Learn Single-Cell RNA Sequencing (scRNA-seq) Analysis

Single-cell RNA sequencing (scRNA-seq) has revolutionized biology by allowing researchers to study gene expression at the resolution of individual cells. It has become an indispensable technology in cancer research, immunology, developmental biology, neuroscience, regenerative medicine, and precision medicine.

Despite its importance, many researchers struggle to learn scRNA-seq analysis because most tutorials require Linux systems, high-performance computing resources, or extensive programming experience.

This course removes those barriers.

Using Google Colab, you will learn to perform an end-to-end single-cell RNA sequencing analysis entirely in the cloud using Python and Scanpy, one of the most widely adopted frameworks for scRNA-seq analysis.

Every concept is explained from the ground up before being demonstrated through practical coding exercises. By the end of the course, you will be able to analyze your own scRNA-seq datasets with confidence.

Throughout the course, we build a complete analysis pipeline that includes:

• Reading single-cell datasets

• Understanding the AnnData data structure

• Quality Control (QC)

• Detecting and removing low-quality cells

• Filtering genes and cells

• Library-size normalization

• Log transformation

• Identification of highly variable genes

• Data scaling

• Principal Component Analysis (PCA)

• Construction of nearest-neighbor graphs

• Leiden clustering

• UMAP visualization

• Marker gene identification

• Cell type annotation

• Biological interpretation of clustering results

Everything is performed inside Google Colab, meaning you do not need to install software or configure a complicated computational environment.

The course emphasizes practical learning. Every lecture demonstrates real analyses that students can reproduce immediately.

Whether you are beginning your journey into single-cell transcriptomics or want to strengthen your bioinformatics skills, this course provides a complete, hands-on roadmap.