Automated identification of the mouse brain's spatial compartments from in situ sequencing data.

Gabriele PartelMarkus M HilscherGiorgia MilliLeslie SolorzanoAnna H KlemmMats NilssonCarolina Wählby

Published in: BMC biology (2020)

We show that spatial gene expression data can be used for unsupervised and unbiased annotations of mouse brain spatial compartments based only on molecular markers, without the need of subjective manual annotations based on tissue and cell morphology or matching reference atlases.

Keyphrases

gene expression
machine learning
single cell
electronic health record
big data
dna methylation
high throughput
deep learning
stem cells
depressive symptoms
sleep quality
data analysis
artificial intelligence
bone marrow
bioinformatics analysis