Global Certificate Bioinformatic Analysis of Single-Cell RNA
-- ViewingNowThe Global Certificate in Bioinformatic Analysis of Single-Cell RNA is a comprehensive course designed to equip learners with essential skills in bioinformatic data analysis. This course is critical in the current industry landscape, where there is a high demand for professionals who can analyze and interpret single-cell RNA sequencing data.
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โข Single-Cell RNA Sequencing (scRNA-seq): Introduction to the technology, benefits, and challenges of scRNA-seq, including data generation, processing, and quality control.
โข Data Preprocessing: Data cleaning, normalization, and transformation techniques for scRNA-seq data, including gene filtering and cell filtering methods.
โข Dimensionality Reduction: Techniques for reducing the dimensionality of scRNA-seq data, including t-SNE, UMAP, and PCA, and their applications.
โข Clustering and Cell Type Identification: Overview of clustering algorithms and methods for cell type identification, including hierarchical clustering, k-means, and Louvain method.
โข Differential Expression Analysis: Statistical methods for identifying differentially expressed genes in scRNA-seq data, including DESeq2, edgeR, and limma.
โข Pseudotime and Trajectory Analysis: Techniques for reconstructing the developmental trajectory of cells, including Monocle, Slingshot, and PAGA.
โข Functional Enrichment Analysis: Methods for interpreting differentially expressed genes in the context of biological pathways and gene ontologies, including DAVID, GO, and KEGG.
โข Integrative Analysis: Approaches for integrating scRNA-seq data with other data types, including bulk RNA-seq, ATAC-seq, and proteomics.
โข Data Visualization: Techniques for visualizing scRNA-seq data, including heatmaps, violin plots, and dot plots, using libraries such as ggplot2 and seaborn.
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