Prediction of a Cell-Class-Specific Mouse Mesoconnectome Using Gene Expression Data
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ORIGINAL ARTICLE
Prediction of a Cell-Class-Specific Mouse Mesoconnectome Using Gene Expression Data Nestor Timonidis1
· Rembrandt Bakker1,2 · Paul Tiesinga1
© The Author(s) 2020
Abstract Reconstructing brain connectivity at sufficient resolution for computational models designed to study the biophysical mechanisms underlying cognitive processes is extremely challenging. For such a purpose, a mesoconnectome that includes laminar and cell-class specificity would be a major step forward. We analyzed the ability of gene expression patterns to predict cell-class and layer-specific projection patterns and assessed the functional annotations of the most predictive groups of genes. To achieve our goal we used publicly available volumetric gene expression and connectivity data and we trained computational models to learn and predict cell-class and layer-specific axonal projections using gene expression data. Predictions were done in two ways, namely predicting projection strengths using the expression of individual genes and using the co-expression of genes organized in spatial modules, as well as predicting binary forms of projection. For predicting the strength of projections, we found that ridge (L2-regularized) regression had the highest cross-validated accuracy with a median r 2 score of 0.54 which corresponded for binarized predictions to a median area under the ROC value of 0.89. Next, we identified 200 spatial gene modules using a dictionary learning and sparse coding approach. We found that these modules yielded predictions of comparable accuracy, with a median r 2 score of 0.51. Finally, a gene ontology enrichment analysis of the most predictive gene groups resulted in significant annotations related to postsynaptic function. Taken together, we have demonstrated a prediction workflow that can be used to perform multimodal data integration to improve the accuracy of the predicted mesoconnectome and support other neuroscience use cases. Keywords Spatial gene co-expression · Connectomics · Machine learning · Predictive models · Mouse brain · Axonal projection · Gene expression · Gene ontology enrichment analysis · Ridge regression · Dictionary learning · Sparse coding · ROC analysis · Cellularly resolved connectome
Introduction A wiring diagram of the brain (connectome) is a necessary step for advancing modern neuroscience for two reasons. Electronic supplementary material The online version of this article (https://doi.org/10.1007/s12021-020-09471-x) contains supplementary material, which is available to authorized users. Nestor Timonidis
[email protected] 1
Neuroinformatics Department, Donders Centre for Neuroscience, Radboud University Nijmegen, Heyendaalseweg 135, 6525 AJ Nijmegen, the Netherlands
2
Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA BRAIN Institute I, J¨ulich Research Centre, Wilhelm-Johnen-Strasse, 52425 J¨ulich, Germany
First, it assists computational neuroscience by providing biologically plausible constraints on brain mode
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