In this paper, clustering techniques are applied to spatial gene expression patterns with a low genomic correlation between the sagittal and coronal projections. The data analysed here are hosted on an available public DB named ABA (Allen Brain Atlas). The results are compared to those obtained by Bohland et al. on the complementary dataset (high correlation values). We prove that, by analysing a reduced dataset,hence reducing the computational burden, we get the same accuracy in highlighting different neuroanatomical region.
Rosati P., Lupascu C.A., Tegolo D. (2018). Clustering of low-correlated spatial gene expression patterns in the mouse brain in the Allen Brain Atlas. In Proceedings of the 2nd International Conference on Computing and Network Communications, CoCoNet 2018 (pp. 50-57). 345 E 47TH ST, NEW YORK, NY 10017 USA : Institute of Electrical and Electronics Engineers Inc. [10.1109/CoCoNet.2018.8476886].
Clustering of low-correlated spatial gene expression patterns in the mouse brain in the Allen Brain Atlas
Tegolo, Domenico
Membro del Collaboration Group
2018-01-01
Abstract
In this paper, clustering techniques are applied to spatial gene expression patterns with a low genomic correlation between the sagittal and coronal projections. The data analysed here are hosted on an available public DB named ABA (Allen Brain Atlas). The results are compared to those obtained by Bohland et al. on the complementary dataset (high correlation values). We prove that, by analysing a reduced dataset,hence reducing the computational burden, we get the same accuracy in highlighting different neuroanatomical region.
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