Differential Co-Expression Networks (DCENs) are graphical representations of genes showing differential co-expression pattern in response to experimental conditions or genetic changes. They have been successfully applied to identify condition-specific modules and provide a picture of the dynamic changes in gene regulatory networks. DCENs analysis investigates the differences among gene interconnections by calculating the expression correlation change of each gene pair between conditions. In this study, we collected many different datasets from NCBI GEO including 25 RNA-seq and 2,102 microarray samples derived from human brain and blood in Alzheimer's disease and performed differential co-expression analyses to identify functional modules responsible for the characterization of Alzheimer's disease. The DCENs were generated using Pearson correlation coefficient and meta-analysis was conducted using rank-based method. The preliminary results show that the structural characteristics of DCENs can provide new insights into the underlying gene regulatory dynamics in Alzheimer's disease. There is low size overlap between microarray- and RNA-seq-derived DCENs however, DCENs from RNA-seq would complement ones from microarray due to the higher coverage and dynamic range of RNA-seq.