Li Yujie, Chen Hanbo, Jiang Xi, Li Xiang, Lv Jinglei, Li Meng, Peng Hanchuan, Tsien Joe Z, Liu Tianming
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.
Neuroinformatics. 2017 Jul;15(3):285-295. doi: 10.1007/s12021-017-9333-1.
Highly differentiated brain structures with distinctly different phenotypes are closely correlated with the unique combination of gene expression patterns. Using a genome-wide in situ hybridization image dataset released by Allen Mouse Brain Atlas, we present a data-driven method of dictionary learning and sparse coding. Our results show that sparse coding can elucidate patterns of transcriptome organization of mouse brain. A collection of components obtained from sparse coding display robust region-specific molecular signatures corresponding to the canonical neuroanatomical subdivisions including fiber tracts and ventricular systems. Other components revealed finer anatomical delineation of domains previously considered homogeneous. We also build an open-access informatics portal that contains the detail of each component along with its ontology and expressed genes. This portal allows intuitive visualization, interpretation and explorations of the transcriptome architecture of a mouse brain.
具有明显不同表型的高度分化的脑结构与基因表达模式的独特组合密切相关。利用艾伦小鼠脑图谱发布的全基因组原位杂交图像数据集,我们提出了一种数据驱动的字典学习和稀疏编码方法。我们的结果表明,稀疏编码可以阐明小鼠脑转录组组织的模式。从稀疏编码中获得的一组成分显示出与包括纤维束和脑室系统在内的典型神经解剖细分相对应的强大的区域特异性分子特征。其他成分揭示了先前被认为是均匀的区域更精细的解剖学划分。我们还建立了一个开放获取的信息学门户,其中包含每个成分的详细信息及其本体和表达基因。这个门户允许对小鼠脑转录组结构进行直观的可视化、解释和探索。