FICTURE: scalable segmentation-free analysis of submicron-resolution spatial transcriptomics.
Yichen SiChangHee LeeYongha HwangJeong H YunWeiqiu ChengChun-Seok ChoMiguel QuirosAsma NusratWeizhou ZhangGoo JunSebastian ZöllnerJun Hee LeeHyun-Min KangPublished in: Nature methods (2024)
Spatial transcriptomics (ST) technologies have advanced to enable transcriptome-wide gene expression analysis at submicron resolution over large areas. However, analysis of high-resolution ST is often challenged by complex tissue structure, where existing cell segmentation methods struggle due to the irregular cell sizes and shapes, and by the absence of segmentation-free methods scalable to whole-transcriptome analysis. Here we present FICTURE (Factor Inference of Cartographic Transcriptome at Ultra-high REsolution), a segmentation-free spatial factorization method that can handle transcriptome-wide data labeled with billions of submicron-resolution spatial coordinates and is compatible with both sequencing-based and imaging-based ST data. FICTURE uses the multilayered Dirichlet model for stochastic variational inference of pixel-level spatial factors, and is orders of magnitude more efficient than existing methods. FICTURE reveals the microscopic ST architecture for challenging tissues, such as vascular, fibrotic, muscular and lipid-laden areas in real data where previous methods failed. FICTURE's cross-platform generality, scalability and precision make it a powerful tool for exploring high-resolution ST.
Keyphrases
- single cell
- high resolution
- rna seq
- deep learning
- high throughput
- convolutional neural network
- electronic health record
- big data
- genome wide
- mass spectrometry
- stem cells
- machine learning
- fatty acid
- dna methylation
- data analysis
- tandem mass spectrometry
- genome wide identification
- systemic sclerosis
- mesenchymal stem cells
- artificial intelligence
- bone marrow