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Learn Single-Cell RNA-Seq Data Analysis Using R & Python

Name: Learn Single-Cell RNA-Seq Data Analysis Using R & Python
Rating: 4.1 (69 reviews)

Master Single-Cell RNA-seq Analysis from Scratch Using R, Python, and Cloud Tools — Master QC, Clustering and Annotation

Created byRafiq Ur Rehman

Last updated 2/2026

English

English [Auto],

What you'll learn

Understand the principles of single-cell RNA sequencing (scRNA-seq) and how it differs from bulk RNA-seq.
Set up and use R and RStudio for bioinformatics workflows, including data import/export, visualization, and package management.
Perform quality control and normalization on single-cell RNA-seq data using Seurat in R.
Execute dimensionality reduction (PCA/UMAP) and clustering to identify distinct cell populations.
Identify marker genes and perform differential gene expression (DEG) analysis between clusters or conditions.
Use SingleR and reference datasets to annotate cell types in single-cell data.
Analyze scRNA-seq data in Python using Scanpy and scVI-tools, from preprocessing to cell type annotation.
Visualize scRNA-seq results with violin plots, PCA, UMAP, and volcano plots for publication-quality graphics.
Run GUI-based and cloud-based single-cell pipelines using platforms like Galaxy or CodeOcean without coding.
Confidently apply complete scRNA-seq pipelines on real-world datasets from GEO (NCBI) using R, Python, or GUI tools.

Course content

5 sections • 17 lectures • 3h 57m total length

What is Single-Cell RNA Sequencing (scRNA-seq)?12:14
Explore how single cell RNA-seq measures gene expression in individual cells, contrasting with bulk RNA-seq to reveal cell-type diversity, trajectories, and rare populations.
From Bulk RNA-Seq to Single-Cell Transcriptomics12:31
Discover how single-cell RNA-seq reveals cellular heterogeneity and overcomes bulk RNA-seq limitations, detailing differences, cell-type resolution, and when to choose single-cell analysis. See implications in tumor and brain tissue.
Key Applications of scRNA-seq in Research and Medicine18:43
Highlight the wide applications of single cell RNA-seq in research and medicine, from developmental biology to cancer and immunology, enabling disease mechanism insight and personalized therapy.
Overview of the Single-Cell RNA-Seq Analysis Pipeline12:30
Explore the single-cell rna-seq analysis pipeline, from data input and quality control to normalization, feature selection, clustering, and cell type annotation using Seurat and Scanpy.
Getting Started with Single Cell Technology

Introduction to R Programming for Bioinformatics7:20
Explore how to set up R and RStudio for bioinformatics, install Bioconductor packages, load libraries, and perform basic data analysis and visualization of gene expression using R and ggplot.
Setting Up R and RStudio for scRNA-seq Analysis12:05
Set up R and RStudio across Windows, Mac, and Ubuntu. Learn to create a bioinformatics project and enable optional Git version control for single-cell RNA-seq analysis in R and Python.
Understanding Data Structures in R (Vectors, Lists, Data Frames)11:40
Learn core data structures in R, including vectors, matrices, data frames, lists, and factors, and apply them to biological data, gene expression, and genome annotations in RStudio.
Importing and Exporting Data in R18:47
Import and export data in R for single-cell RNA-seq workflows, handling CSV, TSV, and Excel files, and basic fasta/fastq formats, with error handling and downstream workflow readiness.
Installing and Managing R Packages18:10
Master installing and managing R packages in RStudio, exploring CRAN, Bioconductor, and GitHub workflows; use devtools, update packages, and troubleshoot common installation issues for robust bioinformatics analysis.
Data Visualization Techniques in R (ggplot2)10:18
Explore data visualization techniques in R for single-cell RNA-seq data, using ggplot2 to plot gene expression, GC content, and heatmaps, with scatter, box, histogram, and volcano plots.

Installing Required Libraries for scRNA-seq in R11:02
Install and load R packages for scRNA-seq in RStudio and set up a reproducible project directory. Utilize dplyr, ggplot2, patchwork, SingleR, and cellDex for data handling, visualization, and annotation.
Downloading and Loading Single-Cell RNA-seq Datasets14:17
Download and load ten x genomics single-cell rna-seq datasets into a Seurat object in R, understanding the output files (matrix.mtx, features.tsv, barcodes.tsv) and related geo resources.
Quality Control and Data Normalization11:59
Perform quality control and normalization of single-cell RNA-seq data in Seurat, filtering cells by mitochondrial content and gene counts, and selecting highly variable genes for dimensionality reduction and clustering.
Dimensionality Reduction, Clustering, and Visualization11:30
Perform dimensionality reduction, clustering, and visualization of single-cell RNA-seq data using PCA and UMAP, followed by cell type annotation and differential expression analysis.
Identifying Marker Genes and Differential Expression11:32
Identify cluster markers and perform differential expression analysis in single-cell RNA-seq data using R and Python; compare clusters, apply log2 fold change thresholds, and interpret marker genes.
Single-Cell RNA-Seq Analysis in R
Characterizing Tumor Heterogeneity Using Single-Cell RNA-seq Data

Requirements

No prior experience with single-cell RNA-seq is required – this course is designed for complete beginners and walks you through every concept from scratch.
Basic understanding of biology or genomics is helpful but not mandatory. We explain all necessary biological terms in a simple and practical way.
No prior coding experience needed – we will guide you step-by-step in using both R and Python for bioinformatics.
A computer with internet access (Windows, macOS, or Linux) is required to install R, RStudio, Python, and other free tools used in the course.
Willingness to learn and explore real-world biological data using modern bioinformatics tools.
(Optional) If you’re familiar with basic command-line usage or RNA-seq, it will make your journey faster—but again, not required!

