Cell-type-specific transcriptomes and the Allen Atlas (II): discussion of the linear model of brain-wide densities of cell types

Abstract

The voxelized Allen Atlas of the adult mouse brain (at a resolution of 200 microns) has been used in [arXiv:1303.0013] to estimate the region-specificity of 64 cell types whose transcriptional profile in the mouse brain has been measured in microarray experiments. In particular, the model yields estimates for the brain-wide density of each of these cell types. We conduct numerical experiments to estimate the errors in the estimated density profiles. First of all, we check that a simulated thalamic profile based on 200 well-chosen genes can transfer signal from cerebellar Purkinje cells to the thalamus. This inspires us to sub-sample the atlas of genes by repeatedly drawing random sets of 200 genes and refitting the model. This results in a random distribution of density profiles, that can be compared to the predictions of the model. This results in a ranking of cell types by the overlap between the original and sub-sampled density profiles. Cell types with high rank include medium spiny neurons, several samples of cortical pyramidal neurons, hippocampal pyramidal neurons, granule cells and cholinergic neurons from the brain stem. In some cases with lower rank, the average sub-sample can have better contrast properties than the original model (this is the case for amygdalar neurons and dopaminergic neurons from the ventral midbrain). Finally, we add some noise to the cell-type-specific transcriptomes by mixing them using a scalar parameter weighing a random matrix. After refitting the model, we observe than a mixing parameter of $5\%$ leads to modifications of density profiles that span the same interval as the ones resulting from sub-sampling.

178 pages, 207 figures, 7 tables; v2: typos corrected; v3: more typos corrected, missing pseudo-code in section 3.5 written, image attachment changed in Figure 6, misuse of notation $r^{signal}$ corrected, conclusions unchanged

References in corpus (4)

• Brain Gene Expression Analysis: a MATLAB toolbox for the analysis of brain-wide gene-expression data
• Cell-type-specific microarray data and the Allen atlas: quantitative analysis of brain-wide patterns of correlation and density
• Computational neuroanatomy and gene expression: optimal sets of marker genes for brain regions
• Computational neuroanatomy and co-expression of genes in the adult mouse brain, analysis tools for the Allen Brain Atlas

Cited by in corpus (3)

• Cell-type-specific computational neuroanatomy, simulations from the sagittal and coronal Allen Brain Atlas
• Cell-type-specific neuroanatomy of brain-wide expression of autism-related genes
• Computational neuroanatomy: mapping cell-type densities in the mouse brain, simulations from the Allen Brain Atlas