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

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.

Sleep Quality Detection Based on EEG Signals Using Transfer Support Vector Machine Algorithm

“…At present, the research direction of sleep quality mainly focuses on the study of sleep staging. With the continuous development of computer technology, many machine learning methods had been proposed and used in the medical applications (Bezdek, 1980(Bezdek, , 1981Hall et al, 1992;Ahmed et al, 2002;Pedrycz, 2002;Chen and Zhang, 2004;Chuang et al, 2006;Weijer and Gevers, 2006;Jing et al, 2007;Cleuziou et al, 2009;Gu and Zhou, 2009;Zhu et al, 2009;Krinidis and Chatzis, 2010;Hall and Goldgof, 2011;Ji et al, 2011;Jiang et al, 2012Jiang et al, , 2014Jiang et al, , 2020Yu et al, 2012;Chen et al, 2013;Gong et al, 2013;Thanh and Wu, 2013;Li et al, 2014;Elazab et al, 2015;Okita et al, 2015;Qian et al, 2015;Wang et al, 2015Wang et al, , 2019Zheng et al, 2015;Devi and Setty, 2018;Lee et al, 2018;Rosati et al, 2018;Cai et al, 2019;Gu et al, 2019;Chrobak et al, 2020;Kumar et al, 2020;Liu et al, 2020;Singh et al, 2020;Sunjana and Azizah, 2020;Yin et al, 2020;…”

Novel modelling of clustering for enhanced classification performance on gene expression data

“…The approach uses a profiling mechanism for identification of disease condition using fusion-based mechanism. The work of Rosati et al [13] has analyzed gene expression data where a hierarchical clustering approach mainly has been introduced using spatial information of the associated pattern. Existing system also finds that low rank clustering is another frequency used mechanism for analyzing complex medical data.…”