Description

Welcome to our comprehensive course, “Learn Single-Cell RNA-Seq Data Analysis Using R, Python, GUI Tools, and Cloud Platforms,” designed to empower you with the essential skills and knowledge to explore one of the most advanced and high-resolution technologies in genomics: Single-Cell RNA Sequencing (scRNA-seq).

In the era of precision biology, where understanding individual cellular behavior is critical to unlocking insights in cancer biology, immunology, neuroscience, and regenerative medicine, scRNA-seq has emerged as a transformative tool. This course serves as your complete entry point into the field of single-cell transcriptomics, guiding you from foundational theory to hands-on data analysis using the most powerful open-source tools available.

Why Single-Cell RNA-Seq?

With the limitations of traditional bulk RNA sequencing—where gene expression is averaged across thousands or millions of cells—scRNA-seq allows you to analyze gene expression at single-cell resolution, revealing the hidden heterogeneity in tissues, uncovering rare cell types, and providing new dimensions to disease understanding and treatment strategies.

Bioinformatics for the Single Cell Era

At the heart of this course is the interdisciplinary power of bioinformatics—a fusion of biology, computer science, and data science. You’ll master the computational approaches and statistical frameworks that drive modern single-cell data analysis, learning how to interpret, visualize, and extract meaningful biological insights from complex datasets.

Comprehensive Learning Journey: From the basics of single-cell technology to full-scale analysis workflows, this course walks you through every step of scRNA-seq data analysis with clarity and practical insight.

Multi-Tool Mastery: You'll gain hands-on experience with both R and Python-based analysis pipelines, using powerful libraries like Seurat and Scanpy, while also exploring graphical interfaces and cloud-based platforms like Galaxy for non-coders.

Real-World Data: Work with real scRNA-seq datasets from public repositories like NCBI GEO, giving you confidence in applying your skills to actual biological data.

Expert Instruction: Learn from experienced bioinformatics educators who break down complex concepts into manageable, actionable learning steps.
Section 1: Introduction to Single-Cell RNA-Seq

What is scRNA-seq and how it differs from bulk RNA-seq
Real-world applications in medicine and biology
A clear overview of the entire analysis pipeline

Section 2: Getting Started with R and RStudio

Introduction to R programming for biologists
Installing RStudio and understanding data structures
Importing data, managing packages, and creating visualizations using ggplot2 and Seurat

Section 3: Single-Cell RNA-Seq Analysis in R

Preprocessing scRNA-seq data with Seurat
Quality control, normalization, scaling, and PCA
Clustering, UMAP visualization, marker gene identification, and cell type annotation

Section 4: Single-Cell RNA-Seq Analysis in Python

Using Scanpy and scVI-tools
Replicating full pipelines in Python
Advanced annotation using models like scANVI

Section 5: GUI and Cloud-Based Pipelines

Running scRNA-seq analysis without coding
Using platforms like Galaxy, CodeOcean, or Cellxgene
Downloading and analyzing GEO datasets visually

Who This Course Is For:

Biology or medical students transitioning into bioinformatics
Bioinformatics beginners and researchers
Data scientists exploring single-cell applications
Wet lab scientists wanting to analyze their own scRNA-seq data
Anyone interested in high-resolution transcriptomics

What You Need:

No prior coding experience required — this course is beginner-friendly
All tools used are open-source and freely available
Basic familiarity with biology or interest in genomics is helpful

Join us in this immersive learning experience and gain the confidence to run full single-cell RNA-seq analysis pipelines, interpret your results, and contribute meaningfully to cutting-edge research. Whether you aim to build a research career or enhance your skill set as a life science professional, this course is your gateway to mastering single-cell bioinformatics using R, Python, GUI tools, and cloud platforms.

Who this course is for:

A life science, biotechnology, or bioinformatics student looking to build strong, industry-relevant data analysis skills.
A researcher working with transcriptomic data who wants to explore cellular heterogeneity and cell-type-specific gene expression.
A beginner in R or Python programming who wants to apply their coding skills to cutting-edge biological research.
A data scientist or programmer interested in transitioning into the field of bioinformatics or computational biology.
A PhD or Master’s student needing practical, hands-on guidance to analyze your single-cell datasets for publications.
A working professional looking to add single-cell transcriptomics to your bioinformatics toolkit to stay competitive in the job market.

Learn Single-Cell RNA-Seq Data Analysis Using R & Python

What you'll learn

Explore related topics

Course content

Getting Started with Single Cell Technology (Theory)4 lectures • 56min

Getting Started with R and R-Studio6 lectures • 1hr 18min

Single-Cell RNA-Seq Analysis in R5 lectures • 1hr

Single-Cell RNA-Seq Analysis in Python1 lecture • 14min

GUI and Cloud-Based scRNA-Seq Tools1 lecture • 30min

Requirements

Description

Who this course is for: