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

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.

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)

Hands-on: Obtaining Raw Datasets for scRNA-seq Analysis

21:49
Hands-on: Installation of Software and Python Packages (Scanpy)

11:50
Introduction to Anaconda and Jupyter-Notebook for Bioinformatics

22:46
How to Get Started With Jupyter-Notebook for Bioinformatics Analysis

17:14
How to Write & Run Your First Script in Jupyter-Notebook

09:41
Technical Issues With Single-Cell RNA-seq Data

15:32
UMIs, Cell Barcodes, Read Alignment and Quantification

16:21
Hands-on: Read Alignment, Deduplication and Quantification (Generating a Count Matrix) Using Python

00:00
Quality Control of Single-Cell Data: Filtering Dead Cells and Unwanted Data

19:06
Batch Effect Correction

05:19
Hands-on: Filtering Dead Cell and Low Quality Data

22:39
Normalization, Highly Variable Features and Scaling of scRNA-seq Data

12:57
Data Imputation and Fixing Transcript Dropouts

08:16
Hands-on: Normalization, Identification of Highly Variable Features and Scaling

05:50
High Dimension Data Visualization and Dimension Reduction (PCA, Heatmap, JackStraw, Elbow Plot)

18:16
Hands-on: Dimension Reduction and Visualization (UMAP, tSNE, PCA, JackStraw and Elbow Plot)

10:03
Cell Subpopulation Identification and Cell Annotation

15:06
Hands-on: Clustering of Cell Subpopulations

00:00
Hands-on: Cell Annotation of Subpopulations

00:00
Differential Gene Expression and Marker Genes Identification

11:32
Hands-on: Differential Gene Expression Analysis Between Cell Subpopulations

00:00
Hands-on: Identification of Marker Genes and Expression Patterns

00:00
Cell Lineage, Trajectory and Pseudotime Analysis

11:22
Hands-on: Psuedotime Analysis

00:00
Theory: Downstream Functional Enrichment Analysis

18:39
Hands-on: Gene Ontology Analysis Using GeneCodis4

14:21
Hands-on: Pathways Analysis Using KEGG, PANTHER, Reactome

07:59
Conclusions and Remarks

05:46

Additional Supplementary Lectures

Introduction to Linux for Bioinformatics

22:26
PWD – Print Working Directory

01:26
MKDIR – Making Directories

08:13
CD – Changing Directories

05:03
MV – Moving Files, Directories and Data

05:11
RM – Deleting Files and Directories

01:24
Which & Whereis – Find Programs You Installed

03:43
Find – Finding User Created Files

03:39
LS – Listing Files and Directories on Linux

06:46
Cat – Visualization and Inspection of Text Data

03:56
Head – Reading Specified Number of Lines from Top

03:50
Tail- Reading Specified Number of Lines from Bottom

02:23
Touch – Modifying File Statistics and Creating Files

07:04
Stat – Statistics of File & Directories

02:43
Wget – Retrieval of Genome Assemblies

06:48
Curl – Retrieval of Bioinformatics Files

02:25
Vim – Create and Edit Text Files

05:59
Diff – Find Sequence Differences in Files

02:35
How Mapping is Performed for Next-Generation Sequencing Datasets

48:22
How Differentially Expressed Genes are Identified in Bulk RNA-seq Datas

18:28
How Quality Control is Performed in Bulk RNA-seq Data

19:37

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About Course

What Will You Learn?

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

Gene Expression and Its Significance

Cellular and Tumor Heterogeneity

Bulk RNA-sequencing vs. Single-Cell RNA-sequencing

Single-Cell RNA-seq Technologies (10x Genomics, Smart-Seq, Drop-seq and more)

Cell Isolation and Cell Lysis Protocols for Single-Cell Genomics

In-depth Introduction to Single-Cell RNA-sequencing Analysis Pipeline

Droplet Technologies: 10x Genomics

Full-Length Transcript Technologies: Smart-Seq2

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

Hands-on: Obtaining Raw Datasets for scRNA-seq Analysis

Hands-on: Installation of Software and Python Packages (Scanpy)

Introduction to Anaconda and Jupyter-Notebook for Bioinformatics

How to Get Started With Jupyter-Notebook for Bioinformatics Analysis

How to Write & Run Your First Script in Jupyter-Notebook

Technical Issues With Single-Cell RNA-seq Data

UMIs, Cell Barcodes, Read Alignment and Quantification

Hands-on: Read Alignment, Deduplication and Quantification (Generating a Count Matrix) Using Python

Quality Control of Single-Cell Data: Filtering Dead Cells and Unwanted Data

Batch Effect Correction

Hands-on: Filtering Dead Cell and Low Quality Data

Normalization, Highly Variable Features and Scaling of scRNA-seq Data

Data Imputation and Fixing Transcript Dropouts

Hands-on: Normalization, Identification of Highly Variable Features and Scaling

High Dimension Data Visualization and Dimension Reduction (PCA, Heatmap, JackStraw, Elbow Plot)

Hands-on: Dimension Reduction and Visualization (UMAP, tSNE, PCA, JackStraw and Elbow Plot)

Cell Subpopulation Identification and Cell Annotation

Hands-on: Clustering of Cell Subpopulations

Hands-on: Cell Annotation of Subpopulations

Differential Gene Expression and Marker Genes Identification

Hands-on: Differential Gene Expression Analysis Between Cell Subpopulations

Hands-on: Identification of Marker Genes and Expression Patterns

Cell Lineage, Trajectory and Pseudotime Analysis

Hands-on: Psuedotime Analysis

Theory: Downstream Functional Enrichment Analysis

Hands-on: Gene Ontology Analysis Using GeneCodis4

Hands-on: Pathways Analysis Using KEGG, PANTHER, Reactome

Conclusions and Remarks

Additional Supplementary Lectures

Introduction to Linux for Bioinformatics

PWD – Print Working Directory

MKDIR – Making Directories

CD – Changing Directories

MV – Moving Files, Directories and Data

RM – Deleting Files and Directories

Which & Whereis – Find Programs You Installed

Find – Finding User Created Files

LS – Listing Files and Directories on Linux

Cat – Visualization and Inspection of Text Data

Head – Reading Specified Number of Lines from Top

Tail- Reading Specified Number of Lines from Bottom

Touch – Modifying File Statistics and Creating Files

Stat – Statistics of File & Directories

Wget – Retrieval of Genome Assemblies

Curl – Retrieval of Bioinformatics Files

Vim – Create and Edit Text Files

Diff – Find Sequence Differences in Files

How Mapping is Performed for Next-Generation Sequencing Datasets

How Differentially Expressed Genes are Identified in Bulk RNA-seq Datas

How Quality Control is Performed in Bulk RNA-seq Data

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