Big Data and Neurodegeneration Disorders

Big data analysis (e.g., of RNA-seq data) usually involves machine learning and statistical computational analyses. Meticulous analysis might assist us to better understand the molecular mechanisms that underlie devastative neurodegenerative diseases such as Alzheimer’s and Parkinson’s diseases (AD and PD, respectively). Here, I review methods for big data analysis specifically from neurodegenerative disease patients. The computational methods may include sample classification (e.g., PCA/HCL/t-SNE), cell-type-specific analysis, network analyses, and computation of statistical p-value. Additionally, meticulous analyses of alternative splicing and inflammation may be involved. The software for analyses may include MATLAB, R MySQL, and Python. Specifically, single-cell data may be analyzed. Furthermore, the data may be subsequently compared to data from young compared to old postmortem brain sample exon microarray data (produced by the UKBEC). So far several genes were identified as involved in AD including microglia and neuronal cell marker genes (e.g., TREM, CD33). However the majority of disease molecular mechanisms and underlying genes are still largely unclear. Validation of cell-type-specific findings may include cell specific quantification (e.g., of oligodendrocytes or neurons). Recently, a large cohort of amyotrophic lateral sclerosis (ALS) patient’s genomic data was also collected and may be subjected to analysis. Specifically, protein domains related to the disease progression may be identified. The samples are typically collected from human patient’s blood. These research avenues may provide hope for future genomic therapeutics for these devastative diseases.