BIO HELP LEARNING COURSE

Single-Cell RNA-Seq Data Analysis Training

Learn single-cell RNA-seq analysis from raw counts to annotated cell types, using Cell Ranger, Seurat, Scanpy and CellChat on real datasets.

◉ Beginner to Advanced◷ 15 Hours / Live▣ Yes — Certificate of Completion

Course Information

Everything you need to know before enrolling.

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Live online classes
Group & college batches
Certificate on completion

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▤ Overview ☷ Curriculum ♙ Instructor ☆ Reviews ▣ FAQs

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About This Course

Single-cell RNA sequencing reveals what each individual cell is doing, uncovering cell types and states that bulk experiments average away. This 15-day training shows you how to turn single-cell data into clear biological answers.

Day by day you work through the full workflow: Cell Ranger outputs, quality control, normalization, PCA, clustering, UMAP, marker genes, cell-type annotation, differential expression, pathway analysis and cell–cell communication, ending with a complete mini project and report.

Who this course is for

MSc and PhD students working with transcriptomics data
Researchers planning or analysing single-cell experiments
Learners who already know basic R or Python

◎

What You’ll Learn

✓Understand the principles and applications of single-cell RNA sequencing

✓Perform quality control and preprocessing of scRNA-Seq data

✓Construct gene–cell expression matrices

✓Perform normalization and feature selection

✓Perform PCA and dimensionality reduction

✓Generate UMAP/t-SNE visualizations

✓Identify cell clusters and marker genes

✓Annotate cell populations using marker genes

✓Perform differential gene expression analysis

✓Interpret biological pathways and cellular states

⌘

Tools & Technologies

10x GenomicsCell RangerSeuratScanpyRPythonCellChat

Course Curriculum

What you will learn, day by day

15 days 15 hours of training 60 topics Certificate on completion

- Why study gene expression one cell at a time
- Bulk RNA-seq vs. single-cell RNA-seq
- Major scRNA-seq technologies and platforms
- Research and clinical applications
- Sample preparation and cell capture
- Cell barcodes, UMIs and library structure
- Choosing cell numbers and sequencing depth
- The gene-by-cell count matrix
- How 10x Chromium works
- Running cellranger count
- Understanding the web summary report
- Output files: filtered matrix, barcodes and features
- Loading data into Seurat and Scanpy
- QC metrics: genes per cell, UMIs and mitochondrial %
- Visualizing QC distributions
- Identifying low-quality cells and doublets
- Setting filtering thresholds
- Log-normalization and SCTransform
- Selecting highly variable genes
- Scaling and regressing out unwanted variation
- Why reduce dimensions
- Running PCA on single-cell data
- Choosing the number of PCs with elbow plots
- Interpreting PC loadings
- Building the nearest-neighbour graph
- Louvain and Leiden clustering
- Choosing the clustering resolution
- Checking cluster quality
- How UMAP and t-SNE work
- Running and tuning embeddings
- Visualizing clusters and metadata
- Common pitfalls when interpreting embeddings
- Finding cluster markers
- Statistical tests used for marker detection
- Dot plots, violin plots and feature plots
- Ranking markers for annotation
- Manual annotation with known markers
- Reference-based and automated annotation
- Using public marker databases
- Labelling and validating cell types
- Comparing conditions within cell types
- Pseudobulk vs. cell-level testing
- Volcano plots and result tables
- Interpreting fold changes
- Over-representation analysis
- GO and KEGG enrichment
- Gene set enrichment approaches
- Visualizing enriched pathways
- Ligand–receptor interaction concepts
- Setting up a CellChat analysis
- Inferring signalling networks
- Visualizing communication patterns
- Heatmaps and multi-panel figures
- Integrating multiple samples
- Telling the biological story
- Preparing publication-ready plots
- Complete analysis on a real dataset
- From raw counts to annotated cell types
- Writing the analysis report
- Presenting results and feedback

♙

Your Instructor

Bio Help Learning Educator

Bioinformatics Educator & Mentor

Learn from experienced educators and practitioners through practical, career-focused bioinformatics training.

Frequently Asked Questions

15 answers about this course

What is Single-Cell RNA-Seq?

Single-cell RNA-Seq (scRNA-Seq) is a sequencing approach used to study gene expression at the level of individual cells.

Who is this course suitable for?

This course is suitable for biotechnology, life science, bioinformatics, computational biology, and related students, researchers, and professionals.

Do I need prior scRNA-Seq experience?

No. The course starts with the fundamentals and progressively moves toward practical analysis.

What will I learn in this training?

You will learn the complete scRNA-Seq analysis workflow, including QC, normalization, clustering, dimensionality reduction, cell-type annotation, differential expression, pathway analysis, and biological interpretation.

Which datasets will be used?

The training includes practical analysis using real-world single-cell RNA-Seq datasets.

Which tools will I learn?

You will work with tools and frameworks including 10x Genomics, Cell Ranger, Seurat, Scanpy, R, Python, and CellChat.

Will I learn Seurat?

Yes. Seurat will be used for major steps of single-cell data preprocessing, dimensionality reduction, clustering, marker identification, and downstream analysis.

Will I learn Python-based analysis?

Yes. The course introduces Python-based single-cell analysis workflows alongside R-based analysis.

Will I learn cell-type annotation?

Yes. You will learn how to identify and annotate cell populations using marker genes.

Will I learn cell–cell communication analysis?

Yes. The course includes an introduction to cell–cell communication analysis using CellChat.

Is this a hands-on training?

Yes. The training is designed around practical analysis and includes a complete scRNA-Seq mini project.

How long is the training?

The training consists of 15 sessions of 60 minutes each, totaling 15 hours.

Will I receive a certificate?

Yes. Participants who complete the training receive a Certificate of Completion.

Will I get a project at the end?

Yes. Day 15 is dedicated to a complete scRNA-Seq mini project and report generation.

What will I be able to do after completing the course?

You will be able to perform and interpret key steps of a single-cell RNA-Seq analysis workflow from quality control through biological interpretation.

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