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所在平台: Udemy |
课程主页: https://www.udemy.com/course/learn-single-cell-rna-seq-data-analysis-using-r-python/
课程评论：没有评论
课程名称：使用R与Python学习单细胞RNA测序数据分析 课程概述： 欢迎参加我们的综合课程“使用R、Python、图形用户界面工具和云平台学习单细胞RNA测序数据分析”，该课程旨在让您掌握探索基因组学中最先进的技术之一——单细胞RNA测序（scRNA-seq）的必要技能和知识。在精确生物学的时代，理解单个细胞的行为对于揭示癌症生物学、免疫学、神经科学和再生医学方面的洞见至关重要，scRNA-seq已成为一项变革性工具。本课程是进入单细胞转录组学领域的完整入门指南，从基础理论到使用最强大的开源工具进行数据分析，帮助您掌握相关技能。 为什么选择单细胞RNA测序？ 与传统的批量RNA测序（在其中基因表达是横跨成千上万或数百万个细胞的平均值）相比，scRNA-seq使您能够以单个细胞的分辨率分析基因表达，揭示组织中的隐藏异质性，发现稀有细胞类型，并为疾病理解和治疗策略提供新的维度。 单细胞时代的生物信息学： 本课程的核心是生物信息学的跨学科力量——生物学、计算机科学和数据科学的融合。您将掌握驱动现代单细胞数据分析的计算方法和统计框架，学习如何解读、可视化和从复杂数据集中提取有意义的生物学见解。 全面的学习旅程： 从单细胞技术的基础知识到全流程分析工作流，本课程将为您清晰而实用地讲解每个单细胞RNA测序数据分析的步骤。 多工具掌握： 您将获得使用基于R和Python的分析流程的实践经验，使用强大的库如Seurat和Scanpy，同时探索如Galaxy等图形用户界面和基于云的平台，适合非编码者使用。 真实世界的数据： 使用来自公共数据库（如NCBI GEO）的真实scRNA-seq数据集，让您在实际生物数据中自信地应用您的技能。 专家指导： 学习经验丰富的生物信息学教育工作者，他们将复杂的概念分解为可管理的可操作的学习步骤。 课程内容包括： 第一部分：单细胞RNA测序简介 - 什么是scRNA-seq及其与批量RNA-seq的不同 - 医学与生物学中的实际应用 - 整个分析流程的清晰概述 第二部分：R与RStudio入门 - 为生物学家介绍R编程 - 安装RStudio和理解数据结构 - 导入数据、管理包，并使用ggplot2和Seurat创建可视化 第三部分：使用R进行单细胞RNA测序分析 - 使用Seurat预处理scRNA-seq数据 - 质量控制、标准化、缩放和主成分分析（PCA） - 聚类、UMAP可视化、标记基因识别和细胞类型注释 第四部分：使用Python进行单细胞RNA测序分析 - 使用Scanpy和scVI工具 - 在Python中复制完整的工作流程 - 使用诸如scANVI等模型进行高级注释 第五部分：图形用户界面和基于云的流程 - 无需编码即可运行scRNA-seq分析 - 使用Galaxy、CodeOcean或Cellxgene等平台 - 直观地下载和分析GEO数据集 适合人群： - 转向生物信息学的生物学或医学学生 - 生物信息学初学者和研究人员 - 探索单细胞应用的数据科学家 - 希望分析自己scRNA-seq数据的实验室科学家 - 对高分辨率转录组学感兴趣的任何人 课程要求： - 无需之前的编码经验，课程适合初学者 - 所使用的所有工具均为开源和免费 - 对生物学的基本了解或对基因组学的兴趣将是加分项 加入我们这个沉浸式的学习体验，获得全套单细胞RNA测序分析流程的运作能力，解读结果，并在前沿研究中做出有意义的贡献。无论您是希望建立研究生涯还是增强生命科学专业技能，本课程将是您掌握使用R、Python、图形用户界面工具和云平台的单细胞生物信息学的门户。
Welcome to our comprehensive course, "Learn Single-Cell RNA-Seq Data Analysis Using R, Python, GUI Tools, and Cloud Platforms," designed to empower you with the essential skills and knowledge to explore one of the most advanced and high-resolution technologies in genomics: Single-Cell RNA Sequencing (scRNA-seq).In the era of precision biology, where understanding individual cellular behavior is critical to unlocking insights in cancer biology, immunology, neuroscience, and regenerative medicine, scRNA-seq has emerged as a transformative tool. This course serves as your complete entry point into the field of single-cell transcriptomics, guiding you from foundational theory to hands-on data analysis using the most powerful open-source tools available.Why Single-Cell RNA-Seq?With the limitations of traditional bulk RNA sequencing-where gene expression is averaged across thousands or millions of cells-scRNA-seq allows you to analyze gene expression at single-cell resolution, revealing the hidden heterogeneity in tissues, uncovering rare cell types, and providing new dimensions to disease understanding and treatment strategies.Bioinformatics for the Single Cell EraAt the heart of this course is the interdisciplinary power of bioinformatics-a fusion of biology, computer science, and data science. You'll master the computational approaches and statistical frameworks that drive modern single-cell data analysis, learning how to interpret, visualize, and extract meaningful biological insights from complex datasets.Comprehensive Learning Journey: From the basics of single-cell technology to full-scale analysis workflows, this course walks you through every step of scRNA-seq data analysis with clarity and practical insight.Multi-Tool Mastery: You'll gain hands-on experience with both R and Python-based analysis pipelines, using powerful libraries like Seurat and Scanpy, while also exploring graphical interfaces and cloud-based platforms like Galaxy for non-coders.Real-World Data: Work with real scRNA-seq datasets from public repositories like NCBI GEO, giving you confidence in applying your skills to actual biological data.Expert Instruction: Learn from experienced bioinformatics educators who break down complex concepts into manageable, actionable learning steps.Section 1: Introduction to Single-Cell RNA-SeqWhat is scRNA-seq and how it differs from bulk RNA-seqReal-world applications in medicine and biologyA clear overview of the entire analysis pipelineSection 2: Getting Started with R and RStudioIntroduction to R programming for biologistsInstalling RStudio and understanding data structuresImporting data, managing packages, and creating visualizations using ggplot2 and SeuratSection 3: Single-Cell RNA-Seq Analysis in RPreprocessing scRNA-seq data with SeuratQuality control, normalization, scaling, and PCAClustering, UMAP visualization, marker gene identification, and cell type annotationSection 4: Single-Cell RNA-Seq Analysis in PythonUsing Scanpy and scVI-toolsReplicating full pipelines in PythonAdvanced annotation using models like scANVISection 5: GUI and Cloud-Based PipelinesRunning scRNA-seq analysis without codingUsing platforms like Galaxy, CodeOcean, or CellxgeneDownloading and analyzing GEO datasets visuallyWho This Course Is For:Biology or medical students transitioning into bioinformaticsBioinformatics beginners and researchersData scientists exploring single-cell applicationsWet lab scientists wanting to analyze their own scRNA-seq dataAnyone interested in high-resolution transcriptomicsWhat You Need:No prior coding experience required - this course is beginner-friendlyAll tools used are open-source and freely availableBasic familiarity with biology or interest in genomics is helpfulJoin us in this immersive learning experience and gain the confidence to run full single-cell RNA-seq analysis pipelines, interpret your results, and contribute meaningfully to cutting-edge research. Whether you aim to build a research career or enhance your skill set as a life science professional, this course is your gateway to mastering single-cell bioinformatics using R, Python, GUI tools, and cloud platforms.
