Professional Certificate Single-Cell RNA Analysis and Interpretation
-- ViewingNowThe Professional Certificate in Single-Cell RNA Analysis and Interpretation is a comprehensive course designed to equip learners with the essential skills needed to excel in the rapidly growing field of single-cell RNA sequencing (scRNA-seq). This course is crucial for career advancement as scRNA-seq is a powerful technology that enables the analysis of gene expression at the single-cell level, providing unprecedented insights into cellular heterogeneity and function.
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⢠Single-Cell RNA Sequencing (scRNA-seq) Fundamentals: Introduction to scRNA-seq technology, its applications, and advantages over bulk RNA sequencing. Discuss data generation, quality control, and data preprocessing.
⢠Data Normalization and Filtering: Techniques for normalizing and filtering scRNA-seq data, including normalization methods, quality control metrics, and filtering strategies.
⢠Clustering and Dimensionality Reduction: Overview of clustering and dimensionality reduction techniques used in scRNA-seq data analysis, including t-SNE, UMAP, and various clustering algorithms.
⢠Cell Type Identification: Explore methods for identifying cell types within scRNA-seq data, including known marker genes, machine learning-based algorithms, and reference-based approaches.
⢠Differential Expression Analysis: Techniques for identifying differentially expressed genes (DEGs) between cell types or conditions in scRNA-seq data, including statistical testing and multiple testing correction.
⢠Functional Enrichment Analysis: Overview of functional enrichment analysis for scRNA-seq data, including methods for identifying enriched pathways, gene ontologies, and other functional categories.
⢠Trajectory Inference and Pseudotime Analysis: Introduction to trajectory inference and pseudotime analysis, including algorithms for reconstructing cellular differentiation and developmental trajectories from scRNA-seq data.
⢠Integration of Multi-omic Data: Techniques for integrating scRNA-seq data with other omics data types, such as proteomics and epigenomics, to gain a more comprehensive understanding of cellular function and regulation.
⢠Data Visualization and Interpretation: Strategies for visualizing and interpreting scRNA-seq data, including interactive visualization tools, data summarization techniques, and statistical methods for interpreting large
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