STARCH: copy number and clone inference from spatial transcriptomics data

Rebecca Elyanow, Ron Zeira, Max Land, Benjamin J. Raphael

Research output: Contribution to journalArticlepeer-review

21 Scopus citations


Tumors are highly heterogeneous, consisting of cell populations with both transcriptional and genetic diversity. These diverse cell populations are spatially organized within a tumor, creating a distinct tumor microenvironment. A new technology called spatial transcriptomics can measure spatial patterns of gene expression within a tissue by sequencing RNA transcripts from a grid of spots, each containing a small number of cells. In tumor cells, these gene expression patterns represent the combined contribution of regulatory mechanisms, which alter the rate at which a gene is transcribed, and genetic diversity, particularly copy number aberrations (CNAs) which alter the number of copies of a gene in the genome. CNAs are common in tumors and often promote cancer growth through upregulation of oncogenes or downregulation of tumor-suppressor genes. We introduce a new method STARCH (spatial transcriptomics algorithm reconstructing copy-number heterogeneity) to infer CNAs from spatial transcriptomics data. STARCH overcomes challenges in inferring CNAs from RNA-sequencing data by leveraging the observation that cells located nearby in a tumor are likely to share similar CNAs. We find that STARCH outperforms existing methods for inferring CNAs from RNA-sequencing data without incorporating spatial information.

Original languageEnglish (US)
Article number035001
JournalPhysical Biology
Issue number3
StatePublished - May 2021
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Molecular Biology
  • Biophysics
  • Structural Biology
  • Cell Biology


  • CNA
  • RNA-seq
  • algorithm
  • copy number
  • spatial transcriptomics


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