Salehi Sohrab, Steif Adi, Roth Andrew, Aparicio Samuel, Bouchard-Côté Alexandre, Shah Sohrab P
Bioinformatics Graduate Program, University of British Columbia, 570 West 7th Avenue, Vancouver, V5Z 4S6, BC, Canada.
Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, V6T 2B5, BC, Canada.
Genome Biol. 2017 Mar 1;18(1):44. doi: 10.1186/s13059-017-1169-3.
Next-generation sequencing (NGS) of bulk tumour tissue can identify constituent cell populations in cancers and measure their abundance. This requires computational deconvolution of allelic counts from somatic mutations, which may be incapable of fully resolving the underlying population structure. Single cell sequencing (SCS) is a more direct method, although its replacement of NGS is impeded by technical noise and sampling limitations. We propose ddClone, which analytically integrates NGS and SCS data, leveraging their complementary attributes through joint statistical inference. We show on real and simulated datasets that ddClone produces more accurate results than can be achieved by either method alone.
对整块肿瘤组织进行的下一代测序（NGS）能够识别癌症中的组成细胞群体并测量其丰度。这需要对来自体细胞突变的等位基因计数进行计算反卷积，而这可能无法完全解析潜在的群体结构。单细胞测序（SCS）是一种更直接的方法，尽管其取代NGS受到技术噪声和采样限制的阻碍。我们提出了ddClone，它通过联合统计推断分析整合NGS和SCS数据，利用它们的互补属性。我们在真实和模拟数据集上表明，ddClone产生的结果比单独使用任何一种方法都更准确。
