SlideCNA: spatial copy number alteration detection from Slide-seq-like spatial transcriptomics data

SlideCNA: spatial copy number alteration detection from Slide-seq-like spatial transcriptomics data

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Abstract Solid tumors are spatially heterogeneous in their genetic, molecular, and cellular composition, but recent spatial profiling studies have mostly charted genetic and RNA variation kenya tree coral for sale in tumors separately.To leverage the potential of RNA to identify copy number alterations (CNAs), we develop SlideCNA, a computational tool to extract CNA signals from sparse spatial transcriptomics data with near single cellular resolution.SlideCNA uses expression-aware spatial binning to overcome sparsity limitations while maintaining spatial signal to recover CNA patterns.We test SlideCNA on simulated and real Slide-seq data of (metastatic) moen finney breast cancer and demonstrate its potential for spatial subclone detection.