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

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.