NDLI: Analysis of low-correlated spatial gene expression patterns: a clustering approach in the mouse brain data hosted in the Allen Brain Atlas

Content Provider	IET Digital Library
Author	Rosati, Paolo Lupaşcu, Carmen A. Tegolo, Domenico
Abstract	The Allen Brain Atlas (ABA) provides a similar gene expression dataset by genome-scale mapping of the C57BL/6J mouse brain. In this study, the authors describe a method to extract the spatial information of gene expression patterns across a set of 1047 genes. The genes were chosen from among the 4104 genes having the lowest Pearson correlation coefficient used to compare the expression patterns across voxels in a single hemisphere for available coronal and sagittal volumes. The set of genes analysed in this study is the one discarded in the article by Bohland et al., which was considered to be of a lower consistency, not a reliable dataset. Following a normalisation task with a global and local approach, voxels were clustered using hierarchical and partitioning clustering techniques. Cluster analysis and a validation method based on entropy and purity were performed. They analyse the resulting clusters of the mouse brain for different number of groups and compared them with a classically-defined anatomical reference atlas. The high degree of correspondence between clusters and anatomical regions highlights how gene expression patterns with a low Pearson correlation coefficient between sagittal and coronal sections can accurately identify different neuroanatomical regions.
Starting Page	996
Ending Page	1006
Page Count	11
ISSN	17519632
Volume Number	12
e-ISSN	17519640
Issue Number	Issue 7, Oct (2018)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-cvi/12/7
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-cvi.2018.5217
Journal	IET Computer Vision
Publisher Date	2018-08-06
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Allen Brain Atlas Biology And Medical Computing Biology Computing Biomedical Measurement And Imaging Biophysics of Neurophysiological Processes Brain C57BL/6J Mouse Brain Classically-defined Anatomical Reference Atlas Cluster Analysis Clustering Approach Computer Vision And Image Processing Technique Coronal Sections Data Handling Technique Energy 6.0 J Genetics Genome-scale Mapping Genomics Health Physics Hierarchical Clustering Technique Image Registration Low-correlated Spatial Gene Expression Pattern Medical And Biomedical Uses of Field Medical Image Processing Mouse Brain Data Neuroanatomical Region Neurophysiology Optical, Image And Video Signal Processing Partitioning Clustering Technique Patient Diagnostic Method And Instrumentation Pattern Clustering Pearson Correlation Coefficient Radiations Radioactivity Sagittal Sections Spatial Information
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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