Print Email Facebook Twitter Visualizing the spatial gene expression organization in the brain through non-linear similarity embeddings Title Visualizing the spatial gene expression organization in the brain through non-linear similarity embeddings Author Mahfouz, A.M.E.T.A. Van de Giessen, M. Van der Maaten, L.J.P. Huisman, S.M.H. Reinders, M.J.T. Hawrylycz, M.J. Lelieveldt, B.P.F. Faculty Electrical Engineering, Mathematics and Computer Science Department Intelligent Systems Date 2014-10-16 Abstract The Allen Brain Atlases enable the study of spatially resolved, genome-wide gene expression patterns across the mammalian brain. Several explorative studies have applied linear dimensionality reduction methods such as Principal Component Analysis (PCA) and classical Multi-Dimensional Scaling (cMDS) to gain insight into the spatial organization of these expression patterns. In this paper, we describe a non-linear embedding technique called Barnes-Hut Stochastic Neighbor Embedding (BH-SNE) that emphasizes the local similarity structure of high-dimensional data points. By applying BH-SNE to the gene expression data from the Allen Brain Atlases, we demonstrate the consistency of the 2D, non-linear embedding of the sagittal and coronal mouse brain atlases, and across 6 human brains. In addition, we quantitatively show that BH-SNE maps are superior in their separation of neuroanatomical regions in comparison to PCA and cMDS. Finally, we assess the effect of higher-order principal components on the global structure of the BH-SNE similarity maps. Based on our observations, we conclude that BH-SNE maps with or without prior dimensionality reduction (based on PCA) provide comprehensive and intuitive insights in both the local and global spatial transcriptome structure of the human and mouse Allen Brain Atlases. Subject Allen Brain Atlasspatial-mapped gene expressionbrain transcriptome structuredimensionality reductionStochastic Neighbor Embedding To reference this document use: http://resolver.tudelft.nl/uuid:e8921b7d-f463-4f7a-a729-50f7a622834a DOI https://doi.org/10.1016/j.ymeth.2014.10.004 Publisher Elsevier ISSN 1046-2023 Source Methods, 73, 2015 Part of collection Institutional Repository Document type journal article Rights (c) 2014 The AuthorsThis is an open access article under the CC BY-NC-ND license Files PDF Mahfouz 2014.pdf 5.27 MB Close viewer /islandora/object/uuid:e8921b7d-f463-4f7a-a729-50f7a622834a/datastream/OBJ/view