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To create a new project, from the Home page click the New Project button; enter a project name and then click Create project (Figure 1). Once a new project has been created, the user is automatically directed to the Data tab of the Project Viewclick the Add data button in the Analyses tab. 

Import the FASTQ files

To proceed, click the Import Add data button in the Data Analyses tab (Figure 2). Click the Automatically create samples from files buttonIn the Single cell > scATAC-Seq section select fastq and click Next. The file browser interface will open (Figure 3). Select the FASTQ files using the file browser interface and push the Create sample button Finish button to complete the task. Paired end reads will be automatically detected and multiple lanes for the same sample will be automatically combined into a single sample. We encourage users to include all the FASTQ files including the index files although they are optional. 

When the FASTQ files have finished importing, the Unaligned reads data node will turn from transparent to opaqueappear in the Analyses tab.

Convert FASTQ to count 

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• Click the Unaligned reads data node
• Select Cell Ranger - ATAC in the 10x Genomics section in the task menu on the right
• Select Single cell ATAC in Assay type for ATAC-Seq data only
• Choose the proper Reference assembly for the data (you may have to create the reference)
• Press the Finish button to run the task with default settings (Figure 4)
  
  

To learn more about how to run Cell Ranger - ATAC task in Flow, please refer to our online documentation.

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QA/QC

An important step in analyzing single cell ATAC data is to filter out low quality cells. A few examples of low-quality cells are doublets, cells with a low TSS enrichment score, cells with a high proportion of reads mapping to the genomic blacklist regions, or cells with too few reads to be analyzed. Users are able to do this in Partek Flow using the Single cell QA/QC task. 

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Numbered figure captions
SubtitleText QA/QC task report for scATAC - Seq data in Flow. AnchorName QA/QC task report
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The Single cell QA/QC report includes interactive violin plots showing the value of every cell in the project on several quality measures (Figure 6).

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To filter out low quality cells (Figure 7), 

• Open the Select & Filter menu
• Set the filters on nucleosome signal < 4; Peak region fragment 500-30000; and % reads in peaks > 15% ; Blacklist ratio < 0.05leave the rest as they are
• Click the filter icon Image Addedand Apply observation filter to run the Filter cells task on the first Single cell ATAC counts data node, it generates a Filtered cells node
• Click PCA from the drop-down list

Filter features

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Annotate regions

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The input for Annotate peaks is a Peaks type data node. 

• Click a Peaks the Filtered features data node
• Click the Peak analysis section in the toolbox
• Click Annotate regions
• Set the Genomic overlaps parameter

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Users are able to define the transcription start site (TSS) and transcription termination site (TTS) limit in the unit of bp.

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To run TF-IDF normalization, 

• Click a single Single cell counts data node, in this case the Annotated regions node
• Click the Normalization and scaling section in the toolbox
• Click TF-IDF normalization

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Graph-based clustering

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A new Graph-based clusters data and a Biomarkers data node will be generated. 

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UMAP

Similar to t-SNE, Uniform Manifold Approximation and Projection (UMAP) is a dimensional reduction technique. UMAP aims to preserve the essential high-dimensional structure and present it in a low-dimensional representation. UMAP is particularly useful for visually identifying groups of similar samples or cells in large high-dimensional data sets. 

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Promoter sum matrix

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Classifying cells

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• Make sure the right data source has been selected. For scATAC-seq data, it shall be the normalized counts of  promoter sum values in most cases (Figure 17) 
• Set Color by in the Style configuration to the normalized counts node
• Type MS4A1 in the search box and select it. Rotate the 3D plot if you need to see this cluster more clearly. 
• Click Click Image Added to activate Lasso mode
• Draw a lasso around the cluster of MS4A1-expressing cells 
• Click Classify selection under Tools in the left panel
• Type B cells for the Name
• Click Save (Figure 18)

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Differential analysis

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• Click Next 
• Define comparisons between factor or interaction levels (Figure 20)
• Click Add comparison to add the comparison to the Comparisons table. 
• Click Finish to run the statistical test as default

Hurdle model produces a Feature list task node. The results table and options are the same as the GSA task report except the last two columns. The percentage of cells where the feature is detected (value is above the background threshold) in different groups (Pct(group1), Pct(group2)) are calculated and included in the Hurdle model report. 

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Numbered figure captions
SubtitleText Generate filtered node for differential analysis results in Flow. AnchorName Generate filtered node
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Once we have filtered a list of differentially expressed genes, we can visualize these genes by generating a heatmap, or perform the Gene set enrichment analysis and motif detection. 

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For information about automating steps in this analysis workflow, please see our documentation page on Making a Pipeline. 

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