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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โข 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.
โข 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.
โข 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.
โข 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.
โข 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.
โข 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.
โข 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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