Journal Article

iIMPACT: integrating image and molecular profiles for spatial transcriptomics analysis

Wen, Zhuoyu, Wang, Shidan, Zhu, Bencong, Li, Qiwei, Jia, Liwei, Jiang, Xi, Xu, Lin, Guo, Lei, Xiao, Guanghua

2024

Overview

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.

• Publisher: BioMed Central,Springer Nature B.V,BMC
• Subject: Advances in Spatial Transcriptomics for Understanding Development and Disease / AI-reconstructed histology image / Animal Genetics and Genomics / Bioinformatics / Biomedical and Life Sciences / Breast cancer / Cluster Analysis / Clustering / Data analysis / Datasets / Evolutionary Biology / Functional morphology / Gene expression / Gene Expression Profiling - methods / gene expression regulation / genome / Histology / Human Genetics / Humans / Image Processing, Computer-Assisted - methods / landscapes / Life Sciences / Markov random field / Method / Microbial Genetics and Genomics / Morphology / Neural networks / Parameter estimation / Plant Genetics and Genomics / Spatial clustering / Spatially resolved transcriptomics / Spatially variable gene / Statistical methods / Transcriptome / Transcriptomics