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Title: iIMPACT: integrating image and molecular profiles for spatial transcriptomics analysis
Abstract

Current clustering analysis of spatial transcriptomics data primarily relies on molecular information and fails to fully exploit the morphological features present in histology images, leading to compromised accuracy and interpretability. To overcome these limitations, we have developed a multi-stage statistical method called iIMPACT. It identifies and defines histology-based spatial domains based on AI-reconstructed histology images and spatial context of gene expression measurements, and detects domain-specific differentially expressed genes. Through multiple case studies, we demonstrate iIMPACT outperforms existing methods in accuracy and interpretability and provides insights into the cellular spatial organization and landscape of functional genes within spatial transcriptomics data.

 
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NSF-PAR ID:
10512736
Author(s) / Creator(s):
; ; ; ; ; ; ; ;
Publisher / Repository:
Springer Science + Business Media
Date Published:
Journal Name:
Genome Biology
Volume:
25
Issue:
1
ISSN:
1474-760X
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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