Spatial transcriptomics (Visium) data analysis with Chipster

This hands-on course in Zoom introduces the analysis of spatially resolved transcriptomics (Visium) data. It covers the processing of transcript counts from quality control and filtering to dimensional reduction, clustering, cell type identification and detection of spatially variable genes. You will also learn how to do integrated analysis of multiple samples. The free and user-friendly Chipster software is used in the exercises, and the course is thus suitable for everybody.

Prerequisites

This course is a continuation to the course Single-cell RNA-seq data analysis with Chipster. We will not repeat the theory of the analysis steps which are shared between the two types of data, but focus on steps specific for spatial data. Therefore we expect that you have prior knowledge of scRNA-seq data analysis (if you don’t, please note that you can do the MOOC Single-cell RNA-seq data analysis with Chipster prior to the course).

Practicalities

The course consists of lectures and exercises. The lectures will be pre-recorded, and participants are requested to view the videos prior to the course and test their knowledge with a set of questions. This gives you more time to reflect on the concepts so that you can use the classroom time more efficiently for discussions and exercises.

Trainers

Eija Korpelainen, Maria Lehtivaara

Learning goals

You will learn how to:
– perform quality control and filter out low quality spots
– normalize gene expression values
– remove unwanted sources of variation
– select highly variable genes and perform dimensionality reduction (PCA)
– cluster spots
– visualize clusters using UMAP and tSNE
– identify genes which have spatial patterning without taking clusters or spatial annotation into account
– subset out anatomical regions
– integrate spatial data with single-cell data
– identify cell types
– integrate multiple samples