ICCK Transactions on Internet of Things
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TY - JOUR AU - Wang, Zilin AU - Zhang, Ping AU - Sun, Weicheng AU - Li, Dongxu PY - 2024 DA - 2024/02/12 TI - Application of Dimension Reduction Methods to High-Dimensional Single-Cell 3D Genomic Contact Data JO - ICCK Transactions on Internet of Things T2 - ICCK Transactions on Internet of Things JF - ICCK Transactions on Internet of Things VL - 2 IS - 1 SP - 20 EP - 25 DO - 10.62762/TIOT.2024.186430 UR - https://www.icck.org/article/abs/TIOT.2024.186430 KW - Dimensionality reduction KW - Single-cell Hi-C KW - PCA KW - t-SNE KW - LDA AB - 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. SN - pending PB - Institute of Central Computation and Knowledge LA - English ER -
@article{Wang2024Applicatio,
author = {Zilin Wang and Ping Zhang and Weicheng Sun and Dongxu Li},
title = {Application of Dimension Reduction Methods to High-Dimensional Single-Cell 3D Genomic Contact Data},
journal = {ICCK Transactions on Internet of Things},
year = {2024},
volume = {2},
number = {1},
pages = {20-25},
doi = {10.62762/TIOT.2024.186430},
url = {https://www.icck.org/article/abs/TIOT.2024.186430},
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.},
keywords = {Dimensionality reduction, Single-cell Hi-C, PCA, t-SNE, LDA},
issn = {pending},
publisher = {Institute of Central Computation and Knowledge}
}
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