Integrated single cell data analysis reveals cell specific networks and novel coactivation markers.
Integrated single cell data analysis reveals cell specific networks and novel coactivation markers.
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综合的单细胞数据分析揭示了细胞特异性网络和新颖的共激活标记。
DOI：
10.1186/s12918-016-0370-4
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发表时间：
2016-12-05
影响因子：
--
通讯作者：
Yang JY
中科院分区：
文献类型：
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作者：
Ghazanfar S;Bisogni AJ;Ormerod JT;Lin DM;Yang JY
近年来，大规模单细胞转录组分析呈爆炸式增长，使人们能够前所未有地深入了解单个细胞的行为。使用单细胞 RNA 测序数据识别高表达水平的基因可用于表征非常活跃的基因和发生这种情况的细胞。特别是单细胞 RNA-Seq 可以对高基因表达以及基因共表达进行细胞特异性表征。我们提供了一个多功能的建模框架来识别跨多个数据集的不同神经元细胞类型的转录状态以及共激活结构。我们采用伽马正态混合模型来识别跨细胞的活跃基因表达，并使用这些来表征嗅觉感觉神经元细胞成熟度的标记，并构建细胞特异性共激活网络。我们发现，对多个数据集的组合分析可以识别更多已知的成熟标记，并指出一些可能参与神经元成熟的新基因。我们还观察到，成熟神经元的细胞特异性共激活网络往往比未成熟神经元具有更高的集中网络测量。多个数据集的整合有望带来更多的统计能力来识别感兴趣的基因和模式。我们发现，将数据转换为活性和非活性基因状态可以更直接地比较数据集，从而识别成熟标记基因和细胞特异性网络观察，同时考虑到单细胞转录组数据的独特特征。本文的在线版本 (doi:10.1186/s12918-016-0370-4) 包含补充材料，可供授权用户使用。
Large scale single cell transcriptome profiling has exploded in recent years and has enabled unprecedented insight into the behavior of individual cells. Identifying genes with high levels of expression using data from single cell RNA sequencing can be useful to characterize very active genes and cells in which this occurs. In particular single cell RNA-Seq allows for cell-specific characterization of high gene expression, as well as gene coexpression. We offer a versatile modeling framework to identify transcriptional states as well as structures of coactivation for different neuronal cell types across multiple datasets. We employed a gamma-normal mixture model to identify active gene expression across cells, and used these to characterize markers for olfactory sensory neuron cell maturity, and to build cell-specific coactivation networks. We found that combined analysis of multiple datasets results in more known maturity markers being identified, as well as pointing towards some novel genes that may be involved in neuronal maturation. We also observed that the cell-specific coactivation networks of mature neurons tended to have a higher centralization network measure than immature neurons. Integration of multiple datasets promises to bring about more statistical power to identify genes and patterns of interest. We found that transforming the data into active and inactive gene states allowed for more direct comparison of datasets, leading to identification of maturity marker genes and cell-specific network observations, taking into account the unique characteristics of single cell transcriptomics data. The online version of this article (doi:10.1186/s12918-016-0370-4) contains supplementary material, which is available to authorized users.