Regional analysis to delineate intrasample heterogeneity with RegionalST
Yue Lyu, Chong Wu, Wei Sun, Ziyi Li
TL;DR
RegionalST is a new computational tool that helps researchers analyze spatial transcriptomics data to better understand tissue heterogeneity and its implications for diseases like cancer.
Contribution
RegionalST introduces a one-stop solution for quantifying cell type mixtures, identifying sub-regions, and performing cross-region differential analysis in spatial transcriptomics.
Findings
RegionalST enables accurate visualization and analysis of diverse spatial transcriptomics data.
The method effectively identifies sub-regions and quantifies cell type interactions.
Simulations and real data applications confirm its efficiency and flexibility.
Abstract
Spatial transcriptomics has greatly contributed to our understanding of spatial and intra-sample heterogeneity, which could be crucial for deciphering the molecular basis of human diseases. Intra-tumor heterogeneity, e.g. may be associated with cancer treatment responses. However, the lack of computational tools for exploiting cross-regional information and the limited spatial resolution of current technologies present major obstacles to elucidating tissue heterogeneity. To address these challenges, we introduce RegionalST, an efficient computational method that enables users to quantify cell type mixture and interactions, identify sub-regions of interest, and perform cross-region cell type-specific differential analysis for the first time. Our simulations and real data applications demonstrate that RegionalST is an efficient tool for visualizing and analyzing diverse spatial…
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Taxonomy
TopicsSingle-cell and spatial transcriptomics · Gene expression and cancer classification · Bioinformatics and Genomic Networks