|
所在平台: Udemy |
课程主页: https://www.udemy.com/course/learn-single-cell-rna-seq-data-analysis-using-r-and-python/
课程评论：没有评论
课程名称：使用R和Python学习单细胞RNA-seq数据分析 课程概述：你是否对单细胞RNA测序（scRNA-seq）的神奇世界感到好奇，但不知道从何开始？无论你是生物学学生、计算生物学初学者，还是数据科学爱好者，这门课程——“掌握单细胞RNA-seq数据分析使用R和Python”——都是你分析scRNA-seq数据的完整、易于入门的指南，采用现代生物信息学技术和开源工具。 学习内容：这门全面的课程旨在循序渐进地介绍单细胞RNA测序，从理论背景到全面的实践实施。你将从理解scRNA-seq的工作原理开始，学习其如何从传统的整体RNA-seq演变而来，以及它在癌症生物学、免疫学、神经科学和发育生物学等现代生物医学研究中的应用。 课程分为五个部分： 1. 单细胞RNA-seq简介 - scRNA-seq是什么？ - 它与整体RNA-seq有何不同？ - scRNA-seq在生物医学研究中的实际应用。 - 准确理解从原始数据到生物学见解的分析流程。 2. 生物信息学中的R编程学习 - 针对生物学家的R编程入门。 - 安装和配置R和RStudio。 - 理解向量、矩阵和数据框等数据结构。 - 如何导入/导出数据、安装包和生成美观的可视化图形。 3. R中的scRNA-seq数据分析 - 安装Seurat、SingleR、celldex等重要库。 - 下载和预处理真实的scRNA-seq数据集。 - 执行质量控制（QC）和标准化。 - 使用PCA降维、细胞聚类和UMAP可视化。 - 识别标记基因，进行差异基因表达（DEG）分析，注释细胞类型。 4. Python中的单细胞RNA-seq分析 - 学习使用基于Python的工具，如Scanpy和scVI-tools。 - 如何使用Python复制完整的分析流程。 - 进行高级细胞类型注释。 5. 针对非编码者的GUI和云管道 - 不会编程？没问题！ - 学会使用图形用户界面（GUI）运行scRNA-seq分析。 - 使用云平台如Galaxy或CodeOcean。 - 从GEO访问数据集并在不编写代码的情况下进行处理。 选择这门课程的理由： - 不需要先前的编码经验，课程将逐步指导你。 - 涵盖R和Python的工作流程，提供灵活性。 - 使用NCBI GEO的真实数据集，使学习更实用和相关。 - 对于更喜欢视觉工具的学员，提供GUI选项。 - 由有经验的讲师授课，专注于训练全球的生物信息学学生。 适合人群： - 生物信息学或基因组学的初学者。 - 转向计算生物学的生物学学生。 - 对单细胞转录组学感兴趣的数据科学家。 - 希望分析自己scRNA-seq数据的研究人员。 通过这门课程，你将能自信地进行完整的scRNA-seq分析，理解生物学结果，甚至申请需要单细胞数据技能的角色或研究职位。立即加入，解锁单细胞RNA-seq分析的力量，将你的生物信息学之旅提升到一个新的高度，利用R、Python和基于GUI的工具！
Are you interested in exploring the fascinating world of single-cell RNA sequencing (scRNA-seq) but don't know where to begin? Whether you're a biology student, a bioinformatics beginner, or a data science enthusiast, this course - "Master Single-Cell RNA-seq Data Analysis using R and Python" - is your complete, beginner-friendly guide to analyzing scRNA-seq data using modern bioinformatics techniques and open-source tools.What You'll LearnThis comprehensive course is designed to introduce single-cell RNA sequencing step-by-step, from theoretical background to full-scale practical implementation. You will begin with an understanding of how scRNA-seq works, how it evolved from traditional bulk RNA-seq, and where it is used in modern biological and medical research such as cancer biology, immunology, neuroscience, and developmental biology.The course is divided into 5 sections:Section 1: Introduction to Single-Cell RNA-seqYou'll start with the fundamentals:What is scRNA-seq?How it differs from bulk RNA-seq?Real-world applications of scRNA-seq in biomedical research.A complete overview of the scRNA-seq analysis pipeline, giving you clarity on each step from raw data to biological insights.Section 2: Learning R for BioinformaticsBefore jumping into real analysis, we prepare you with:An introduction to R programming for biologistsInstalling and configuring R and RStudioUnderstanding data structures like vectors, matrices, and data framesHow to import/export data, install packages, and generate beautiful visualizations - critical skills for any bioinformatics project.Section 3: scRNA-seq Data Analysis in RThis is the core of the course, where you:Install key libraries like Seurat, SingleR, celldexDownload and preprocess real scRNA-seq datasetsPerform Quality Control (QC) and normalizationReduce dimensionality using PCA, cluster cells, and run UMAP visualizationsIdentify marker genes, perform differential gene expression (DEG) analysis, and annotate cell typesAll analyses are done hands-on in R, with detailed walkthroughs.Section 4: Single-Cell RNA-seq Analysis in PythonIn this section, you'll learn to use Python-based tools such as:ScanpyscVI-toolsHow to replicate the full analysis pipeline using PythonPerform advanced cell type annotation using scANVIThis enables you to become fluent in both R and Python-based workflows, increasing your versatility as a data analyst or researcher.Section 5: GUI and Cloud Pipelines for Non-CodersNot a programmer? No problem!You'll learn how to:Run scRNA-seq analysis using Graphical User Interfaces (GUI)Use cloud platforms like Galaxy or CodeOceanAccess datasets from GEO and process them without writing codeWhy Take This Course?No prior coding experience needed - we'll guide you step-by-stepCovers both R and Python pipelines, giving you flexibilityUses real datasets from NCBI GEO, making your learning practical and relevantIncludes GUI options for those who prefer visual toolsTaught by an instructor experienced in training bioinformatics students worldwideWho is this Course For?Beginners in bioinformatics or genomicsBiology students transitioning into computational biologyData scientists curious about single-cell transcriptomicsResearchers wanting to analyze their own scRNA-seq dataBy the end of this course, you will be confident in running complete scRNA-seq analyses, interpreting biological results, and even applying for roles or research positions requiring single-cell data skills.Join now to unlock the power of single-cell RNA-seq analysis - and take your bioinformatics journey to the next level using R, Python, and GUI-based tools!
