Characterizing the replicability of cell types defined by single cell RNA-sequencing data using MetaNeighbor.
Characterizing the replicability of cell types defined by single cell RNA-sequencing data using MetaNeighbor.
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DOI：
10.1038/s41467-018-03282-0
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发表时间：
2018-02-28
影响因子：
16.6
通讯作者：
Gillis J
中科院分区：
文献类型：
--
作者：
Crow M;Paul A;Ballouz S;Huang ZJ;Gillis J
单细胞RNA测序(scRNA-seq)技术为发现和表征细胞类型提供了一种新的途径；然而，当前管道固有的实验特有的技术偏见和分析可变性可能会破坏其可复制性。特殊命名约定的使用进一步阻碍了元分析。在这里，我们展示了我们的复制框架MetaNeighbor，它量化了细胞类型在数据集之间复制的程度，并能够快速识别具有高相似性的集群。我们首先衡量神经元身份的可复制性，比较八个技术和生物上不同的数据集的结果，以确定更复杂评估的最佳实践。然后我们将其应用于新的中间神经元亚型，发现24/45亚型有复制的证据，这使得能够识别强大的候选标记基因。在所有任务中，我们发现大量可变表达的基因可以高精度地识别可复制的细胞类型，这为大规模评估scRNA-seq数据提供了一条一般的前进路线。由于生物信息学管道之间的技术偏差和分析可变性，单细胞RNA测序分析在复制方面提出了挑战。在这里，Crow等人开发了MetaNeighbor，用于测量数据集之间的细胞类型复制，并使用它来识别具有复制证据的神经元亚型的标记基因。
Single-cell RNA-sequencing (scRNA-seq) technology provides a new avenue to discover and characterize cell types; however, the experiment-specific technical biases and analytic variability inherent to current pipelines may undermine its replicability. Meta-analysis is further hampered by the use of ad hoc naming conventions. Here we demonstrate our replication framework, MetaNeighbor, that quantifies the degree to which cell types replicate across datasets, and enables rapid identification of clusters with high similarity. We first measure the replicability of neuronal identity, comparing results across eight technically and biologically diverse datasets to define best practices for more complex assessments. We then apply this to novel interneuron subtypes, finding that 24/45 subtypes have evidence of replication, which enables the identification of robust candidate marker genes. Across tasks we find that large sets of variably expressed genes can identify replicable cell types with high accuracy, suggesting a general route forward for large-scale evaluation of scRNA-seq data. Single cell RNA-sequencing analysis poses challenges in replication due to technical biases and analytic variability among bioinformatics pipelines. Here, Crow et al develop MetaNeighbor for measuring cell-type replication across datasets, and use it to identify marker genes for neuron subtypes with evidence of replication.
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