Current variant callers are not suitable for single-cell DNA sequencing, as they do not account for allelic dropout, false-positive errors and coverage nonuniformity. We developed Monovar (https://bitbucket.org/hamimzafar/monovar), a statistical method for detecting and genotyping single-nucleotide variants in single-cell data. Monovar exhibited superior performance over standard algorithms on benchmarks and in identifying driver mutations and delineating clonal substructure in three different human tumor data sets.
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This work was supported by grants to N.N. from the Lefkofsky Foundation, NCI (RO1CA169244-01), NIH (R21CA174397), Agilent University Relations, and MD Anderson Knowledge Gap and Center for Genetics & Genomics. N.N. is a Damon Runyon-Rachleff Innovator (DRR-25-13), ACS Research Scholar, T.C. Hsu Endowed Scholar and Sabin Fellow. K.C. is a Sabin Fellow and was supported by an NCI grant (RO1CA172652). The study was supported by the Bosarge, Chapman and Dell Foundations and NCI (CA016672). The authors thank W. Zhou.
The authors declare no competing financial interests.
Integrated supplementary information
Supplementary Figure 1 Comparison of Monovar to Standard Variant Calling Methods using Isogenic Cell Line Data
Monovar, GATK UnifiedGenotyper, GATK HaplotypeCaller and Samtools were compared using single cell exome sequencing data generated from a normal isogenic fibroblast cell line in terms of SNV detection (a) Precision versus Detection Efficiency (Recall) and (b) SNV transition and transversion spectrum for FP errors.
Monovar and GATK HaplotypeCaller were compared in terms of (a) Precision and (b) Detection Efficiency (Recall), respectively at 40×, 30×, 20× and 10× sequencing depths, acquired via down-sampling the SKN2 SCS data.
The SNV detection (a) Precision and (b) Detection Efficiency of Monovar were measured by comparing SNVs detected from a set of datasets, created by in silico intermixing of variable numbers of SKN2 and 12 TNBC cells, with SNVs detected from SKN2 bulk sequencing data.
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Zafar, H., Wang, Y., Nakhleh, L. et al. Monovar: single-nucleotide variant detection in single cells. Nat Methods 13, 505–507 (2016). https://doi.org/10.1038/nmeth.3835
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