Replicability of spatial gene expression atlas data from the adult mouse brain

Lu, Shaina, Ortiz, Cantin, Fürth, Daniel, Fischer, Stephan, Meletis, Konstantinos, Zador, Anthony, Gillis, Jesse (October 2020) Replicability of spatial gene expression atlas data from the adult mouse brain. bioRxiv. (Unpublished)

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Abstract

<h4>Background</h4> Spatial gene expression is particularly interesting in the mammalian brain, with the potential to serve as a link between many data types. However, as with any type of expression data, cross-dataset benchmarking of spatial data is a crucial first step. Here, we assess the replicability, with reference to canonical brain sub-divisions, between the Allen Institute’s in situ hybridization data from the adult mouse brain (ABA) and a similar dataset collected using Spatial Transcriptomics (ST). With the advent of tractable spatial techniques, for the first time we are able to benchmark the Allen Institute’s whole-brain, whole-transcriptome spatial expression dataset with a second independent dataset that similarly spans the whole brain and transcriptome. <h4>Results</h4> We use LASSO, linear regression, and correlation-based feature selection in a supervised learning framework to classify expression samples relative to their assayed location. We show that Allen reference atlas labels are classifiable using transcription, but that performance is higher in the ABA than ST. Further, models trained in one dataset and tested in the opposite dataset do not reproduce classification performance bi-directionally. Finally, while an identifying expression profile can be found for a given brain area, it does not generalize to the opposite dataset. <h4>Conclusions</h4> In general, we found that canonical brain area labels are classifiable in gene expression space within dataset and that our observed performance is not merely reflecting physical distance in the brain. However, we also show that cross-platform classification is not robust. Emerging spatial datasets from the mouse brain will allow further characterization of cross-dataset replicability.

Item Type:	Paper
Subjects:	Investigative techniques and equipment > brain atlas bioinformatics > genomics and proteomics > genetics & nucleic acid processing > DNA, RNA structure, function, modification > genes, structure and function > gene expression organism description > animal > mammal > rodent > mouse
CSHL Authors:	Fischer, Stephan Zador, Anthony M. Gillis, Jesse Lu, Shaina Furth, Daniel
Communities:	CSHL labs > Gillis Lab CSHL labs > Zador lab School of Biological Sciences > Publications
SWORD Depositor:	CSHL Elements
Depositing User:	CSHL Elements
Date:	17 October 2020
Date Deposited:	06 May 2021 19:57
Last Modified:	29 Feb 2024 19:20
URI:	https://repository.cshl.edu/id/eprint/40032

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