Hands-on: Single-Cell RNA-Sequencing Data Analysis Using Python [Complete Training]

Categories: Python, RNA-Seq, Single Cell
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About Course

We present a comprehensive single cell RNA-seq data analysis course designed for beginners and advanced researchers in Life Sciences who are interested in exploring the exciting field of single cell genomics. No prior Python programming or Linux knowledge is required. This course is focused on the analysis of scRNA-seq data using command-line tools and Python packages, including Scanpy and other relevant Python packages (see course content section).
Throughout the course, we will explore the differences between scRNA-seq and bulk RNA-seq, and why scRNA-seq is essential for studying cellular and tumor heterogeneity.
This course is ideal for anyone without any prior knowledge of scRNA analysis, as it provides complete guidance and hands-on training from theground up.
We cover all scRNA technologies with an emphasis on 10x Genomics and Smart-seq2. We teach both theory and hands-on experience of each topic in the entire pipeline. We guide students on how to take raw scRNA datasets, install software and Python packages, perform UMI and cell barcode filtering, align against reference genome, and subsequently perform deduplication and quantification (count matrix).
Our course teaches students how to utilize Python programming (ScanPy package) to perform quality control on scRNA data, remove unwanted dead cells that may hinder analysis results, perform normalization, scaling, data imputation, and dimension reduction, and create visualizations through UMAP, tSNE, and PCA plots. We also provide instruction on cell clustering and identifying cell subpopulations in samples, annotating them (cell annotation), and conducting cell lineage and trajectory analysis of the cells.
Finally, we teach students how to analyze differentially expressed genes in either all of the subpopulations or their chosen subpopulations of cells, identifying marker genes that are only expressed in their chosen subpopulations.
By the end of the course, students will have a comprehensive understanding of single cell RNA-seq data analysis using Python packages, and be equipped to tackle their own single-cell sequencing projects.

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What Will You Learn?

  • In-depth Introduction to scRNA-seq
  • Complete end-to-end scRNA-seq analysis
  • Cellular and Tumor Heterogeneity
  • Bulk vs. Single Cell RNA-seq Analysis
  • Single-Cell RNA-seq Technologies
  • 10x Genomics Complete Pipeline
  • From Raw Datasets to Cell Sub-populations
  • Normalization, Quality Control and Dimension Reduction
  • Cell Clustering and Cell Annotation
  • Differential Gene Expression Analysis
  • Downstream Analysis
  • ScanPy Package Complete Guidelines
  • Python-based scRNA-seq Analysis

Course Content

In-depth Introduction to Single Cell RNA-Sequencing, Pipeline and Single-Cell Technologies

  • Introduction to Single-Cell RNA-seq, Its Pipeline and Analysis
    23:34
  • Gene Expression and Its Significance
    21:42
  • Cellular and Tumor Heterogeneity
    09:10
  • Bulk RNA-sequencing vs. Single-Cell RNA-sequencing
    21:50
  • Single-Cell RNA-seq Technologies (10x Genomics, Smart-Seq, Drop-seq and more)
    23:09
  • Cell Isolation and Cell Lysis Protocols for Single-Cell Genomics
    09:53
  • In-depth Introduction to Single-Cell RNA-sequencing Analysis Pipeline
    24:45
  • Droplet Technologies: 10x Genomics
    14:10
  • Full-Length Transcript Technologies: Smart-Seq2
    17:55

Hands-on Single-Cell RNA-seq Data Analysis (From Raw Reads to Cell Subpopulation Identification and DEGs Identification)

Additional Supplementary Lectures

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