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 (eg. NegProbes). Click on  Filtering > Filter features from the menu on the right. Select Metadata and set the task settings as follows, then 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-depended 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 normalise our filtered data. Click Normalization and scaling > Normalisation. Use the recommended settings by clicking :

Data Exploration

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

• Click on the normalised 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. Run the UMAP as default:

• While the UMAP is running we can also queue a clustering analysis. Select Exploratory analysis > Graph-based clustering. We are going to use the Leiden algorithm to cluster our data. In the advanced settings, set the resolution parameter to 1e-4 and click Apply:

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