Introduction to Spatial Transcriptomics Data Analysis

Authors

Learning outcomes

General learning outcomes

At the end of the course, the participants are expected to:

  • Explain the principles and describe applications of both sequencing-based and imaging-based spatially-resolved transcriptomics (SRT)
  • Identify potential pitfalls and limitations of SRT experiments and analysis workflows.
  • Define applications for cell segmentation and apply frequently-used cell segmentation methods
  • Assess and interpret raw outputs and spatial metadata files, understanding their structure and relevance for downstream analyses.
  • Define important aspects of quality control, feature selection, dimensionality reduction and differential gene expression to SRT data and apply those.
  • Clarify various spatial statistics and their application to biological questions.
  • Use frequently-used methods to analyze multi-sample SRT experiments.

Learning outcomes explained

To reach the general learning outcomes above, we have set a number of smaller learning outcomes. Each chapter starts with these smaller learning outcomes. Use these at the start of a chapter to get an idea what you will learn. Use them also at the end of a chapter to evaluate whether you have learned what you were expected to learn.

Learning experiences

To reach the learning outcomes we will use lectures, exercises, polls and group work. During exercises, you are free to discuss with other participants. During lectures, focus on the lecture only.

Exercises

Several blocks has practical work involved. Some more than others. We’ll have a (short) discussion after each exercise block. All answers to the practicals are incorporated, but they are hidden. Do the exercise first by yourself, before checking out the answer. If your answer is different from the answer in the practicals, try to figure out why they are different.