Abstract：
RNA-Seq data analysis stands as a vital part of genomics research, turning vast and complex datasets into meaningful biological insights. It is a field marked by rapid evolution and ongoing innovation, necessitating a thorough understanding for anyone seeking to unlock the potential of RNA-Seq data. In this chapter, we describe the intricate landscape of RNA-seq data analysis, elucidating a comprehensive pipeline that navigates through the entirety of this complex process. Beginning with quality control, the chapter underscores the paramount importance of ensuring the integrity of RNA-seq data, as it lays the groundwork for subsequent analyses. Preprocessing is then addressed, where the raw sequence data undergoes necessary modifications and enhancements, setting the stage for the alignment phase. This phase involves mapping the processed sequences to a reference genome, a step pivotal for decoding the origins and functions of these sequences.Venturing into the heart of RNA-seq analysis, the chapter then explores differential expression analysis-the process of identifying genes that exhibit varying expression levels across different conditions or sample groups. Recognizing the biological context of these differentially expressed genes is pivotal; hence, the chapter transitions into functional analysis. Here, methods and tools like Gene Ontology and pathway analyses help contextualize the roles and interactions of the identified genes within broader biological frameworks. However, the chapter does not stop at conventional analysis methods. Embracing the evolving paradigms of data science, it delves into machine learning applications for RNA-seq data, introducing advanced techniques in dimension reduction and both unsupervised and supervised learning. These approaches allow for patterns and relationships to be discerned in the data that might be imperceptible through traditional methods.
摘要：
RNA-Seq数据分析是基因组学研究的重要组成部分，将庞大而复杂的数据集转化为有意义的生物学见解。这是一个以快速发展和持续创新为标志的领域，任何寻求释放RNA-Seq数据潜力的人都需要彻底了解。在这一章中,我们描述了RNA-seq数据分析的复杂局面，阐明一个全面的管道，导航通过整个复杂的过程。从质量控制开始，本章强调确保RNA-seq数据的完整性至关重要，因为它为后续分析奠定了基础。然后处理预处理，其中原始序列数据经过必要的修改和增强，设置校准阶段的阶段。这个阶段涉及将处理过的序列映射到参考基因组，解码这些序列的起源和功能的关键步骤。进入RNA-seq分析的核心，然后，本章探讨了差异表达分析-鉴定在不同条件或样本组中表现出不同表达水平的基因的过程。认识到这些差异表达基因的生物学背景至关重要；因此，本章过渡到功能分析。这里,基因本体论和通路分析等方法和工具有助于在更广泛的生物学框架内了解已识别基因的作用和相互作用。然而,本章并不停留在传统的分析方法上。拥抱不断发展的数据科学范式，它深入研究了RNA-seq数据的机器学习应用，在降维以及无监督和监督学习方面引入先进技术。这些方法允许在数据中辨别通过传统方法可能难以察觉的模式和关系。
