Learn Single-Cell RNA-seq Data Analysis using R and Python

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

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.

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
Are you interested in exploring the fascinating world ofsingle-cell RNA sequencing (scRNA-seq)but don’t know where to begin? Whether you're abiology student, abioinformatics beginner, or adata science enthusiast, this course —"Master Single-Cell RNA-seq Data Analysis using R and Python"— is your complete, beginner-friendly guide to analyzing scRNA-seq data using modern bioinformatics techniques and open-source tools.

What You’ll Learn

This comprehensive course is designed tointroduce single-cell RNA sequencing step-by-step, from theoretical background to full-scale practical implementation. You will begin with an understanding of how scRNA-seq works, how it evolved from traditional bulk RNA-seq, and where it is used in modern biological and medical research such as cancer biology, immunology, neuroscience, and developmental biology.

The course is divided into 5 sections:

Section 1:Introduction to Single-Cell RNA-seq

You'll start with the fundamentals:

What is scRNA-seq?

How it differs from bulk RNA-seq?

Real-worldapplications of scRNA-seqin biomedical research.

A completeoverview of the scRNA-seq analysis pipeline, giving you clarity on each step from raw data to biological insights.

Section 2:Learning R for Bioinformatics

Before jumping into real analysis, we prepare you with:

An introduction toR programming for biologists

Installing and configuring R and RStudio

Understandingdata structureslike vectors, matrices, and data frames

How toimport/export data, install packages, and generate beautifulvisualizations— critical skills for any bioinformatics project.

Section 3:scRNA-seq Data Analysis in R

This is the core of the course, where you:

Install key libraries likeSeurat,SingleR,celldex

Download and preprocess real scRNA-seq datasets

PerformQuality Control (QC)andnormalization

Reduce dimensionality usingPCA, cluster cells, and runUMAPvisualizations

Identifymarker genes, performdifferential gene expression (DEG)analysis, and annotate cell typesAll analyses are donehands-on in R, with detailed walkthroughs.

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

In this section, you'll learn to use Python-based tools such as:

Scanpy

scVI-tools

How to replicate the full analysis pipeline using Python

Perform advancedcell type annotation using scANVIThis enables you to become fluent in both R and Python-based workflows, increasing your versatility as a data analyst or researcher.

Section 5:GUI and Cloud Pipelines for Non-Coders

Not a programmer? No problem!You'll learn how to:

Run scRNA-seq analysis usingGraphical User Interfaces (GUI)

Usecloud platformslike Galaxy or CodeOcean

Access datasets from GEO and process them without writing code

Why Take This Course?

No prior coding experience needed— we'll guide you step-by-step

Coversboth R and Pythonpipelines, giving you flexibility

Usesreal datasets from NCBI GEO, making your learning practical and relevant

Includes GUI options for those who prefer visual tools

Taught by an instructor experienced in training bioinformatics students worldwide

Who is this Course For?

Beginners inbioinformaticsorgenomics

Biology students transitioning intocomputational biology

Data scientists curious aboutsingle-cell transcriptomics

Researchers wanting toanalyze their own scRNA-seq data

By the end of this course, you will beconfident in running complete scRNA-seq analyses, interpreting biological results, and even applying for roles or research positions requiring single-cell data skills.

Join nowtounlock the power of single-cell RNA-seq analysis— and take your bioinformatics journey to the next level using R, Python, and GUI-based tools!

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.
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