Clustering-independent estimation of cell abundances in bulk tissues using single-cell RNA-seq data.

Rachael G Aubin, Javier Montelongo, Robert Hu, Elijah Gunther, Patrick Nicodemus, Pablo G Camara
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Abstract

Single-cell RNA-sequencing has transformed the study of biological tissues by enabling transcriptomic characterizations of their constituent cell states. Computational methods for gene expression deconvolution use this information to infer the cell composition of related tissues profiled at the bulk level. However, current deconvolution methods are restricted to discrete cell types and have limited power to make inferences about continuous cellular processes like cell differentiation or immune cell activation. We present ConDecon, a clustering-independent method for inferring the likelihood for each cell in a single-cell dataset to be present in a bulk tissue. ConDecon represents an improvement in phenotypic resolution and functionality with respect to regression-based methods. Using ConDecon, we discover the implication of neurodegenerative microglia inflammatory pathways in the mesenchymal transformation of pediatric ependymoma and characterize their spatial trajectories of activation. The generality of this approach enables the deconvolution of other data modalities such as bulk ATAC-seq data.

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使用单细胞RNA-seq数据对大块组织中细胞丰度进行聚类独立估计。
单细胞RNA测序通过对生物组织组成细胞状态进行转录组学表征,改变了对生物组织的研究。基因表达去卷积的计算方法使用这些信息来推断在体积水平上描述的相关组织的细胞组成。然而,目前的去卷积方法仅限于离散的细胞类型,并且对连续的细胞过程(如细胞分化或免疫细胞激活)进行推断的能力有限。我们提出了ConDecon,这是一种独立于聚类的方法,用于推断单细胞数据集中每个细胞存在于大块组织中的可能性。ConDecon代表了相对于当前反褶积方法在功能性和准确性方面的改进。使用ConDecon,我们发现了神经退行性小胶质细胞炎症途径在室管膜瘤间充质转化中的意义,概括了斑马鱼胚胎发生过程中细胞分化的空间模式,并从大量ATAC-seq数据中进行了时间推断。总体而言,ConDecon显著增强了我们对大块组织样本中动态细胞过程的理解。
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