  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.
