Dimensionality reduction for visualizing spatially resolved profiling data using SpaSNE.

ABSTRACT:

Background

Spatially 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-distributed stochastic neighbor embedding (t-SNE) and uniform manifold approximation and projection (UMAP), were not tailored for spatially resolved profiling data.

Results

Here 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 dat

SUBMITTER: Zhou Y 

PROVIDER: S-EPMC11831803 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature