Partek Flow Documentation

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QA/QC & Data Processing

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

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  • We will first remove all non-expression features in the data (ege.g. NegProbes). Click on  Filtering > Filter features from the menu on the right. Select Metadata and set the task settings as follows, then click Finish:

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  • 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:

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  • 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 dependent and will change based on your dataset). Then click  and Apply observation filter to the filtered counts node:

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

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

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Data Exploration

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

  • Click on the normalised 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):

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  • 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

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While the UMAP is running we can also queue a clustering

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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 10 
  • In the advanced settings, set the resolution parameter to 8e-5 and click Apply:

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