End-to-End scRNA-seq Workflow
Process single-cell RNA-seq data from raw count matrices to biologically interpretable clusters and markers.
Accelerated Single-Cell Data Analysis
A practical course taught live in ScarfWeb, our browser-based GUI workbench for single-cell analysis. Your team goes from raw count matrices to confident biological insight in under 3 hours.
Every step happens in ScarfWeb
Instructors demo the full single-cell workflow in ScarfWeb, Nygen's no-code GUI workbench for single-cell analysis. Participants follow along in the browser with no local compute and no software to install. Existing results from Seurat, Scanpy, or Bioconductor pipelines can be imported whenever they help.
What You Will Learn
Core capabilities your team builds during the session.
ScarfWeb Fluency
Navigate upload, quality control, integration, exploration, and export confidently in the ScarfWeb GUI.
Quality and Batch Effects
Interpret quality control metrics, tune filters, and correct batch effects across experiments.
Cell-Type Annotation
Annotate cell types with marker evidence to uncover meaningful biological insights.
Differential Expression
Define cell selections, set up contrasts, and interpret differential gene expression results.
Course Outline
A structured 2.5-hour journey from raw data to publishable results. CITE-seq and HTO upload can be covered during the data steps where audience interest allows.
Introduction
Course goals, what single-cell analysis can answer, and how the session is structured. (5 min)
Single-Cell Data Journey
From sequencing to count matrices: what the data is and how it reaches ScarfWeb. (15 min)
Data Upload on ScarfWeb
Upload count matrices, attach metadata, and set up your project in the workbench. (15 min)
Analysis Setup and Best Practices
Configure the analysis, choose parameters, and apply best practices for reproducible workflows. (25 min)
Break and Q&A
Open session with the instructors to discuss your own data and analysis questions. (10 min)
UMAP Exploration and Clustering
Explore embeddings, inspect clusters, and read what the structure is telling you. (10 min)
Data Quality and Batch Effects
Assess data quality and correct batch effects before drawing conclusions. (10 min)
Cell-Type Annotation
Assign cell types from marker evidence and review annotation confidence. (10 min)
Cell Selections and Differential Expression
Create cell selections and run differential gene expression between groups of interest. (15 min)
Publishing and Wrap-Up
Publish and share results, export outputs, and recap next steps. (15 min)
What You Do Hands-On in ScarfWeb
Each part of the live session maps to a step you run yourself in the workbench.
Upload Matrices and Metadata
Bring in preprocessed count matrices in common formats and attach sample and cell-level annotations.
Quality Control and HVG Selection
Filter cells by quality metrics and select highly variable genes with adjustable thresholds.
Integration and Dimensionality Reduction
Harmonize datasets across experiments, then run dimensionality reduction for joint analysis.
UMAP Exploration and Selections
Navigate cluster embeddings, inspect groups, and define cell selections for downstream comparisons.
Differential Expression and Markers
Compare cell groups, identify marker genes, and interpret statistical outputs in context.
Share and Export
Publish findings, generate shareable links, and export outputs for further analysis.
Who This Course Is For
Built for teams that need practical single-cell analysis fluency.
PhD Students and Postdocs
Build clear analysis instincts early and understand why each workflow step matters.
Experimental Researchers
Interpret outputs with confidence and connect single-cell results to biological decisions.
Bioinformatics and Core Facility Teams
Standardize best practices across projects and support users on reproducible workflows.
Course Instructors
Learn directly from domain experts in single-cell biology and computational genomics.
Goran Karlsson
Co-founder and Head of Partnerships, Nygen
PhD, Associate Professor in Molecular Hematology, Lund University.
Parashar Dhapola
Co-founder and CEO, Nygen
PhD, Computational Genomics.
Frequently Asked Questions
No. The course is completely free of charge. Nygen does not require the hosting facility to purchase or license any software.
All hands-on work runs in ScarfWeb, Nygen's browser-based GUI workbench for single-cell analysis. Participants only need a laptop with internet access.
None beyond a laptop with internet access. All analysis runs on ScarfWeb's cloud platform. No software installation is required.
Instructors and participants work directly in ScarfWeb, on ISO 27001 Ready, GDPR-compliant cloud infrastructure. There are no infrastructure demands on the hosting facility.
Yes. The interactive portion of the session supports working with participant datasets, and the break includes open Q&A about your own projects.
The core curriculum focuses on single-cell RNA-seq. Where audience interest allows, instructors can briefly cover CITE-seq and HTO upload in ScarfWeb.
Approximately 2 hours and 30 minutes, covering upload through publishing in ScarfWeb, including an interactive segment where participants can work with their own data.
The hosting facility should provide a conference room or lecture room suitable for the expected audience size.
Book this course for your institution
We bring the course to you. Free for core facilities, research groups, and academic institutions.
