scRNA seq Data Analysis
Unlock cellular heterogeneity with advanced single-cell RNA transcriptomics pipelines. Master high-throughput genomic data analysis using machine learning and bioinformatics workflows.
Course Description
In modern genomic research, understanding tissue complexity at single-cell resolution is pivotal for groundbreaking scientific discoveries. This intensive online crash course, presented by Dr. Omics Edu and highlighted in scRNASeq.jpg, is meticulously designed to master scRNA-seq Data Analysis. Participants will explore the entire lifecycle of transcriptomic data, transitioning from raw sequencing reads to deep biological insights. Throughout this program, you will navigate quality control filtering, normalization, and dimensionality reduction techniques essential for handling high-throughput datasets. The curriculum integrates traditional statistical approaches with cutting-edge machine learning algorithms to accurately cluster distinct cell populations and track cellular trajectories. By working hands-on with realistic bioinformatics pipelines, you will learn to uncover rare cell types and decode complex disease mechanisms. Ultimately, this course bridges the gap between raw data and actionable biological discovery, empowering life science researchers to lead the future of precision medicine.
What You'll Learn
The fundamental mechanics of single-cell isolation, library preparation, and high-throughput sequencing technologies.
How to construct and implement an automated quality control pipeline for filtering low-quality cell data.
Methods for executing dimensionality reduction techniques, including PCA, t-SNE, and UMAP.
Strategies to apply unsupervised machine learning algorithms for robust cell clustering and cell-type annotation.
Techniques for identifying differentially expressed genes (DEGs) and mapping complex cellular trajectories.
Curriculum
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Module 1: Foundations of Single-Cell Transcriptomics and Raw FASTQ Preprocessing.
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Module 2: Alignment, Quantification, and Building the Gene Expression Matrix.
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Module 3: Quality Control, Normalization, and Regression of Technical Confounders.
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Module 4: Feature Selection, Dimensionality Reduction, and Machine Learning-Driven Cell Clustering.
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Module 5: Differential Expression Analysis, Biomarker Identification, and Pathway Enrichment.
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