The Research On Methods Of Gene Co-expression Network Analysis Of Sleep Deprivation

Sleep deprivation is a state that one can not sleep regularly, the research of sleep deprivation can not only recover the function and mechanism but also provide corresponding defence mechanism. Recently, there are some methods for sleep deprivation, such as brain image, EEG and gene expression. But these methods focus on difference of the special brain region or gene expression level. More and more studies show that the properties and functions of organism are determined by the complex gene network rather than some special gene. This thesis try to construct gene co-expression networks based on the mouse brain gene expression data of different sleep states and then compare and analyze the structure of the networks under the framework of complex network theory to research sleep deprivation problem. The main work and contributions of this thesis are as follows:( 1) This thesis construct and compare the gene co-expression networks of mouse brain to explore the structural alternation of co-expression networks during the sleep and wake. To make our networks more precise and strict, we take permutation to avoid the influence of random factors. From the results of comparison, we find that under the condition of undeprivated sleep, wake mouse owns more orderly co-expression network. When the sleep deprivation measures are taken, the co-expression network becomes more random. And after the sleep recovery after deprivation, the co-expression network is still more random and fragmental. Thus, we consider sleep deprivation cause damages to gene co-expression network, while sleep recovery cannot make it up.( 2) This thesis propose a fast binary image segmentation method, which can efficiently detect the spatially restricted expression regions of a gene and/or co-expression regions between genes. A naive permutation approach is applied to assess the statistical significance of the detected regions. Our method is expected to provide a faster and more efficient measure to construct more exact gene co-expression network to recover the mechanism of sleep.