Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA, USA.
School of Automation, Northwestern Polytechnical University, Xi'an, China.
Brain Struct Funct. 2017 Dec;222(9):4253-4270. doi: 10.1007/s00429-017-1460-9. Epub 2017 Jun 29.
Gene coexpression patterns carry rich information regarding enormously complex brain structures and functions. Characterization of these patterns in an unbiased, integrated, and anatomically comprehensive manner will illuminate the higher-order transcriptome organization and offer genetic foundations of functional circuitry. Here using dictionary learning and sparse coding, we derived coexpression networks from the space-resolved anatomical comprehensive in situ hybridization data from Allen Mouse Brain Atlas dataset. The key idea is that if two genes use the same dictionary to represent their original signals, then their gene expressions must share similar patterns, thereby considering them as "coexpressed." For each network, we have simultaneous knowledge of spatial distributions, the genes in the network and the extent a particular gene conforms to the coexpression pattern. Gene ontologies and the comparisons with published gene lists reveal biologically identified coexpression networks, some of which correspond to major cell types, biological pathways, and/or anatomical regions.
基因共表达模式携带着关于极其复杂的大脑结构和功能的丰富信息。以无偏倚、综合和解剖全面的方式对这些模式进行特征描述，将阐明更高阶的转录组组织，并为功能回路提供遗传基础。在这里，我们使用字典学习和稀疏编码，从 Allen Mouse Brain Atlas 数据集的空间分辨解剖综合原位杂交数据中推导出共表达网络。其关键思想是，如果两个基因使用相同的字典来表示它们的原始信号，那么它们的基因表达必须具有相似的模式，因此将它们视为“共表达”。对于每个网络，我们同时了解其空间分布、网络中的基因以及特定基因与共表达模式吻合的程度。基因本体论和与已发表基因列表的比较揭示了生物学上确定的共表达网络，其中一些对应于主要的细胞类型、生物途径和/或解剖区域。
