Cited By
View all- Yousuff MBabu RRathinam A(2024)Nonlinear dimensionality reduction based visualization of single-cell RNA sequencing dataJournal of Analytical Science and Technology10.1186/s40543-023-00414-015:1Online publication date: 11-Jan-2024
A comparison of dimensionality reduction methods for large biological data
Authors: 
Ashley Babjac, Taylor Royalty, Andrew D Steen, and Scott J EmrichAuthors Info & Claims
Ashley Babjac, Taylor Royalty, Andrew D Steen, and Scott J EmrichAuthors Info & ClaimsAugust 2022
Article No.: 15, Pages 1 - 7
Abstract
Large-scale data often suffer from the curse of dimensionality and the constraints associated with it; therefore, dimensionality reduction methods are often performed prior to most machine learning pipelines. In this paper, we directly compare autoencoders performance as a dimensionality reduction technique (via the latent space) to other established methods: PCA, LASSO, and t-SNE. To do so, we use four distinct datasets that vary in the types of features, metadata, labels, and size to robustly compare different methods. We test prediction capability using both Support Vector Machines (SVM) and Random Forests (RF). Significantly, we conclude that autoencoders are an equivalent dimensionality reduction architecture to the previously established methods, and often outperform them in both prediction accuracy and time performance when condensing large, sparse datasets.
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Index Terms
- A comparison of dimensionality reduction methods for large biological data
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August 2022
549 pages
ISBN:9781450393867
DOI:10.1145/3535508
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Published: 07 August 2022
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BCB '22: 13th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics
August 7 - 10, 2022
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View all- Yousuff MBabu RRathinam A(2024)Nonlinear dimensionality reduction based visualization of single-cell RNA sequencing dataJournal of Analytical Science and Technology10.1186/s40543-023-00414-015:1Online publication date: 11-Jan-2024
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