Deep Learning Platform for Unified Multi-Technology Spatial Data Analysis
SpatialX is a powerful platform for analyzing spatial biology data across multiple images and technologies with optimized computation and built-in cell annotation.
Deep Learning Platform for Unified Multi-Technology Spatial Data Analysis
SpatialX is a powerful platform for analyzing spatial biology data across multiple images and technologies with optimized computation and built-in cell annotation.
Data visualization
Cell type annotation
Differential expressed gene
Neighborhood analysis
TuringSegment
Region search
Region segmentation
Visualizing images, protein channels, cell segmentation, cell centers, and transcripts from tissue to single cell levels.
Annotating cell type composition of the sample using MetaReference (BioTuring database of 2000+ scRNA-seq studies).
Identifying differentially expressed genes from two cell populations using custom cell selection tools
Identifying cell types that spatially associated and neighborhood clustering showing the density of similar cell type clusters.
The optimized Cellpose model for faster and more memory-efficient cell segmentation
Identifying and labeling similar regions with the region of interest based on the similarity of cell and tissue morphology
Combining gene expression with spatial data for defining functional biological areas.
We’re proud to be working with leading global pharmaceutical companies and individual scientists, and we’re grateful for their feedback, suggestions, and support.