Tumor genetic analysis from single-cell RNA-seq data

Fan, J. et al. Genome Res. (2018).

Single-cell RNA-seq offers a powerful way to dissect expression in heterogeneous tumors, but it cannot relate expression differences to genetic clones. Sequencing of both DNA and RNA from individual cells is possible, but it remains relatively costly. As an alternative, Fan et al. have developed a hidden Markov model integrated Bayesian approach for detecting copy-number variants (CNVs) and loss of heterozygosity from single-cell RNA-seq data (HoneyBADGER). The software identifies candidate focal CNVs by jointly analyzing single-nucleotide polymorphisms in a given region for allelic imbalance, taking the expected rate of monoallelic detection into account. The researchers show that the tool can genotype large deletions and CNVs in simulated data, multiple myeloma, acute myeloid leukemia, and breast cancer samples, even from 3′-tag RNA-seq data. Their analysis identifies some transcriptional signatures that correspond to genetic clones, and others that do not.

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Correspondence to Tal Nawy.

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Nawy, T. Tumor genetic analysis from single-cell RNA-seq data. Nat Methods 15, 571 (2018).

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