State Key Laboratory of Genetic Engineering, National Clinical Research Center for Aging and Medicine, Huashan Hospital, Collaborative Innovation Center of Genetics and Development, Human Phenome Institute, Center for Evolutionary Biology, Shanghai Engineering Research Center of Industrial Microorganisms, School of Life Sciences, Fudan University, Shanghai 200438, P.R. China.
Ministry of Education (MOE) Key Laboratory of Contemporary Anthropology, Human Phenome Institute, School of Life Sciences, Fudan University, Shanghai 200438, P.R. China.
Brief Bioinform. 2023 Sep 20;24(5). doi: 10.1093/bib/bbad273.
Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful tool for uncovering cellular heterogeneity. However, the high costs associated with this technique have rendered it impractical for studying large patient cohorts. We introduce ENIGMA (Deconvolution based on Regularized Matrix Completion), a method that addresses this limitation through accurately deconvoluting bulk tissue RNA-seq data into a readout with cell-type resolution by leveraging information from scRNA-seq data. By employing a matrix completion strategy, ENIGMA minimizes the distance between the mixture transcriptome obtained with bulk sequencing and a weighted combination of cell-type-specific expression. This allows the quantification of cell-type proportions and reconstruction of cell-type-specific transcriptomes. To validate its performance, ENIGMA was tested on both simulated and real datasets, including disease-related tissues, demonstrating its ability in uncovering novel biological insights.
单细胞 RNA 测序 (scRNA-seq) 已成为揭示细胞异质性的强大工具。然而，该技术的高成本使其难以用于研究大型患者队列。我们介绍了 ENIGMA（基于正则化矩阵完成的去卷积），这是一种通过利用 scRNA-seq 数据中的信息，将批量组织 RNA-seq 数据准确地去卷积为具有细胞类型分辨率的读出的方法，从而解决了这一限制。通过采用矩阵完成策略，ENIGMA 将通过批量测序获得的混合转录组与细胞类型特异性表达的加权组合之间的距离最小化。这允许定量细胞类型比例并重建细胞类型特异性转录组。为了验证其性能，ENIGMA 在模拟和真实数据集（包括与疾病相关的组织）上进行了测试，证明了其在揭示新的生物学见解方面的能力。
