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, 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.
We recommend uploading your FASTQ files (fastq.gz) to a folder on the 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.
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 View.
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To proceed, click the Import button in the Data tab (Figure 2). Click the Automatically create samples from files button. The file browser interface will open (Figure 3). Select the FASTQ files using the file browser interface and push the Create sample 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 opaque.
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Because different cells will have a different number of total counts, it is important to normalize the data prior to downstream analysis. For droplet-based single cell isolation and library preparation methods that use a 3' counting strategy, where only the 3' end of each transcript is captured and sequenced, we recommend the following normalization - 1. CPM (counts per million), 2. Add 1, 3. Log2. This accounts for differences in total UMI counts per cell and log transforms the data, which makes the data easier to visualize.
to add the recommended normalization scheme This adds CPM (counts per million), Add 1, and Log2 to the Normalization order panel. Normalization steps are performed in descending order.
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A new Normalized counts data node will be produced. You can choose to change the color of this node by right-clicking on the task node then clicking Change color and/or rename the result node by right-clicking and selecting Rename data node.
In the example below, I have changed the color to dark blue and renamed the results node based on the scheme.
For more information on normalizing data in Partek Flow, please see the Normalize Counts section of the user manual.
A common task in bulk and single-cell RNA-Seq analysis is to filter the data to include only informative genes (features). Because there is no gold standard for what makes a gene informative or not and ideal gene filtering criteria depend on your experimental design and research question, Partek Flow has a wide variety of flexible filtering options. The Filter features step can also be performed before normalization.
There are four categories of filter available - noise reduction, statistics based, feature metadata, and feature list.
The noise reduction filter allows you to exclude genes considered background noise based on a variety of criteria. The statistics based filter is useful for focusing on a certain number or percentile of genes based on a variety of metrics, such as variance. The feature list filter allows you to filter your data set to include or exclude particular genes.
For example, you can use a noise reduction filter to exclude genes that are not expressed by any cell in the data set, but were included in the matrix file.
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This results node, Filtered counts, will be the starting point for the next stage of analysis.
Principal components (PC) analysis (PCA) is an exploratory technique that is used to describe the structure of high dimensional data by reducing its dimensionality. Because PCA is used to reduce the dimensionality of the data prior to clustering as part of a standard single cell analysis workflow, it is useful to examine the results of PCA for your data set prior to clustering.
You can choose Features contribute equally to standardize the genes prior to PCA or allow more variable genes to have a larger effect on the PCA by choosing by variance. By default, we take variance into account and focus on the most variable genes.
If you have multiple samples, you can choose to run PCA for each sample individually or for all samples together by selecting or not selecting the Split cells by sample option (Figure 5).
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A new PCA task node will be produced.
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Beside PCA coordinates of the cells, PCA task report also includes, the Scree plot, the component loadings table, and the PC projections table.
The Scree plot lists PCs on the x-axis and the amount of variance explained by each PC on the y-axis, measured in Eigenvalue. The higher the Eigenvalue, the more variance is explained by the PC. Typically, after an initial set of highly informative PCs, the amount of variance explained by analyzing additional PCs is minimal. By identifying the point where the Scree plot levels off, you can choose an optimal number of PCs to use in downstream analysis steps like graph-based clustering, UMAP and t-SNE.
, choose the PCA data node (Figure 7)Note that Partek Flow suggests appropriate data for each plot type that is chosen so only PCA results will be available to select from for the Scree plot.
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In this data set, a reasonable cut-off could be set anywhere between 7 and 20 PCs.
Viewing the genes correlated with each PC can be useful when choosing how many PCs to include.
option in the New plot icon under Setup and select the PCA data node to open the Component loadings table (Figure 8)
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This table lists genes on rows and PCs on columns, the value in this table is correlation coefficient r. The table can be downloaded as a text file by clicking on the Export table data icon 
on the upper-right corner of the plot.
To display PCA projects table, click on the Table drop-down list in the Content icon under Configure and choose PCA projections (Figure 9)
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PCA projections table contains each row as an observation (a cell in this case), each column represents one principal component (Figure 10). This table can be downloaded as text file, the same way as the component loading table.
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Graph-based clustering identifies groups of similar cells using PC values as the input. By including only the most informative PCs, noise in the data set is excluded, improving the results of clustering.
Clustering can be performed on each sample individually or on all samples together. Here, we are working with a single sample.
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The Number of principal components should be set based on the your examination of the Scree plot and component loadings table. The default value of 100 is likely exhaustive for most data sets, but may introduce noise that reduces the number of clusters that can be distinguished.
A new Graph-based clusters data and Biomarkers data node will be generated along with the task nodes.
The Graph-based clustering result lists the Total number of clusters and what proportion of cells fall into each cluster as well as Maximum modularity which is a measurement of the quality of the clustering result where optimal modularity is 1. The Biomarkers node includes the top features for each graph-based cluster. It displays the top-10 genes that distinguish each cluster from the others. Download at the bottom right of the table can be used to view and save more features. These are calculated using an ANOVA test comparing the cells in each group to all the other cells, filtering to genes that are 1.5 fold upregulated, and sorting by ascending p-value. This ensures that the top-10 genes of each cluster are highly and disproportionately expressed in that cluster.
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We will use t-SNE to visualize the results of Graph-based clustering.
t-Distributed Stochastic Neighbor Embedding (t-SNE) is a dimensional reduction technique that prioritizes local relationships to build a low-dimensional representation of the high-dimensional data that places objects that are similar in high-dimensional space close together in the low-dimensional representation. This makes t-SNE well suited for analyzing high-dimensional data when the goal is to identify groups of similar objects, such as cell types in single cell RNA-Seq data.
If you have multiple samples, you can choose to run t-SNE for each sample individually or for all samples together using the Split cells by sample option. Please note that this option will not be present if you are running t-SNE on a clustering result. For clarity, clustering results run with all samples together must be viewed together and clustering results run by sample must be viewed by sample.
Like Graph-based clustering, t-SNE takes PC values as its input and further reduces the data down to two or three dimensions. For consistency, you should use the same number of PCs as the input for t-SNE that you used for Graph-based clustering.
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A new t-SNE task node will be produced.
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The t-SNE scatter plot is interactive and can be viewed for 2D or 3D (calculated separately). The t-SNE plot is 3D by default. You can rotate the 3D plot by left-clicking and dragging your mouse or using Control under Configure. You can zoom in and out using your mouse wheel. You can pan by right-clicking and dragging your mouse. You can use Style to modify color, shape, size, and labeling (e.g. add a fog effect to improve depth perception on the plot). Add a 2D plot clicking New plot, selecting 2D Scatter plot and selecting t-SNE as the source of the data.
Click on the plot to ensure that the plot window is selected. Click Style under Configure to color the t-SNE.
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The cells on the plot will be colored based on their expression level of CD79A (Figure 16). In the example in Figure 16, the Style icon has been dragged to a different location on the screen and the legend has also been resized and moved. Resizing the legend can either be done on the legend itself or using the Description icon under Configure.
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Coloring by one gene uses the two-color numeric palette, which can be customized by clicking 
. To color by more than one gene use the Numeric triad option in the drop-down. If you color by more than one gene, the color palette switches to a Green-Red-Blue color scheme with the balance between the three color channels determined by the values of the three genes. For example, a cell that expresses all three genes would be white, a cell that expresses the first two genes would be yellow, and a cell that expresses none of the genes would be black (Figure 17).
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Clicking a cell on the plot shows the expression values of the cell in the legend. Hovering over a cell on the plot also shows this information and related details (Figure 18).
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If you want to color by more than three genes at time, such as by a list of genes that distinguish a particular cell type, you can use the color by Feature list option.
Coloring by a list, in this way, calculates the first three principal components for the gene list and colors the cells on the plot by their values along those three PCs with green for PC1, red for PC2, and blue for PC3 (Figure 19).
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Typically, the expression of a set of marker genes will be highly correlated, allowing the first PC to account for a large percentage of the variance between cells for that gene list. As a result, the group of cells characterized by their expression of the genes on the list will separate from the rest of the cells along PC1 and will be colored green (Figure 16). If the gene list is more complex, for example, including marker genes for multiple cell types, there may be several sets of correlated genes accounting for significant amounts of variance, leading to groups of cells being distinguishable along PC2 and PC3 as well. In that case, there may be green, blue, and red groups of cells on the plot. If the gene list does not distinguish any group of cells, all cells will have similar PC values, leading to similarly colored cells on the plot.
In addition to coloring by gene expression and by gene lists, the points can be colored by any cell or sample attribute. Available attributes are listed as options in the Color by drop-down menu. Note that any available options are dependent upon the selected data node.
The most basic way to select a point on the scatter plot is to click it with the mouse while in pointer mode. To select multiple cells, you can hold Ctrl on your keyboard and click the cells. To select larger groups of cells, you can switch to Lasso mode by clicking 
in the plot controls. The lasso lets you freely draw a shape to select a cluster of cells.
to activate Lasso modeYou can also create a lasso with straight lines using Lasso mode by clicking, releasing, and clicking again to draw a shape.
