Abstract
Due to the growth of interest in single-cell genomics, computational methods for distinguishing true variants from artifacts are highly desirable. While special attention has been paid to false positives in variant or mutation calling from single-cell sequencing data, an equally important but often neglected issue is that of false negatives derived from allele dropout during the amplification of single cell genomes. In this paper, we propose a simple strategy to reduce the false negatives in single-cell sequencing data analysis. Simulation results show that this method is highly reliable, with an error rate of 4.94×10-5, which is orders of magnitude lower than the expected false negative rate (~34%) estimated from a single-cell exome dataset, though the method is limited by the low SNP density in the human genome. We applied this method to analyze the exome data of a few dozen single tumor cells generated in previous studies, and extracted cell specific mutation information for a small set of sites. Interestingly, we found that there are difficulties in using the classical clonal model of tumor cell growth to explain the mutation patterns observed in some tumor cells.
Highlights
Multi-cellular life often starts from a single fertilized egg that develops through mitotic cell division into an organism composed of a large number of somatic cells, each of which contains an entire genome
We reasoned that neighboring polymorphisms may help to assess whether allele dropout has occurred in the region of a target site
If there is a germ-line single nucleotide polymorphic (SNP) site that is next to a target site and heterozygous in the individual, one would expect just one allele at the SNP site when there is allele dropout, and two alleles when there is no dropout (Fig 2)
Summary
Multi-cellular life often starts from a single fertilized egg that develops through mitotic cell division into an organism composed of a large number of somatic cells, each of which contains an entire genome. Because DNA replication is not 100% accurate, mutations occur during every cell division, resulting in a slightly different genome for every somatic cell [1]. Cancer originates from a single somatic cell that proliferates through mitotic cell division to form a tumor composed of numerous cancer cells, each of which contains a slightly different genome [2]. It is of great interest to study such somatic mutations in single cells to understand, for instance, the effect of genetic divergence in neurons in the brain on their functional diversity or neurological disease [3], early differentiation in human embryogenesis [4], intratumoral genetic heterogeneity [5], etc. Emerging single-cell genome sequencing techniques are highly desirable research tools.
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