Cell-type-specific computational neuroanatomy, simulations from the sagittal and coronal Allen Brain Atlas

Grange, Pascal

doi:10.48550/arxiv.1505.06434

Cited by 1 publication

(1 citation statement)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moreover, we estimated the densities of the contribution of each region in the coarsest version of the ARA to the total density of each cell type in the data set. For most cell types, this confirms the ranking of cell types by stability obtained in [4], but based on error bars obtained from the same set of genes in every fitting of the model (see the accompanying preprint [26] for exhaustive results for all cell types in the panel). The most stable results against sub-sampling tend to correspond to cell types for which the anatomical distribution of results is more peaked.…”

Section: Anatomical Analysis Of Resultssupporting

confidence: 77%

Computational neuroanatomy: mapping cell-type densities in the mouse brain, simulations from the Allen Brain Atlas

Grange

2015

J. Phys.: Conf. Ser.

Self Cite

View full text Add to dashboard Cite

The Allen Brain Atlas (ABA) of the adult mouse consists of digitized expression profiles of thousands of genes in the mouse brain, co-registered to a common three-dimensional template (the Allen Reference Atlas). This brain-wide, genome-wide data set has triggered a renaissance in neuroanatomy. Its voxelized version (with cubic voxels of side 200 microns) can be analyzed on a desktop computer using MATLAB. On the other hand, brain cells exhibit a great phenotypic diversity (in terms of size, shape and electrophysiological activity), which has inspired the names of some well-studied cell types, such as granule cells and medium spiny neurons. However, no exhaustive taxonomy of brain cells is available. A genetic classification of brain cells is under way, and some cell types have been characterized by their transcriptome profiles. However, given a cell type characterized by its transcriptome, it is not clear where else in the brain similar cells can be found. The ABA can been used to solve this region-specificity problem in a data-driven way: rewriting the brain-wide expression profiles of all genes in the atlas as a sum of cell-type-specific transcriptome profiles is equivalent to solving a quadratic optimization problem at each voxel in the brain. However, the estimated brain-wide densities of 64 cell types published recently were based on one series of co-registered coronal in situ hybridization (ISH) images per gene, whereas the online ABA contains several image series per gene, including sagittal ones. In the presented work, we simulate the variability of cell-type densities in a Monte Carlo way by repeatedly drawing a random image series for each gene and solving optimization problems. This yields error bars on the region-specificity of cell types.

show abstract

Section: Anatomical Analysis Of Resultssupporting

confidence: 77%