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By default, selected cells are shown in bold while unselected cells are dimmed (Figure 21). This can be changed to gray selected cells using the Select & Filter tool in the left panel as shown in Figure 21.
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Alternatively, you can select cells using any criteria available for the data node that is selected in the Select & Filter tool. To change the data selection click the circle (node) and select the data.
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This adds check boxes for each level of the attribute (i.e., clusters). Click a check box to select the cells with that attribute level.
This selects cells from Graph-based clusters 2 and 3 (Figure 23). The number of selected cells is listed in the Legend on the plot.
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Cells can also be selected based on their gene expression values in the Select & Filter section.
Very specific selections can be configured by adding criteria in this way. In the example below, Clusters 2 and 3 and high CD3D expression is selected (Figure 24).
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Once a cell has been selected on the plot, it can be filtered. The filter controls can exclude or include (only) any selected cell. Filtering can be particularly useful when you want to use a gene expression threshold to classify a group of cells, but the gene in question is not exclusively expressed by your cell type of interest.
In this example we can filter to include just cells from the selection we have already made.
(filter include) to filter to just the selected cells (Figure 25). The plot will update to show only the included cells as seen in Figure 25.
Cells that are not shown on the plot cannot be selected, allowing you to focus on the visible cells. The number of cells shown on the plot out of the total number of original cells is shown in the Legend. You can adjust the view to focus on only the included cells.
on the plot controls or toggle on Fit visible in the Axes configuration to rescale the axes to the filtered pointsTo revert to the original scaling, click the 
button again or turn off Fit visible with the toggle.
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(filter exclude) (Figure 26)Additional inclusion or exclusion filters can be added to focus on a smaller subset of cells.
The plot will update to show all cells and return to the original scaling.
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Classifying cells allows to you assign cells to groups that can be used in downstream analysis and visualizations. Commonly, this is used to describe cell types, such as B cells and T cells, but can be used to describe any group of cells that you want to consider together in your analysis, such as cycling cells or CD14 high expressing cells. Each cell can only belong to one class at a time so you cannot create overlapping classes.
To classify a cell, just select it then click Classify selection in the Classify tool.
For example, we can classify a cluster of cells expressing high levels of CD79A as B cells.
to activate Lasso mode
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Because most of these cells express CD79A, a B cell marker, and because they cluster together on the t-SNE, suggesting they have similar overall gene expression, we believe that all these cells are B cells.
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The classification, B cells, is added to the Classifications section of the control panel and the number of cells in that classification is listed next to the name (Figure 29).
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You can edit the name of a classification or delete it. The classifications you have made are saved as a working draft so if you close the plot and return to it, the classifications will still be there and can be visualized on the plot as "New classification". However, classifications are not available for downstream tasks until you apply them. Continue classifying the clusters and save the Data viewer session until you are ready to apply the classification to the data project.
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To use the classifications in downstream tasks and visualizations, you must first apply them.
Once you have added a classification to the project, you can color the t-SNE plot by the Classification.
Here, I classified a few additional cell types using a combination of known marker genes and the clustering results then applied the classification (Figure 31).
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Summarize Classifications with the number and percentage of cells from each sample that belong to each classification using an Attribute table under New plot. This is particularly useful when you are classifying cells from multiple samples.
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A common goal in single cell analysis is to identify genes that distinguish a cell type. To do this, you can use the differential analysis tools in Partek Flow. I will show how to use the Gene Specific Analysis (GSA) test in Partek Flow, which on its default settings is equivalent to limma-trend, a statistical test shown to be highly effective for differential analysis of single cell RNA-Seq data (Soneson and Robinson 2018).
The first page of the configuration dialog asks what attributes you want to include in the statistical test. Here, we only want to consider the Classifications, but in a more complex experiment, you could also include experimental conditions or other sample attributes.
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We will make a comparison between NK cells and all the other cell types to identify genes that distinguish NK cells. You can also use this tool to identify genes that differ between two cell types or genes that differ in the same cell type between experimental conditions.
The top panel is the numerator for fold-change calculations so the experimental or test groups should be selected in the top panel.
The bottom panel is the denominator for fold-change calculations so the control group should be selected in the bottom panel.
This adds the comparison to the statistical test.
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The GSA task report lists genes on rows and the results of the statistical test (p-value, fold change, etc.) on columns (Figure 37). For more information, please see our documentation page on the GSA task report.
