| Title |
Transcriptome Architecture of Adult Mouse Brain Revealed by Sparse Coding of Genome-Wide In Situ Hybridization Images
|
|---|---|
| Published in |
Neuroinformatics, June 2017
|
| DOI | 10.1007/s12021-017-9333-1 |
| Pubmed ID | |
| Authors |
Yujie Li, Hanbo Chen, Xi Jiang, Xiang Li, Jinglei Lv, Meng Li, Hanchuan Peng, Joe Z. Tsien, Tianming Liu |
| 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. |
X Demographics
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Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 2 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 14 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 14 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 4 | 29% |
| Researcher | 3 | 21% |
| Student > Master | 2 | 14% |
| Professor | 1 | 7% |
| Other | 1 | 7% |
| Other | 1 | 7% |
| Unknown | 2 | 14% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 3 | 21% |
| Neuroscience | 3 | 21% |
| Agricultural and Biological Sciences | 2 | 14% |
| Biochemistry, Genetics and Molecular Biology | 2 | 14% |
| Engineering | 1 | 7% |
| Other | 0 | 0% |
| Unknown | 3 | 21% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 29 August 2021.
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#14,940,583
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Outputs from Neuroinformatics
#235
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Outputs of similar age
#188,699
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Outputs of similar age from Neuroinformatics
#2
of 3 outputs
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