Masterclass Certificate Single-Cell RNA Sequencing: Advanced Methods
-- ViewingNowThe Masterclass Certificate in Single-Cell RNA Sequencing: Advanced Methods is a comprehensive course designed to equip learners with the essential skills necessary for career advancement in the rapidly evolving field of genomics and biotechnology. This course focuses on the importance of single-cell RNA sequencing (scRNA-seq) technology, which has revolutionized the way researchers study gene expression and cellular heterogeneity in complex biological systems.
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โข Single-Cell RNA Sequencing (scRNA-seq) Fundamentals: An introduction to the basic principles, workflows, and applications of scRNA-seq. This unit will cover key concepts, such as library preparation, sequencing, and data analysis.
โข Advanced Library Preparation Techniques: Delve into the latest library preparation methods, including droplet-based and plate-based approaches, and their impact on data quality and throughput. Discuss the advantages and limitations of each technique.
โข Data Analysis and Computational Tools: Explore popular computational tools and algorithms for scRNA-seq data analysis, such as Seurat, Scanpy, and Monocle. Learn how to perform quality control, normalization, dimensionality reduction, and clustering.
โข Differential Expression and Trajectory Analysis: Understand how to identify differentially expressed genes across cell populations and construct cellular differentiation trajectories using tools like MAST and Slingshot.
โข Integrative Analysis of Multi-Modal Data: Learn how to combine and analyze scRNA-seq data with other omics data, such as ATAC-seq and proteomics, to gain a more comprehensive understanding of cellular states.
โข Machine Learning and Artificial Intelligence in scRNA-seq: Discover how machine learning and artificial intelligence techniques can be applied to scRNA-seq data for improved data analysis, interpretation, and visualization.
โข Experimental Design and Best Practices: Explore best practices for designing scRNA-seq experiments, including sample selection, library preparation, and data analysis strategies to maximize the chances of successful experimental outcomes.
โข Data Interpretation and Visualization: Understand how to effectively communicate scRNA-seq results through data visualization and interpret findings in the context of current scientific knowledge.
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