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

## TL;DR

SlideCNA is a new tool that detects copy number alterations in spatial transcriptomics data, helping to understand tumor heterogeneity at a cellular level.

## Contribution

SlideCNA introduces a novel method for CNA detection using expression-aware spatial binning in sparse spatial transcriptomics data.

## Key findings

- SlideCNA effectively recovers CNA patterns from simulated and real Slide-seq data.
- The tool demonstrates potential for identifying spatial subclones in metastatic breast cancer.
- Expression-aware spatial binning improves CNA detection despite data sparsity.

## 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 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) breast cancer and demonstrate its potential for spatial subclone detection.

The online version contains supplementary material available at 10.1186/s13059-025-03573-y.