StaVia: spatially and temporally aware cartography with higher-order random walks for cell atlases.
Shobana V StassenMinato KobashiEdmund Y LamYuanhua HuangJoshua W K HoKevin K TsiaPublished in: Genome biology (2024)
Single-cell atlases pose daunting computational challenges pertaining to the integration of spatial and temporal information and the visualization of trajectories across large atlases. We introduce StaVia, a computational framework that synergizes multi-faceted single-cell data with higher-order random walks that leverage the memory of cells' past states, fused with a cartographic Atlas View that offers intuitive graph visualization. This spatially aware cartography captures relationships between cell populations based on their spatial location as well as their gene expression and developmental stage. We demonstrate this using zebrafish gastrulation data, underscoring its potential to dissect complex biological landscapes in both spatial and temporal contexts.
Keyphrases
- single cell
- rna seq
- gene expression
- high throughput
- electronic health record
- induced apoptosis
- big data
- dna methylation
- depressive symptoms
- neural network
- machine learning
- stem cells
- oxidative stress
- cell cycle arrest
- signaling pathway
- artificial intelligence
- mesenchymal stem cells
- deep learning
- data analysis
- convolutional neural network