Joint analysis of single-cell and bulk tissue sequencing data to infer intratumor heterogeneity

Wei Sun, Chong Jin, Jonathan A. Gelfond, Ming Hui Chen, Joseph G. Ibrahim

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


Many computational methods have been developed to discern intratumor heterogeneity (ITH) using DNA sequence data from bulk tumor samples. These methods share an assumption that two mutations arise from the same subclone if they have similar mutant allele-frequencies (MAFs), and thus it is difficult or impossible to distinguish two subclones with similar MAFs. Single-cell DNA sequencing (scDNA-seq) data can be very informative for ITH inference. However, due to the difficulty of DNA amplification, scDNA-seq data are often very noisy. A promising new study design is to collect both bulk and single-cell DNA-seq data and jointly analyze them to mitigate the limitations of each data type. To address the analytic challenges of this new study design, we propose a computational method named BaSiC (Bulk tumor and Single Cell), to discern ITH by jointly analyzing DNA-seq data from bulk tumor and single cells. We demonstrate that BaSiC has comparable or better performance than the methods using either data type. We further evaluate BaSiC using bulk tumor and single-cell DNA-seq data from a breast cancer patient and several leukemia patients.

Original languageEnglish (US)
Pages (from-to)983-994
Number of pages12
Issue number3
StatePublished - Sep 1 2020
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • General Biochemistry, Genetics and Molecular Biology
  • General Immunology and Microbiology
  • General Agricultural and Biological Sciences
  • Applied Mathematics


  • bulk tumor samples
  • intratumor heterogeneity
  • missing mutation calls
  • single-cell DNA sequencing


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