The Spatial Transcriptomics Toolkit: Memory, Clustering, and Deconvolution

In this episode, Alex and Lorena tackle the computational challenges of spatial transcriptomics. Learn how BPCells can help you work with millions of cells without needing terabytes of RAM, discover how Banksy's neighborhood-aware clustering reveals tissue architecture, and explore RCTD's approach to cell type deconvolution in spatially-resolved data. Plus, Lorena reviews Positron, the new R-friendly IDE that's catching attention in the bioinformatics community.https://github.com/bnprks/BPCellshttps://github.com/prabhakarlab/Banksyhttps://github.com/dmcable/spacexrhttps://github.com/bcbio/spatial-reportshttps://github.com/seandavi/awesome-single-cellhttps://positron.posit.co/Send us your comments, questions, and suggestions using this form 📁: https://forms.gle/ncwo6HZeN4uA9gPg7Follow us on LinkedIn: https://www.linkedin.com/in/lpantano/ and https://www.linkedin.com/in/alexandra-bartlett-926b32109/Thanks to Amulya Shastry for editing and management support.If you enjoyed the episode, please subscribe and leave us a review. Subscribe here: https://podcast.ausha.co/a-coffee-with-compbio?s=1Hosted on Ausha. See ausha.co/privacy-policy for more information.