Department of Applied Mathematics, Peter the Great St. Petersburg Polytechnic University, St. Petersburg, Russia.
Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
Genome Biol. 2018 Jun 19;19(1):78. doi: 10.1186/s13059-018-1449-6.
Recent single-cell RNA-seq protocols based on droplet microfluidics use massively multiplexed barcoding to enable simultaneous measurements of transcriptomes for thousands of individual cells. The increasing complexity of such data creates challenges for subsequent computational processing and troubleshooting of these experiments, with few software options currently available. Here, we describe a flexible pipeline for processing droplet-based transcriptome data that implements barcode corrections, classification of cell quality, and diagnostic information about the droplet libraries. We introduce advanced methods for correcting composition bias and sequencing errors affecting cellular and molecular barcodes to provide more accurate estimates of molecular counts in individual cells.
最近基于液滴微流控技术的单细胞 RNA-seq 方案使用大规模多重编码来实现对数千个单个细胞转录组的同时测量。这些数据的日益复杂性给后续的计算处理和这些实验的故障排除带来了挑战，目前可用的软件选项很少。在这里，我们描述了一个用于处理基于液滴的转录组数据的灵活管道，该管道实现了条形码校正、细胞质量分类以及液滴文库的诊断信息。我们引入了用于校正影响细胞和分子条形码的组成偏差和测序错误的先进方法，以提供个体细胞中分子计数的更准确估计。
Zotero 插件
