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This guide illustrates how to process FASTQ files produced using the 10x Genomics Chromium Single Cell ATAC assay to obtain a Single cell counts data node, which is the starting point for analysis of single-cell ATAC experiments.

If you are new to Partek® Flow®Partek Flow, please see Getting Started with Your Partek Flow Hosted Trial for information about data transfer and import and Creating and Analyzing a Project for information about the Partek Flow user interface.  

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We recommend uploading your FASTQ files (fastq.gz) to a folder on your Partek® Flow® Partek Flow server before importing them into a project. Data files can be transferred into Flow from the Home page by clicking the Transfer file button (Figure 1). Following the instruction In Figure 1 to complete the data transfer. Users have the option to change the Upload directory by clicking the Browse button and either select another existing directory or create a new directory. 

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Import the FASTQ files

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Convert FASTQ to count 

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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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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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Latent semantic indexing (LSI)  was first introduced for the analysis of scATAC-seq data by Cusanovich et al. 2018[2]. LSI combines steps of frequency-inverse document frequency (TF-IDF) normalization followed by singular value decomposition (SVD). Partek® Flow® wrapped Signac's TF-IDF normalization for single cell ATAC-seq dataset. It is a two-step normalization procedure that both normalizes across cells to correct for differences in cellular sequencing depth, and across peaks to give higher values to more rare peaks[3].

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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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