School of Bioinformatics · Genomics & transcriptomics
Single-Cell RNA-seq Analysis
Single-cell sequencing measures gene expression in thousands of individual cells. This course covers the complete analysis of droplet-based data, with particular attention to the choices that change biological conclusions.
Course overview
Single-Cell RNA-seq Analysis covers droplet-based single-cell data from the count matrix onwards: quality control, normalisation, dimensionality reduction, clustering, marker detection, annotation and integration across samples.
Single-cell analysis involves many choices without a single correct setting, such as filtering thresholds, the number of variable genes or the clustering resolution. The course treats them as decisions to justify and test, and shows how each one changes the biological conclusion. Scanpy and Seurat are taught side by side for the core steps.
Learning outcomes
By the end of the course you will be able to:
- Run QC and filter cells and genes with defensible thresholds
- Normalise, select variable genes and reduce dimensions
- Cluster cells and read UMAP and t-SNE plots correctly
- Annotate cell types with markers and reference datasets
- Integrate samples and correct batch effects without removing biology
Skills you will practise
Eligibility and prerequisites
Who can apply
- Students and graduates of B.Sc., M.Sc., B.Tech., M.Tech., B.Pharm., M.Pharm., MBBS, BDS, B.V.Sc. or a PhD programme in the life sciences, biotechnology, bioinformatics, pharmacy, medicine, agriculture, chemistry, statistics or computer science
- Researchers, faculty and industry professionals moving into data-driven biology
- Final-year students may apply before their results are declared
Prerequisites
- RNA-seq Data Analysis or equivalent
- Working knowledge of Python or R
Best suited to
- Researchers with single-cell projects
- Students who have completed bulk RNA-seq analysis
Module-wise syllabus
6 modules. Each pairs instructor-led sessions with a hands-on lab on public data.
Topics
- Droplet and plate-based methods
- Cell Ranger outputs
- Ambient RNA and doublets
Topics
- Mitochondrial fraction and library size
- Doublet detection
- Filtering decisions and their effects
Topics
- Log-normalisation and SCTransform
- Highly variable genes
- PCA, UMAP and t-SNE
Topics
- Graph-based Leiden and Louvain clustering
- Resolution and stability
- Differential markers
Topics
- Reference-based annotation
- Harmony and scVI integration
- Trajectory analysis basics
Topics
- Full workflow on public data
- Annotated atlas
- Reproducible notebook
Practical sessions and projects
Capstone project
Build an annotated cell atlas
Process a public PBMC dataset from counts to annotated cell types and justify every filtering, clustering and annotation decision in a reproducible notebook.
Assessed on the correctness of the analysis, the quality of the code and the reasoning in the write-up. A pass is required for the certificate.
- Single-cell technologies
Read Cell Ranger output for the PBMC 3k dataset, inspect the barcode-rank plot and estimate the ambient RNA contribution.
- Quality control
Set and defend thresholds for library size, detected genes and mitochondrial fraction, and remove predicted doublets.
- Normalisation and reduction
Normalise counts, select highly variable genes and compare PCA, UMAP and t-SNE views of the same data.
- Clustering and markers
Cluster with Leiden at several resolutions, measure cluster stability and find marker genes for each cluster.
- Annotation and integration
Annotate cell types with known markers and with CellTypist, then integrate two samples with Harmony and check what changed.
- Project: a PBMC atlas
Build an annotated PBMC atlas in a reproducible notebook that documents every filtering and clustering decision.
Software, tools and datasets
Software and tools
All software is free and open source or free for academic use. 16 GB of RAM is recommended for the integration labs.
Datasets
- PBMC 3k: The core teaching dataset: about 2,700 peripheral blood mononuclear cells.10x Genomics public datasets
- A second public PBMC sample: Integration across samples.10x Genomics public datasets
- CellTypist reference models: Reference-based annotation.celltypist.org
Core reading
- Luecken MD, Theis FJ (2019). Current best practices in single-cell RNA-seq analysis: a tutorial. Molecular Systems Biology 15:e8746.
- Wolf FA, Angerer P, Theis FJ (2018). SCANPY: large-scale single-cell gene expression data analysis. Genome Biology 19:15.
- Heumos L et al. (2023). Best practices for single-cell analysis across modalities. Nature Reviews Genetics 24:550–572.
Duration, format and fees
- Next cohort
- Dates are sent with your admission offer
- Timetable
- Three evening or weekend sessions a week (IST), plus a lab clinic; intensive and weekend-only schedules on request
- Payment
- Secure online payment link with your admission offer (UPI, cards, net banking). Instalments on request for fees above ₹15,000.
- Refunds
- See the refund and cancellation policy.
Instructor
The instructor for each cohort is named in your admission offer and on this page when the cohort is announced. Vedicgen instructors are selected for hands-on research or industry experience in the subject they teach, and every course is reviewed by a second subject expert.
Questions about who teaches a cohort? [email protected]
Certificate
Learners who complete the course and pass its capstone receive a Vedicgen certificate of completion for Single-Cell RNA-seq Analysis, issued by Vivofold, stating the guided hours and skills assessed. Each certificate has a unique ID and QR code that anyone can check.
Apply for Single-Cell RNA-seq Analysis
Apply
Send the form below. It takes about five minutes.
Review
An adviser confirms your fit and format within two working days.
Offer
You receive dates, timetable and a secure payment link.
Onboard
Set-up guide, pre-reading and joining links.
Frequently asked questions
Both are taught side by side for the core steps, so you can work in either ecosystem and read papers that use either.
In the live cohort, modules are taught in instructor-led sessions with a weekly lab clinic where you work on real public data with help at hand; every session is recorded. The self-paced format uses the same lessons, labs and datasets with mentor review of your capstone. Both formats end with an assessed project reviewed for the code and the science.
A laptop with at least 8 GB of RAM and about 30 GB of free disk space, running Linux, macOS, or Windows with WSL2. 16 GB of RAM is recommended for the integration labs. Installation instructions are sent before the first session, and lab clinics help with any set-up problems.
Yes. Learners who complete the course and pass its assessment receive a Vedicgen certificate of completion, issued by Vivofold. Every certificate carries a unique ID and a QR code that anyone, including an employer or admissions office, can check on the certificate verification page.
Apply with the form on this page. Live and in-person cohorts run through the year; the dates, timetable and joining details of the next cohort are sent with your admission offer and listed on this page once announced. Self-paced enrolment opens as soon as your application is accepted.
Yes. No course runs longer than four weeks, and the length is customisable: choose the standard schedule, an intensive one- to two-week schedule, weekends only, or a custom timetable. Pick your preference on the application form; colleges and teams can book their own dates.
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