Gale and Ira Drukier Institute for Children's Health, Weill Cornell Medicine, 413 E. 69th Street, New York, NY 10021, United States.
Center for Applied Bioinformatics, St. Jude Children's Research Hospital, 262 Danny Thomas Place, Memphis, TN 38105, United States.
Brief Bioinform. 2024 May 23;25(4). doi: 10.1093/bib/bbae254.
Cell hashing, a nucleotide barcode-based method that allows users to pool multiple samples and demultiplex in downstream analysis, has gained widespread popularity in single-cell sequencing due to its compatibility, simplicity, and cost-effectiveness. Despite these advantages, the performance of this method remains unsatisfactory under certain circumstances, especially in experiments that have imbalanced sample sizes or use many hashtag antibodies. Here, we introduce a hybrid demultiplexing strategy that increases accuracy and cell recovery in multi-sample single-cell experiments. This approach correlates the results of cell hashing and genetic variant clustering, enabling precise and efficient cell identity determination without additional experimental costs or efforts. In addition, we developed HTOreader, a demultiplexing tool for cell hashing that improves the accuracy of cut-off calling by avoiding the dominance of negative signals in experiments with many hashtags or imbalanced sample sizes. When compared to existing methods using real-world datasets, this hybrid approach and HTOreader consistently generate reliable results with increased accuracy and cell recovery.
细胞哈希（Cell hashing）是一种基于核苷酸条形码的方法，允许用户在下游分析中对多个样本进行混池和拆池，由于其兼容性、简单性和成本效益，在单细胞测序中得到了广泛的应用。尽管有这些优势，但在某些情况下，该方法的性能仍不尽如人意，特别是在样本量不平衡或使用许多标签抗体的实验中。在这里，我们介绍了一种混合拆池策略，该策略可提高多样本单细胞实验的准确性和细胞回收率。该方法将细胞哈希和遗传变异聚类的结果相关联，无需额外的实验成本或努力，即可实现精确、高效的细胞身份鉴定。此外，我们还开发了 HTOreader，这是一种用于细胞哈希的拆池工具，通过避免在使用大量标签或样本量不平衡的实验中阴性信号的主导地位，提高了截止值调用的准确性。与使用真实数据集的现有方法相比，这种混合方法和 HTOreader 始终能够生成更可靠的结果，提高了准确性和细胞回收率。
