MorphLink: Bridging Cell Morphological Behaviors and Molecular Dynamics in Multi-modal Spatial Omics.
Jing HuangChenyang YuanJiahui JiangJianfeng ChenSunil S BadveYesim Gokmen-PolarRossana L SeguraXinmiao YanAlexander LazarJianjun GaoMichael P EpsteinLinghua WangJian HuPublished in: bioRxiv : the preprint server for biology (2024)
Multi-modal spatial omics data are invaluable for exploring complex cellular behaviors in diseases from both morphological and molecular perspectives. Current analytical methods primarily focus on clustering and classification, and do not adequately examine the relationship between cell morphology and molecular dynamics. Here, we present MorphLink, a framework designed to systematically identify disease-related morphological-molecular interplays. MorphLink has been evaluated across a wide array of datasets, showcasing its effectiveness in extracting and linking interpretable morphological features with various molecular measurements in multi-modal spatial omics analyses. These linkages provide a transparent depiction of cellular behaviors that drive transcriptomic heterogeneity and immune diversity across different regions within diseased tissues, such as cancer. Additionally, MorphLink is scalable and robust against cross-sample batch effects, making it an efficient method for integrative spatial omics data analysis across samples, cohorts, and modalities, and enhancing the interpretation of results for large-scale studies.
Keyphrases
- single cell
- molecular dynamics
- rna seq
- data analysis
- high throughput
- density functional theory
- randomized controlled trial
- gene expression
- high resolution
- single molecule
- electronic health record
- papillary thyroid
- mesenchymal stem cells
- mass spectrometry
- stem cells
- artificial intelligence
- network analysis
- squamous cell