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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.
- Double-click any blank section of the scatter plot to clear the selection
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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.
- Choose Graph-based from the Criteria drop-down menu in the Select & Filter tool after ensuring you on are on the Graph-based cluster node by hovering on the circle (Figure 22). If you are not on the correct node, you need to click the circle 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.
- Click only 2 and 3
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.
- Click the circle and select the Normalized counts node which has gene expression data
- Type cd3d in the text field of the drop-down
- Click on CD3D to add it as criteria to select from and use the slider or text field to adjust the selected values. Pin the histogram to visualize the distribution during selection.
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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Filtering cells on the t-SNE scatter plot
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.
- Click (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.
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To revert to the original scaling, click the button again or turn off Fit visible with the toggle.
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- Alternatively, to exclude selected cells, click (filter exclude) (Figure 26)
Additional inclusion or exclusion filters can be added to focus on a smaller subset of cells.
- Click Clear filters to remove applied filters
The plot will update to show all cells and return to the original scaling.
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Classifying cells
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.
- Set Color by in the Style configuration to the normalized counts node
- Type CD79A in the search box and select it. Rotate the 3D plot if you need to see this cluster more clearly.
- Click to activate Lasso mode
- Draw a lasso around the cluster of CD79A-expressing cells (Figure 27)
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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.
- Color by New classifications under Style (Figure 30) while you are still working on the classifications
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To use the classifications in downstream tasks and visualizations, you must first apply them.
- Click Apply classifications
- Name the classification (e.g. Classified Cell Types)
- Click Run to confirm
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.
- Click New plot
- Select Attribute table and the source of data (Figure 32) which in this case is called Classify result
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- The Classification summary table can also be viewed by navigating back to the pipeline and double-clicking the Classify result node (Figure 33)
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- Click on the Classify result node in the analysis pipeline
- Navigate to the Compute biomarkers task under Statistics in the task menu
- Follow the task dialogue and click Finish (Figure 34)
- Double click the Biomarkers node to view the Biomarkers results
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Comparing gene expression between cell types
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).
- Click the Normalized counts results node
- Click Statistics in the toolbox
- Click Differential Analysis
- Select GSA as the Method to use for differential analysis
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.
- Click NK cells in the top panel
The top panel is the numerator for fold-change calculations so the experimental or test groups should be selected in the top panel.
- Click all the other classifications in the bottom panel
The bottom panel is the denominator for fold-change calculations so the control group should be selected in the bottom panel.
- Click Add comparison
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.
- Click for KLRD1
The 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.
- Click GSA report to return to the table
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.
- Click FDR step up and click the arrow next to it
- Set to 1e-8
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.
- Click Fold change and click the arrow next to it
- Set to -2 to 2
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.
- Click 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.
Generating a heatmap
Once we have filtered to a list of significantly different genes, we can visualize these genes by generating a heatmap.
- Click the Feature list data node produced by the Differential analysis filter
- Click Exploratory analysis in the toolbox
- Click Hierarchical clustering / 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.
- Click Assign order under Cell order
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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- Double-click the Hierarchical cluster task node to open the task report
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.
- Click Heatmap
- Toggle on the Range Min and set to -2
- Toggle on the Range Max and set to 2
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.
- Choose Classifications from the Annotations drop-down menu
- Change the Annotation font size under Style in the Annotations section
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.
- Click Transposed under Data to flip the axes
- Toggle off the Row labels under Axes to remove the sample labels
We can also customize the colors of the plot. Do this by clicking the Legend or Heatmap
- Click the blue box on the Color Palette and set it to teal (#3affe6)
- Click the middle box and set it to black
- Click the red box and set it to yellow (#faff00)
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.
Performing enrichment analysis
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.
- Click the Feature list data node produced by the Differential analysis filter
- Click Biological interpretation
- Click Gene set enrichment
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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- Double-click the Gene set enrichment task node to open the task report
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.
(Link), performing Geneset enrichment analysis and motif detection.
Pipeline
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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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