongxu Li 2

1 College of Informatics, Huazhong Agricultural University, Wuhan 430070, China

2 School of Computer, BaoJi University of Arts and Sciences, Baoji 721016, China

* Corresponding author: Zilin Wang, email: WZLin3288@finmail.com

DOI: 10.62762/TIOT.2024.186430

Received: 13 December 2023, Accepted: 28 January 2024, Published: 12 February 2024

Abstract

The volume and complexity of data in various fields, particularly in biology, are increasing exponentially, posing a challenge to existing analytical methods, which often struggle with high-dimensional data such as single-cell Hi-C data. To address this issue, we employ unsupervised methods, specifically Principal Component Analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (t-SNE), to reduce data dimensions for visualization. Furthermore, we assess the information retention of the decomposed components using a Linear Discriminant Analysis (LDA) classifier model. Our findings indicate that these dimensionality reduction techniques effectively capture and present information not readily apparent in the original high-dimensional data, facilitating the visualization and interpretation of complex biological data. The LDA classifier's performance suggests that PCA and t-SNE maintain critical information necessary for accurate classification. In conclusion, our study demonstrates that PCA and t-SNE are powerful tools for visualizing and analyzing high-dimensional biological data, enabling researchers to gain new insights and understandings that are challenging to achieve with traditional approaches.

Graphical Abstract

Application of Dimension Reduction Methods to High-Dimensional Single-Cell 3D Genomic Contact Data

Keywords

Dimensionality reduction

Single-cell Hi-C

PCA

t-SNE

LDA

References

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APA Style

Wang, Z., Zhang, P., Sun, W., & Li, D. (2024). Application of Dimension Reduction Methods to High-Dimensional Single-Cell 3D Genomic Contact Data. IECE Transactions on Pattern Recognition and Intelligent Systems, 2(1), 20–25 https://doi.org/10.62762/TIOT.2024.186430

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