Analysis of Gene Expression Profiles in the Human Brain Stem, Cerebellum and Cerebral Cortex

Abstract

The human brain is one of the most mysterious tissues in the body. Our knowledge of the human brain is limited due to the complexity of its structure and the microscopic nature of connections between brain regions and other tissues in the body. In this study, we analyzed the gene expression profiles of three brain regions-the brain stem, cerebellum and cerebral cortex-to identify genes that are differentially expressed among these different brain regions in humans and to obtain a list of robust, region-specific, differentially expressed genes by comparing the expression signatures from different individuals. Feature selection methods, specifically minimum redundancy maximum relevance and incremental feature selection, were employed to analyze the gene expression profiles. Sequential minimal optimization, a machine-learning algorithm, was employed to examine the utility of selected genes. We also performed a literature search, and we discuss the experimental evidence for the important physiological functions of several highly ranked genes, including NR2E1, DAO, and LRRC7, and we give our analyses on a gene (TFAP2B) that have not been investigated or experimentally validated. As a whole, the results of our study will improve our ability to predict and understand genes related to brain regionalization and function.

Fig 1. A 3-D histogram illustrating the number of features in the feature set yielding the maximum total prediction accuracy and the corresponding total prediction accuracy.

The height of the bar represents the maximum total prediction accuracy, whereas the color of the bar represents the number of features in the feature set yielding the maximum total prediction accuracy.

Fig 2. Parts of five IFS-curves using the data of H0351.1009 as the training dataset and the data from other people as the test dataset.

The triangle in each curve represents the inflection point.

Fig 3. Parts of five IFS-curves using the data of H0351.1012 as the training dataset and the data from other people as the test dataset.

The triangle in each curve represents the inflection point.

Fig 4. Parts of five IFS-curves using the data of H0351.1015 as the training dataset and the data from other people as the test dataset.

The triangle in each curve represents the inflection point.

Fig 5. Parts of five IFS-curves using the data of H0351.1016 as the training dataset and the data from other people as the test dataset.

The triangle in each curve represents the inflection point.

Fig 6. Parts of five IFS-curves using the data of H0351.2001 as the training dataset and the data from other people as the test dataset.

The triangle in each curve represents the inflection point.

Fig 7. Parts of five IFS-curves using the data of H0351.2002 as the training dataset and the data from other people as the test dataset.

The triangle in each curve represents the inflection point.

Fig 8. A 3-D histogram illustrating the number of features of each inflection point and the corresponding total prediction accuracy.

The height of the bar represents the total prediction accuracy, whereas the color of the bar represents the number of features of each inflection point.