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Course contentsIn this course, you will learn how to analyse single-cell RNA-seq data using the Seurat single cell tools integrated in the easy-to-use Chipster software. The exercises and course data are based on the Seurat guided analyses "Guided tutorial - 2700 PBMCs" and "Stimulated vs Control PBMCs".You will learn the following topics, and how to perform these steps in the Chipster software:perform quality control and filter out low quality cellsnormalize gene expression valuesscale data and remove unwanted sources of variationselect highly variable genesperform dimensionality reduction (PCA, tSNE, UMAP, CCA)cluster cellsfind marker genes for a clusterintegrate two samplesfind conserved cluster marker genes for two samplesfind genes which are differentially expressed between two samples in a cell type specific mannervisualize genes with cell type specific responses in two samples"It is so nice to be able to do the whole workflow in Chipster, compared to the old model, where I had to transfer the tsv file to R-studio and run Seurat there. -- I learned how to use the Seurat tools in Chipster and what all the steps really mean. I learned to check the results after every step to adjust the next steps parameters and to test different PCA plotting tools. I also learned how to find different genes in the clusters and how to visualize them. I never got this far using the R-pipeline. " -Pinja, course participant & PhD student from University of HelsinkiLearning objectivesAfter this course you should be able to:use the Seurat tools available in Chipster to undertake basic analysis of single cell RNA-seq dataname and discuss the different steps of single cell RNA-seq data analysisunderstand the advantages and limitations of single cell RNA-seq data analysis in general and in ChipsterKeywords: Chipster, Seurat, single cell sequencing, RNA-seq, clustering, aligning cells, cluster markersLinks to materialThe relevant material is linked in each course section. Here are some quick links:Course slides, exercises and other material (available also after the course)How to access ChipsterSeurat tools guided analysesArticle about sample integration in Seurat
PracticalitiesEach section of this course contains lecture videos, hands-on exercises and questions/tasks. The tasks can be used to confirm that you have reached the learning goals. You can use the Q&A Forum below to ask questions regarding the course topics or the exercises. Once you have finished all the tasks, you can download a course certificate with a unique course identifier. You can follow your progress with the progress bar on the right. The estimated time to complete the course is 2-3 days. In the certificate we recommend granting 1 credit (ECTS) for the course. In practical matters, please contact event-support (at) csc.fi, and in content related questions, chipster (at) csc.fi.
• ### 4. Normalize gene expression values

In this section we learn about normalising the data.

In this section, you will learn:

• Why do we need to normalize gene expression values
• What is a dropout
• What does global scaling normalization do and when does it not work well

First, watch the lecture video:

After watching the video, contemplate on the questions above. We will perform normalization in Chipster in section 6.

Finally, answer the quiz questions below:

3. Set up a Seurat object and check the quality of cells5. Identify highly variable genes