Department of Mathematics.
Department of Cell and Developmental Biology.
Brief Bioinform. 2022 Jul 18;23(4). doi: 10.1093/bib/bbac223.
Single-cell RNA sequencing trades read-depth for dimensionality, often leading to loss of critical signaling gene information that is typically present in bulk data sets. We introduce DURIAN (Deconvolution and mUltitask-Regression-based ImputAtioN), an integrative method for recovery of gene expression in single-cell data. Through systematic benchmarking, we demonstrate the accuracy, robustness and empirical convergence of DURIAN using both synthetic and published data sets. We show that use of DURIAN improves single-cell clustering, low-dimensional embedding, and recovery of intercellular signaling networks. Our study resolves several inconsistent results of cell-cell communication analysis using single-cell or bulk data independently. The method has broad application in biomarker discovery and cell signaling analysis using single-cell transcriptomics data sets.
单细胞 RNA 测序以读取深度换取维度，这通常会导致关键信号基因信息的丢失，而这些信息通常存在于批量数据集。我们引入了 DURIAN（基于去卷积和多任务回归的推断），这是一种用于恢复单细胞数据中基因表达的综合方法。通过系统的基准测试，我们使用合成和已发表数据集展示了 DURIAN 的准确性、鲁棒性和经验收敛性。我们表明，使用 DURIAN 可以改善单细胞聚类、低维嵌入和细胞间信号网络的恢复。我们的研究解决了使用单细胞或批量数据进行细胞间通讯分析时的几个不一致结果。该方法在使用单细胞转录组数据集进行生物标志物发现和细胞信号分析方面具有广泛的应用。
