| Title |
Discover mouse gene coexpression landscapes using dictionary learning and sparse coding
|
|---|---|
| Published in |
Brain Structure and Function, June 2017
|
| DOI | 10.1007/s00429-017-1460-9 |
| Pubmed ID | |
| Authors |
Yujie Li, Hanbo Chen, Xi Jiang, Xiang Li, Jinglei Lv, Hanchuan Peng, Joe Z. Tsien, Tianming Liu |
| Abstract |
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. |
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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
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Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 16 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 8 | 50% |
| Researcher | 3 | 19% |
| Other | 1 | 6% |
| Professor | 1 | 6% |
| Student > Doctoral Student | 1 | 6% |
| Other | 2 | 13% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 5 | 31% |
| Engineering | 3 | 19% |
| Biochemistry, Genetics and Molecular Biology | 2 | 13% |
| Neuroscience | 2 | 13% |
| Environmental Science | 1 | 6% |
| Other | 1 | 6% |
| Unknown | 2 | 13% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. 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 13 July 2017.
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#19,702,729
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Outputs from Brain Structure and Function
#1,236
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#246,527
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Outputs of similar age from Brain Structure and Function
#33
of 42 outputs
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