Abstract
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.
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Acknowledgements
T. Liu is supported by NIH R01 DA-033393, NSF CAREER Award IIS-1149260, NIH R01 AG-042599, NSF BME-1302089, NSF BCS-1439051 and NSF DBI-1564736.
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Yujie Li and Hanbo Chen are Co-first Authors.
Hanchuan Peng, Joe Z. Tsien and Tianming Liu are Joint Corresponding Authors.
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Li, Y., Chen, H., Jiang, X. et al. Transcriptome Architecture of Adult Mouse Brain Revealed by Sparse Coding of Genome-Wide In Situ Hybridization Images. Neuroinform 15, 285–295 (2017). https://doi.org/10.1007/s12021-017-9333-1
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DOI: https://doi.org/10.1007/s12021-017-9333-1