QA/QC & Data Processing

Once the data has been imported in the project we can start pre-processing the data:

We will first remove all non-expression features in the data (e.g. NegProbes).

• Click on  Filtering > Filter features from the menu on the right.
• Select Metadata and set the task settings as follows
• Click Finish

• Click on the resulting filtered counts node and select QA/QC > Single cell QA/QC, once the task has completed we can open the report by double-clicking the node:

• We will remove the cells with low counts and number of detected features. Click on Select & Filter and set lower threshold to 50 for both (remember that this is data-dependent and will change based on your dataset). Then click  and Apply observation filter to the filtered counts node:

• Click on the node generated by the filtering task, and click Filtering > Filter features. Apply a noise reduction filter:

• We can now normalize our filtered data. Click Normalization and scaling > Normalization. Use the recommended settings by clicking :

Data Exploration

Now that we have filtered low quality cells and normalized our data, we can start clustering to identify cell populations.

• Click on the normalized data node, then from the menu on the right select Exploratory analysis > PCA. We are going to use the top 2000 features by variance and calculate the first 50 principal components (PCs):

• Once the PCA has run, click on the resultant node and select Exploratory analysis > UMAP. Set the UMAP parameters as follows:
  • Top 20 PCs
  • Local neighborhood size 60
  • Minimal distance 0.20

While the UMAP is running we can also queue a clustering task. Select Exploratory analysis > Graph-based clustering. 

• We are going to use the Leiden algorithm to cluster our data (make sure to select the radio button for it)
• Set the number of PCs to 10 
• In the advanced settings, set the resolution parameter to 8e-5 and click Apply:

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