Rakaia: interactive discovery of spatial biology at scale
Watson, M.; Abazari, G.; Chen, E. L.; Tan, T. J.; Gorman, J. L.; Diorio, C.; Berman, H. K.; Campbell, K. R.; Jackson, H. W.
Show abstract
Spatial biology data throughput currently outpaces interpretable analysis, limiting large-scale discovery and translation. Here we present Rakaia, a browser-based platform for multiplexed imaging and spatial transcriptomics, empowering code-free interactive exploration and evaluation. Rakaia enables visualization, annotation, and feature-based querying with prioritization across thousands of images. We use Rakaia to identify cell types of interest in >200 highly multiplexed images from non-malignant human breast tissue. Rakaia is available at: https://rakaia.io/
Matching journals
The top 1 journal accounts for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- Whole-cell segmentation of tissue images with human-level performance using large-scale data annotation and deep learning 95%
- Massively parallel single-cell chromatin landscapes of human immune cell development and intratumoral T cell exhaustion 94%
- BayesSpace enables the robust characterization of spatial gene expression architecture in tissue sections at increased resolution 93%
Similar papers in this journal
- DeepSpaceDB: a spatial transcriptomics atlas for interactive in-depth analysis of tissues and tissue microenvironments 94%
- Single-Cell Trajectory Inference for Detecting Transient Events in Biological Processes 94%
- Cell type identification in spatial transcriptomics data can be improved by leveraging cell-type-informative paired tissue images using a Bayesian probabilistic model. 93%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.