Open AccessAnalysis of low‐correlated spatial gene expression patterns: a clustering approach in the mouse brain data hosted in the Allen Brain Atlas
Author(s) -
Rosati Paolo,
Lupaşcu Carmen A.,
Tegolo Domenico
Publication year - 2018
Publication title -
iet computer vision
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.38
H-Index - 37
eISSN - 1751-9640
pISSN - 1751-9632
DOI - 10.1049/iet-cvi.2018.5217
Subject(s) - brain atlas , voxel , pearson product moment correlation coefficient , atlas (anatomy) , cluster analysis , hierarchical clustering , pattern recognition (psychology) , artificial intelligence , correlation , computational biology , clustering coefficient , computer science , gene , data set , sagittal plane , biology , genetics , mathematics , statistics , anatomy , geometry
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.