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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". This course contains two types of lecture videos: short lectures on each topic by trainers from CSC (ELIXIR-FI), and more in-depth lectures by Paulo Czarnewski (NBIS / ELIXIR-SE), Ahmed Mahfouz (LUMC / ELIXIR-NL) and Jules Gilet (ELIXIR-FR).

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.
• ### 7. Dimension reduction

In this section, you will learn about dimension reduction (PCA, tSNE, UMAP) and selecting the principal components for the clustering step.

The data has multiple dimensions, as there can be thousands of cells and thousands of genes. This would make the data tricky to deal with, which is why dimension reduction step is needed. We reduce the dimensions to ease the clustering step, and also to make the visualisation of the data possible.

First, watch the theory lectures about dimensional reduction:

For more theory, you can view this lecture video and slides by Paulo Czarnewski (NBIS, ELIXIR-SE):

Next, do the following exercises in Chipster:

Principal component analysis
Select seurat_obj_preprocess.Robj from the previous step and run the tool Single cell RNA-seq / Seurat -PCA. Open PCAplots.pdf. Look at the heatmaps and the standard deviation of PCs in the last two pages.

How many principal components should we continue the analysis with (check the elbow in the standard deviation plot, inspect the heatmaps)? Would 10 be ok?

Finally, answer the questions in the quiz:

6. Scale data and regress out unwanted sources of variation8. Cluster cells and detect marker genes for clusters