Global Certificate Single-Cell RNA Sequencing Applications
-- ViewingNowThe Global Certificate in Single-Cell RNA Sequencing Applications is a comprehensive course designed to equip learners with the essential skills needed to excel in the rapidly evolving field of genomics. This course emphasizes the importance of Single-Cell RNA Sequencing (scRNA-seq) technology, its applications, and how it drives impactful discoveries in biology and medicine.
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⢠Single-Cell RNA Sequencing Fundamentals: Introduce the basics of single-cell RNA sequencing (scRNA-seq), covering its history, principles, and benefits over bulk RNA sequencing. Discuss major scRNA-seq techniques, such as Smart-seq, Drop-seq, and 10x Genomics Chromium.
⢠Sample Preparation for scRNA-seq: Explain how to prepare samples for single-cell RNA sequencing, including tissue dissociation, cell isolation, and quality control. Discuss the importance of library preparation and the various methods available.
⢠Data Analysis Workflow: Outline the standard data analysis workflow for scRNA-seq data, including quality control, alignment, quantification, normalization, and downstream analysis. Explain how to identify differentially expressed genes, cell types, and gene networks.
⢠Data Visualization Techniques: Teach data visualization techniques specific to scRNA-seq data, such as t-SNE, UMAP, and bar plots. Explain how to create and interpret these visualizations to gain insights into gene expression patterns and cellular heterogeneity.
⢠Secondary Analysis: Cluster Identification and Cell Type Annotation: Discuss how to identify and interpret cell clusters in scRNA-seq data, including the use of reference datasets and machine learning algorithms. Explain how to assign cell type identities to clusters and interpret the results.
⢠Functional Enrichment Analysis: Cover functional enrichment analysis for scRNA-seq data, including gene set enrichment analysis (GSEA) and gene ontology (GO) analysis. Explain how to interpret the results and generate hypotheses based on the enriched functions.
⢠Integrative Analysis of Multi-omic Data: Teach how to integrate scRNA-seq data with other omics data types, such as ATAC-seq and proteomics data. Explain the benefits of integrative analysis and how to interpret the results.
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