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Computational genetic neuroanatomy of the developing mouse brain: dimensionality reduction, visualization, and clustering.

Ji S - BMC Bioinformatics (2013)

Bottom Line: Our results show that the developing brain anatomy is largely preserved in the reduced gene expression space.To provide a quantitative analysis, we cluster the reduced data into groups and measure the consistency with neuroanatomy at multiple levels.Dimensionality reduction and visual exploration facilitate the study of this relationship.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Computer Science, Old Dominion University, 4700 Elkhorn Avenue, Suite 3300, Norfolk, VA 23529-0162, USA. sji@cs.odu.edu

ABSTRACT

Background: The structured organization of cells in the brain plays a key role in its functional efficiency. This delicate organization is the consequence of unique molecular identity of each cell gradually established by precise spatiotemporal gene expression control during development. Currently, studies on the molecular-structural association are beginning to reveal how the spatiotemporal gene expression patterns are related to cellular differentiation and structural development.

Results: In this article, we aim at a global, data-driven study of the relationship between gene expressions and neuroanatomy in the developing mouse brain. To enable visual explorations of the high-dimensional data, we map the in situ hybridization gene expression data to a two-dimensional space by preserving both the global and the local structures. Our results show that the developing brain anatomy is largely preserved in the reduced gene expression space. To provide a quantitative analysis, we cluster the reduced data into groups and measure the consistency with neuroanatomy at multiple levels. Our results show that the clusters in the low-dimensional space are more consistent with neuroanatomy than those in the original space.

Conclusions: Gene expression patterns and developing brain anatomy are closely related. Dimensionality reduction and visual exploration facilitate the study of this relationship.

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Related in: MedlinePlus

Visualization of the Allen Developing Mouse Brain Atlas data for ages E11.5 and P28 after projecting to 2-D space using t-SNE and PCA. Each point corresponds to a brain voxel, which is displayed according to the longitudinal zones (F=floor, B=basal, A=alar, R=roof) it belongs to. Results for other ages are shown in the Additional file 1.
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Figure 6: Visualization of the Allen Developing Mouse Brain Atlas data for ages E11.5 and P28 after projecting to 2-D space using t-SNE and PCA. Each point corresponds to a brain voxel, which is displayed according to the longitudinal zones (F=floor, B=basal, A=alar, R=roof) it belongs to. Results for other ages are shown in the Additional file 1.

Mentions: To provide in-depth visual exploration of the genetic neuroanatomy along the longitudinal and transversal dimensions, we display in Figure 6 the E11.5 and P28 data sets according to the longitudinal zone that each voxel belongs to. These results can be compared with the Level 3 visualizations in Figures 4 and 5, which displays the reduced data according to the transversal segment that each voxel belongs to. We can observe from the t-SNE results that voxels from the same longitudinal zones do not form clear clusters in comparison to the clustering patterns along the transversal dimension. In general, voxels belongs to the alar plate and basal plate form clear clusters, while those in the roof plate and floor plate tend to be widely distributed. However, we can observe that voxels in the roof and alar plates are usually close to each other, and those in the basal and floor plates tend to form clusters. This shows that our computational results are more consistent with the segmental model, which is also supported by recent experimental evidences.


Computational genetic neuroanatomy of the developing mouse brain: dimensionality reduction, visualization, and clustering.

Ji S - BMC Bioinformatics (2013)

Visualization of the Allen Developing Mouse Brain Atlas data for ages E11.5 and P28 after projecting to 2-D space using t-SNE and PCA. Each point corresponds to a brain voxel, which is displayed according to the longitudinal zones (F=floor, B=basal, A=alar, R=roof) it belongs to. Results for other ages are shown in the Additional file 1.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3750329&req=5

Figure 6: Visualization of the Allen Developing Mouse Brain Atlas data for ages E11.5 and P28 after projecting to 2-D space using t-SNE and PCA. Each point corresponds to a brain voxel, which is displayed according to the longitudinal zones (F=floor, B=basal, A=alar, R=roof) it belongs to. Results for other ages are shown in the Additional file 1.
Mentions: To provide in-depth visual exploration of the genetic neuroanatomy along the longitudinal and transversal dimensions, we display in Figure 6 the E11.5 and P28 data sets according to the longitudinal zone that each voxel belongs to. These results can be compared with the Level 3 visualizations in Figures 4 and 5, which displays the reduced data according to the transversal segment that each voxel belongs to. We can observe from the t-SNE results that voxels from the same longitudinal zones do not form clear clusters in comparison to the clustering patterns along the transversal dimension. In general, voxels belongs to the alar plate and basal plate form clear clusters, while those in the roof plate and floor plate tend to be widely distributed. However, we can observe that voxels in the roof and alar plates are usually close to each other, and those in the basal and floor plates tend to form clusters. This shows that our computational results are more consistent with the segmental model, which is also supported by recent experimental evidences.

Bottom Line: Our results show that the developing brain anatomy is largely preserved in the reduced gene expression space.To provide a quantitative analysis, we cluster the reduced data into groups and measure the consistency with neuroanatomy at multiple levels.Dimensionality reduction and visual exploration facilitate the study of this relationship.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Computer Science, Old Dominion University, 4700 Elkhorn Avenue, Suite 3300, Norfolk, VA 23529-0162, USA. sji@cs.odu.edu

ABSTRACT

Background: The structured organization of cells in the brain plays a key role in its functional efficiency. This delicate organization is the consequence of unique molecular identity of each cell gradually established by precise spatiotemporal gene expression control during development. Currently, studies on the molecular-structural association are beginning to reveal how the spatiotemporal gene expression patterns are related to cellular differentiation and structural development.

Results: In this article, we aim at a global, data-driven study of the relationship between gene expressions and neuroanatomy in the developing mouse brain. To enable visual explorations of the high-dimensional data, we map the in situ hybridization gene expression data to a two-dimensional space by preserving both the global and the local structures. Our results show that the developing brain anatomy is largely preserved in the reduced gene expression space. To provide a quantitative analysis, we cluster the reduced data into groups and measure the consistency with neuroanatomy at multiple levels. Our results show that the clusters in the low-dimensional space are more consistent with neuroanatomy than those in the original space.

Conclusions: Gene expression patterns and developing brain anatomy are closely related. Dimensionality reduction and visual exploration facilitate the study of this relationship.

Show MeSH
Related in: MedlinePlus