Identifying tumor cells at the single-cell level using machine learning


  • J. Dohmen
  • A. Baranovskii
  • J. Ronen
  • B. Uyar
  • V. Franke
  • A. Akalin


  • Genome Biology


  • Genome Biol 23 (1): 123


  • Tumors are complex tissues of cancerous cells surrounded by a heterogeneous cellular microenvironment with which they interact. Single-cell sequencing enables molecular characterization of single cells within the tumor. However, cell annotation-the assignment of cell type or cell state to each sequenced cell-is a challenge, especially identifying tumor cells within single-cell or spatial sequencing experiments. Here, we propose ikarus, a machine learning pipeline aimed at distinguishing tumor cells from normal cells at the single-cell level. We test ikarus on multiple single-cell datasets, showing that it achieves high sensitivity and specificity in multiple experimental contexts.