BioData.pt Crash Course: Differential gene expression analysis in single-cell RNAseq
BioData.pt Crash Course: Differential gene expression analysis in single-cell RNAseq is an introduction to differential gene expression analysis of medium-sized single-cell RNA-seq (scRNA-seq) obtained using the 10x Genomics Chromium System. Participants will learn about the basic concepts behind the technology, its applications and concepts, and go through a hands-on exercise where they will perform a re-analysis of a sample of ~3,000 cells from the Mouse Cell Atlas (MCA). For this they will make use of a user-friendly web application built on top of the popular Seurat package, that allows interactive exploration of the concepts involved in the analysis, including filtering, normalization, dimensionality reduction, clustering, and marker gene identification.
If you're a life scientist who wants to gain a deeper learning of RNAseq methodologies, this course is for you! Join us on the 30th of January at the IGC.
Attending the course requires registration.
Attendance fees: The attendance fee for the course is 40€ for attending this course, or 70€ for attending both this course and the "Microbiome Visualization with Biome-Shiny" course, to be realized on January 31st. Attendance fees are waived for members of the BioData.pt and IGC communities.
Target audience: Researchers that wish to get acquainted with the concepts and methodologies used in single-cell RNAseq.
Trainers: Daniel Neves, Ricardo Leite (BioData.pt/IGC)
Pre-requisites: A basic understanding of Next-Generation sequencing technologies is advisable but not necessary.
Computers will be provided for participants.
Morning period: 09:30-12:30
Single-cell RNA-seq (scRNA-seq) versus (bulk) RNA-seq.
Understanding the scRNA-seq UMI count matrix.
Overview of scRNA-seq data analysis and basic concepts.
Afternoon period: 14:00-17:30
Quality control and filtering.
Dimensionality reduction (PCA, t-SNE, UMAP).
Clustering of cell subpopulations.
Marker gene identification and differential expression analysis.
Genomics Unit - IGC (UG-IGC)