QA/QC, data processing, and dimension reduction
Last updated
Last updated
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
In the Analyses tab
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 Apply observation filter to the filtered counts node:
Click on the node generated by the filtering task in the Analyses tab.
Click Filtering > Filter features. Apply a noise reduction filter:
We can now normalize our filtered data.
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. Click on the PCA result node in the Analyses tab, 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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Click Filter include
Click Normalization and scaling > Normalization. Use the recommended settings by clicking :
