Comprehensive Guide to Gene Expression Analysis from Bulk and Single-Nucleus Transcriptomes

RNA sequencing is a revolutionary technique for studying gene expression, providing a global transcriptome landscape that helps us understand disease-associated patterns. To gain insights into cellular heterogeneity, the advancement of single-nucleus RNA sequencing (snRNA-seq) has emerged as a powerful tool for analyzing gene expression at the single-cell level. The data generated by these techniques are massive, and handling such large datasets requires knowledge of big data analysis and machine learning methods to effectively manage and interpret the data. In this chapter, we cover quality control, normalization, sparse data handling, and transcript quantification for both bulk and snRNA-seq, along with strategies to address challenges such as multi-mapped reads and batch effects. Additionally, we examine how artificial intelligence and machine learning techniques can improve data processing and disease-associated pattern recognition. By comparing different methodologies, we highlight the strengths and limitations of both approaches and provide guidance on selecting the most appropriate strategy based on specific research objectives.