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Genes are listed in ascending order by the p-value of the first comparison so the most significant gene is listed first. To view a volcano plot for any comparison, click 
. To view a violin plot for a gene, click 
next to the Gene ID.
for KLRD1The Feature plot viewer will open showing a violin plot for KLRD1 (Figure 38). The violins are density plots with the width corresponding to frequency.
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You can switch the grouping of cells using the Group by drop-down menu. The order of groups can be adjusted by dragging groups up and down in the Group order panel. To navigate between genes in the table, click the Next > and Previous > buttons.
The table lists all of genes in the data set; using the filter control panel on the left, we can filter to just the genes that are significantly different for the comparison.
Here, we are using a very stringent cutoff to focus only on genes that are specific to NK cells, but other applications may require a less stringent cutoff.
The number of genes at the top of the filter control panel updates to indicate how many genes are left after the filters are applied.
to generate a filtered version of the table for downstream analysis (Figure 39)
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The GSA report will close and a new task, the Differential analysis filter, will run and generate a filtered Feature list data node.
For more information about the GSA task, please see the Differential Gene Expression - GSA section of our user manual.
Once we have filtered to a list of significantly different genes, we can visualize these genes by generating a heatmap.
The hierarchical clustering task will generate the heatmap; choose Heatmap as the plot type. You can choose to Cluster features (genes) and cells (samples) under Feature order and Cell order in the Ordering section. You will almost always want to cluster features as this generates the clear blocks of color that make heatmaps comprehensible. For single cell data sets, you may choose to forgo clustering the cells in favor of ordering them by the attribute of interest. Here, we will not filter the cells, but instead order them by their classification.
You can filter samples using the Filtering section of the configuration dialog. Here, we will not filter out any samples or cells.
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It may initially be hard to distinguish striking differences in the heatmap. This is common in single cell RNA-Seq data because outlier cells will skew the high and low ends. We can adjust the minimum and maximum of the color scheme to improve the appearance of the heatmap.
Distinct blocks of red and blue are now more pronounced on the plot. Cells are on rows and genes are on columns. Because of the limited number of pixels on the screen, genes are grouped. You can zoom in using the zoom controls or your mouse wheel if you want to view individual gene rows. We can annotate the plot with cell attributes.
The plot now includes blocks of color along the left edge indicating the classification of the cells. We can transpose the plot to give the cell labels a bit more space.
We can also customize the colors of the plot. Do this by clicking the Legend or Heatmap
The heatmap now shows a teal to yellow gradient with a black midpoint (Figure 41).
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As with any visualization in Partek Flow, the image can be saved as a publication-quality image to your local machine by clicking 
or sent to a page in the project notebook by clicking 
. For more information about Hierarchical clustering, please see the Hierarchical Clustering section of the user manual.
While a long list of significantly different genes is important information about a cell type, it can be difficult to identify what the biological consequences of these changes might be just by looking at the genes one at a time. Using enrichment analysis, you can identify gene sets and pathways that are over-represented in a list of significant genes, providing clues to the biological meaning of your results.
We distribute the gene sets from the Gene Ontology Consortium, but Gene set enrichment can work with any custom or public gene set database.
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The Gene set enrichment task report lists gene sets on rows with an enrichment score and p-value for each. It also lists how many genes in the gene set were in the input gene list and how many were not (Figure 43). Clicking the Gene set ID links to the geneontology.org page for the gene set.
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In Partek Flow, you can also check for enrichment of KEGG pathways using the Pathway enrichment task. The task is quite similar to the Gene set enrichment task, but uses KEGG pathways as the gene sets.
The task report is similar to the Gene set enrichment task report with enrichment scores, p-values, and the number of genes in and not in the list (Figure 44).
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Clicking the KEGG pathway ID in the Pathway enrichment task report opens a KEGG pathway map (Figure 45). The KEGG pathway maps have fold-change and p-value information from the input gene list overlaid on the map, adding a layer of additional information about whether the pathway was upregulated or downregulated in the comparison.
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Color are customizable using the control panel on the left and the plot is interactive. Mousing over gene boxes gives the genes accounted for by the box, with genes present in the input list shown in bold, and the coloring gene shown in red (Figure 46).
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Clicking a pathway box opens the map of that pathway, providing an easy way to explore related gene networks.
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For information about automating steps in this analysis workflow, please see our documentation page on Making a Pipeline.
Soneson C and Robinson MD. Bias, robustness and scalability in single-cell differential expression analysis. Nature Methods 2018 Apr;15(4):255-261.
