Dimensionality Reduction for Visualizing Spatially Resolved Profiling Data Using Spasne

27 Pages Posted: 9 Jan 2024

See all articles by Lin Xu

Lin Xu

University of Texas at Dallas - Southwestern Medical Center

Yuansheng Zhou

University of Texas at Dallas - Southwestern Medical Center

Chen Tang

University of Texas at Dallas - Southwestern Medical Center

Xue Xiao

University of Texas at Dallas - Southwestern Medical Center

Xiaowei Zhan

University of Texas at Dallas - Southwestern Medical Center

Tao Wang

University of Texas at Dallas - Quantitative Biomedical Research Center; University of Texas at Dallas - Kidney Cancer Program

Guanghua Xiao

University of Texas at Dallas - Southwestern Medical Center

Abstract

Background and objectiveSpatially resolved profiling technologies to quantify transcriptomes, epigenomes, and proteomes have been emerging as groundbreaking methods for comprehensive molecular characterizations. Dimensionality reduction and visualization is an essential step to analyze and interpret spatially resolved profiling data. However, state-of-the-art dimensionality reduction methods for single cell sequencing data, such as the t-SNE and UMAP, were not tailored for spatially resolved profiling data. MethodsHere we developed a spatially resolved t-SNE (SpaSNE) method to integrate both spatial and molecular information. We applied it to a variety of public spatially resolved profiling datasets that were generated from three experimental platforms and consisted of cells from different diseases, tissues, and cell types. ResultsTo compare the performances of SpaSNE, t-SNE, and UMAP, we applied them to four spatially resolved profiling datasets obtained from three distinct experimental platforms (Visium, STARmap, and MERFISH) on both diseased and normal tissues. Comparisons between SpaSNE and these state-of-the-art approaches reveal that SpaSNE achieves more accurate and meaningful visualization that better elucidates the underlying spatial and molecular data structures. ConclusionsThis work demonstrates the broad application of SpaSNE for reliable and robust interpretation on cell types based on both molecular and spatial information, which can set the foundation for many subsequent analysis steps, such as differential gene expression and trajectory or pseudotime analysis on the spatially resolved profiling data.

Keywords: Spatially resolved omics, dimensionality reduction, low dimensional visualization, molecular data structure, spatial organization of cells

Suggested Citation

Xu, Lin and Zhou, Yuansheng and Tang, Chen and Xiao, Xue and Zhan, Xiaowei and Wang, Tao and Xiao, Guanghua, Dimensionality Reduction for Visualizing Spatially Resolved Profiling Data Using Spasne. Available at SSRN: https://ssrn.com/abstract=4689296 or http://dx.doi.org/10.2139/ssrn.4689296

Lin Xu (Contact Author)

University of Texas at Dallas - Southwestern Medical Center ( email )

Dallas, Texas, TX
United States

Yuansheng Zhou

University of Texas at Dallas - Southwestern Medical Center ( email )

Dallas, Texas, TX
United States

Chen Tang

University of Texas at Dallas - Southwestern Medical Center ( email )

Dallas, Texas, TX
United States

Xue Xiao

University of Texas at Dallas - Southwestern Medical Center ( email )

Dallas, Texas, TX
United States

Xiaowei Zhan

University of Texas at Dallas - Southwestern Medical Center ( email )

Tao Wang

University of Texas at Dallas - Quantitative Biomedical Research Center ( email )

2601 North Floyd Road
Richardson, TX 75083
United States

University of Texas at Dallas - Kidney Cancer Program ( email )

2601 North Floyd Road
Richardson, TX 75083
United States

Guanghua Xiao

University of Texas at Dallas - Southwestern Medical Center ( email )

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
40
Abstract Views
188
PlumX Metrics