Exercises

 

Here we provide short tutorials on the different steps of scRNAseq analysis using either of the 3 commonly used scRNAseq analysis pipelines, Seurat Links to an external site., Scran Links to an external site. and Scanpy Links to an external site.. It is up to you which one you want to try out, if you finish quickly, you may have time to run several of them or run of the additional labs below. In principle we perform the same steps with all 3 pipelines, but there are some small differences as all different methods are not implemented in all the pipelines.

 

MAIN exercises

All scripts (Rmarkdown or ipython notebooks) can be found at our github repo in folder labs/compiled Links to an external site.

During this workshop, you will use conda environments to run the exercises. This is because conda environments allow all users to have the same computing environment, i.e. package versions. This enforces reproducibility for you to run this material without the need to re-install or change your local versions. Please me sure you have completed the Precourse material. The environment for this course (2022) is this:

 

Tutorial  Seurat  Scater/Scran  Scanpy
 Quality Control Seurat_qc Links to an external site. (.Rmd Links to an external site.) Scater_qc Links to an external site. (.Rmd Links to an external site.) ScanPY_qc Links to an external site. (.ipynb Links to an external site.)
 Dimensionality reduction Seurat_dr Links to an external site. (.Rmd Links to an external site.) Scater_dr Links to an external site. (.Rmd Links to an external site.) Scanpy_dr Links to an external site. (.ipynb Links to an external site.)
 Data integration Seurat_integr Links to an external site. (.Rmd Links to an external site.) Scater_integr Links to an external site. (.Rmd Links to an external site.) Scanpy_integr Links to an external site. (.ipynb Links to an external site.)
 Clustering Seurat_clust Links to an external site. (.Rmd Links to an external site.) Scater_clust Links to an external site. (.Rmd Links to an external site.) Scanpy_clust Links to an external site. (.ipynb Links to an external site.)
 Differential expression Seurat_dge Links to an external site. (.Rmd Links to an external site.) Scater_dge Links to an external site. (.Rmd Links to an external site.) Scanpy_dge Links to an external site. (.ipynb Links to an external site.)
 Celltype prediction Seurat_ct Links to an external site. (.Rmd Links to an external site.) Scater_ct Links to an external site. (.Rmd Links to an external site.) Scanpy_ct Links to an external site. (.ipynb Links to an external site.)
 Spatial transcriptomics Seurat_ST Links to an external site. (.Rmd Links to an external site.) Scater_ST Links to an external site. (.Rmd Links to an external site.) Scanpy_ST Links to an external site. (.ipynb Links to an external site.)
 Trajectory inference Slingshot_ti Links to an external site. (.Rmd Links to an external site.) .Rmd Links to an external site.) PAGA_ti Links to an external site. (.ipynb Links to an external site.)

 

Above you will find the link to the .Rmd/.ipynb that you should use as well as the rendered exercise report ("answers"). The easiest way of getting started with the exercises is to download the .Rmd/.ipynb file and then open it with Rstudio / Jypyter Notebooks. First activate your conda environment, then copy the link of a .Rmd/.ipynb file and then type:

wget <LINK_TO_Exercise1.Rmd_FILE>
rstudio Exercise1.Rmd &

Or in python for .ipynb:

wget <LINK_TO_Exercise1.ipynb_FILE>
jupyter notebook Exercise1.ipynb &

We highly recommend you to use the files provided instead of copying and pasting from the rendered report. Keep in mind that the results may vary slightly depending on the parameters used.

Many additional information and explanations can be found in the Single Cell Glossary Links to an external site.

 

 

FAQ

As you run into problems, we will try to fill in the FAQ Links to an external site. with common questions.