Analysis of low‐correlated spatial gene expression patterns: a clustering approach in the mouse brain data hosted in the Allen Brain Atlas. Issue 7 (31st August 2018)

• Record Type: Journal Article
• Authors: Rosati, Paolo; Lupaşcu, Carmen A.; Tegolo, Domenico
• Is Part Of: IET computer vision. Volume 12:Issue 7(2018)
• Page Start: 996
• Page End: 1006
• Publication Date: 2018-08-31
• Subjects: genetics -- brain -- genomics -- biology computing -- medical image processing -- image registration -- neurophysiology -- pattern clustering; low-correlated spatial gene expression patterns -- clustering approach -- mouse brain data -- Allen Brain Atlas -- genome-scale mapping -- spatial information -- hierarchical clustering techniques -- partitioning clustering techniques -- cluster analysis -- classically-defined anatomical reference atlas -- C57BL/6J mouse brain -- Pearson correlation coefficient -- sagittal sections -- coronal sections -- neuroanatomical regions -- energy 6.0 J; Computer vision -- Periodicals; Pattern recognition systems -- Periodicals; 006.37
• DOI: 10.1049/iet-cvi.2018.5217
• Languages: English
• ISSNs: 1751-9632

Abstract

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.