Global Certificate Single-Cell RNA Analysis for Biopharmaceutical Research
-- ViewingNowThe Global Certificate in Single-Cell RNA Analysis for Biopharmaceutical Research is a comprehensive course designed to equip learners with the essential skills to excel in the rapidly evolving field of single-cell analysis. This course is critical for professionals seeking to enhance their understanding of single-cell RNA sequencing (scRNA-seq) technologies, data analysis, and interpretation, which are increasingly vital in biopharmaceutical research and development.
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⢠Single-Cell RNA Sequencing Technologies: Overview of single-cell RNA sequencing (scRNA-seq) technologies, including droplet-based and plate-based methods, and their applications in biopharmaceutical research.
⢠Sample Preparation and Library Construction: Detailed explanation of sample preparation techniques, quality control measures, and library construction for scRNA-seq experiments.
⢠Data Analysis Pipelines: Introduction to various data analysis pipelines for scRNA-seq data, including quality control, alignment, normalization, feature selection, and clustering.
⢠Differential Expression Analysis: Explanation of differential expression analysis methods for scRNA-seq data, including statistical testing and multiple testing correction.
⢠Cell Type Identification and Annotation: Techniques for identifying and annotating cell types from scRNA-seq data, including reference-based and unsupervised approaches.
⢠Trajectory Analysis: Overview of trajectory analysis methods for scRNA-seq data, including pseudotime reconstruction and RNA velocity analysis.
⢠Data Integration and Multi-Modal Analysis: Techniques for integrating scRNA-seq data with other omics data, such as ATAC-seq and proteomics, to gain a more comprehensive understanding of biological systems.
⢠Functional Enrichment Analysis: Explanation of functional enrichment analysis methods for scRNA-seq data, including gene set enrichment analysis and gene ontology analysis.
⢠Reproducibility and Best Practices: Guidelines for ensuring reproducibility in scRNA-seq experiments and best practices for data analysis and interpretation.
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