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A rectangle, or task node, will be created for Split matrix along with two output circles, or data nodes, one for each data type (Figure 2). The labels for these data types are determined by features.csv file used when processing the data with Cell Ranger. Here, our data is labeled Gene Expression, for the mRNA data, and Antibody Capture, for the protein data. 

Filter low-quality cells

An important step in analyzing single cell RNA-Seq data is to filter out low-quality cells. A few examples of low-quality cells are doublets, cells damaged during cell isolation, or cells with too few reads to be analyzed. In a CITE-Seq experiment, protein aggregation in the antibody staining reagents can cause a cell to have a very high number of counts; these are low-quality cells are can be excluded. Additionally, if all cells in a data set are expected to show a baseline level of expression for one of the antibodies used, it may be appropriate to filter out cells with very low counts. You can do this in Partek Flow using the Single cell QA/QC task. 

We will start with the protein data.

• Click the Antibody Capture data node
• Click the QA/QC section in the toolbox
• Click Single Cell QA/QC 
• Choose the assembly and annotation used for the gene expression data (Figure 3) from the drop-down menus
• Click Finish
  
  

This produces a Single-cell QA/QC task node (Figure 4). 

• Double-click the Single cell QA/QC task node to open the task report

ROADBLOCK NOTE - right-now it does not recalculate so this actually just creates the same report on both data nodes

ROADBLOCK NOTE - also running Filter cells creates layers instead of running on the branches as expected

The task report lists the number of counts per cell and the number of detected features per cell in two violin plots. For more information, please see our documentation for the Single cell QA/QC task. For this analysis, we will set a maximum counts threshold to exclude potential protein aggregates and, because we expect every cell to be bound by several antibodies, we will also set a minimum counts threshold. 

• Set the Counts filter to Keep cells between 500 and 20000 (Figure 5)
  
  

• Click Apply filter to run the Filter cells task

The output is a Filtered single cell counts data node (Figure 6).

Next, we can repeat this process for the Gene Expression data node. 

• Click the Gene Expression data node
• Click the QA/QC section in the toolbox
• Click Single Cell QA/QC
• Choose the assembly and annotation used for the gene expression data (Figure 3) from the drop-down menus
• Click Finish

This produces a Single-cell QA/QC task node (Figure 7). 

• Double-click the Single cell QA/QC task node to open the task report

The task report lists the number of counts per cell, the number of detected features per cell, and the percentage of mitochondrial reads per cell in three violin plots. For this analysis, we will set a maximum counts threshold maximum and minimum thresholds for total counts and detected genes to exclude potential doublets and a maximum mitochondrial reads percentage filter to exclude potential dead or dying cells. 

• Set the Counts filter to Keep cells between 1500 and 15000 
• Set the Detected genes filter to Keep cells between 400 and 4000
• Set the Mitochondrial counts filter to Keep cells between 0% and 20% (Figure 8)
  
  

• Click Apply filter to run the Filter cells task

The output is a Filtered single cell counts data node (Figure 9).

Normalization

After excluding low-quality cells, we can normalize the data. 

We will start with the protein data.

• Click the Antibody Capture data node
• Click the Normalization and scaling section in the toolbox
• Click Normalization
• Click the green plus next to CLR (Figure 10) or drag CLR to the right-hand panel
  
  

Centered log-ratio (CLR) normalization is the standard method for CITE-Seq data (.