A5041068486- Single-cell and spatial transcriptomics
- Health, Environment, Cognitive Aging
- Cell Image Analysis Techniques
- Atherosclerosis and Cardiovascular Diseases
- Air Quality and Health Impacts
- AI in cancer detection
Peter MacCallum Cancer Centre
2020-2023
The University of Melbourne
2020
Abstract Spatial proteomics technologies have revealed an underappreciated link between the location of cells in tissue microenvironments and underlying biology clinical features, but there is significant lag development downstream analysis methods benchmarking tools. Here we present SPIAT (spatial image tissues), a spatial-platform agnostic toolkit with suite spatial algorithms, spaSim simulator), simulator data. includes multiple colocalization, neighborhood heterogeneity metrics to...
Abstract Spatial technologies that query the location of cells in tissues at single-cell resolution are gaining popularity and likely to become commonplace. The resulting data includes X, Y coordinates millions cells, cell phenotypes marker or gene expression levels. However, date, tools for analysis this largely underdeveloped, making us severely underpowered our ability extract quantifiable information. We have developed SPIAT ( Sp atial I mage A nalysis T issues), an R package with a...
<h3>Background</h3> Spatial technologies that query the location of cells in tissues such as multiplex immunohistochemistry and spatial transcriptomics are gaining popularity likely to become commonplace. The resulting data often includes X, Y coordinates millions cells, cell phenotypes marker or gene expression levels. In cancer, lymphocytes has been linked prognosis response immunotherapy. While these advances have exciting for field, methods currently being used still coarse, making us...