Executive Development Programme Single-Cell RNA Analysis for BioTech
-- ViewingNowThe Executive Development Programme in Single-Cell RNA Analysis for BioTech is a certificate course designed to provide learners with comprehensive knowledge and skills in the rapidly evolving field of single-cell RNA sequencing. This program emphasizes the importance of understanding the principles, techniques, and applications of single-cell RNA analysis to drive innovation and tackle complex challenges in the biotechnology industry.
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⢠Single-Cell RNA Sequencing (scRNA-seq) Fundamentals: Introduction to scRNA-seq technology, its applications, and benefits in biotechnology and pharmaceutical research. Overview of different scRNA-seq platforms and protocols.
⢠Sample Preparation and Library Construction: Detailed discussion on sample preparation techniques, quality control, and library construction for scRNA-seq. Explanation of unique molecular identifiers (UMIs) and barcoding strategies.
⢠Data Analysis Workflows: Overview of computational tools and workflows for scRNA-seq data analysis. Emphasis on quality control, data normalization, and dimensionality reduction techniques.
⢠Clustering and Cell Type Identification: Exploration of unsupervised and supervised clustering methods for scRNA-seq data. Techniques for identifying and annotating cell types, including marker gene analysis and gene expression patterns.
⢠Differential Expression Analysis: Deep dive into statistical methods for detecting differentially expressed genes across cell populations. Explanation of false discovery rate (FDR) and multiple testing correction strategies.
⢠Trajectory Inference and Pseudotime Analysis: Introduction to single-cell trajectory inference and pseudotime analysis methods. Understanding the role of these techniques in understanding cell differentiation and development.
⢠Integrative Analysis of Multi-omics Data: Overview of integrating scRNA-seq data with other omics data, such as ATAC-seq, ChIP-seq, and proteomics data. Explanation of benefits and challenges of multi-omics data integration.
⢠Data Visualization and Interpretation: Best practices for data visualization and interpretation in scRNA-seq data analysis. Discussion on visualization tools and techniques for effectively communicating results.
⢠Ethical and Regulatory Considerations: Overview of ethical and regulatory considerations in scRNA-seq
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