QA/QC, data processing, and dimension reduction

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 the resulting filtered counts node
Select QA/QC > Single cell QA/QC from toolbox, 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)
Click Filter include
Click Apply observation filter to the filtered counts node:

Click on the node generated by the filtering task in the Analyses tab.

We can now normalize our filtered data.

Click Normalization and scaling > Normalization. Use the recommended settings by clicking :

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
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 PCA result node in the Analyses tab.

Select Exploratory analysis > UMAP from the toolbox. 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:

If you need additional assistance, please visit our support page to submit a help ticket or find phone numbers for regional support.

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