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SubtitleText | Location of the Settings link on the main page of Partek Flow |
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AnchorName | Getting to the settings |
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SubtitleText | Tutorial data sets available through Partek Flow |
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AnchorName | Importing tutorial data |
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- Click Single cell glioma (multi-sample)
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SubtitleText | The data tab during tutorial data import |
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AnchorName | Importing tutorial data in progress |
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You can wait a few minutes for the download to complete, or check the download progress by selecting Queue then View queued tasks... to view the Queue (Figure 4).
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SubtitleText | Viewing the queue |
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AnchorName | Viewing the queue |
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Once the download completes, the sample table will appear in the Data Metadata tab, with one row per sample (Figure 5).
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SubtitleText | Sample data table listing the name and the number of cells for each sample |
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AnchorName | Sample data table |
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The sample table is pre-populated with two sample attributes: # Cells and Subtype. Sample attributes can be added and edited manually by clicking Manage in the Sample attributes menu on the left. If a new attribute is added, click Assign values to assign samples to different groups. Alternatively, you can use the Assign values from a file option to assign sample attributes using a tab-delimited text file. For more information about sample attributes, see here.
For this tutorial, we do not need to edit or change any sample attributes.
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SubtitleText | Selecting the Single cell QA/QC task from the task menu |
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AnchorName | Selecting a task |
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A task node, Single cell QA/QC, is produced. Initially, the node will be semi-transparent to indicate that it has been queued, but not completed. A progress bar will appear on the Single cell QA/QC task node to indicate that the task is running.
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SubtitleText | Selecting the task report for any task node opens a report with any tables or charts the task produced |
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AnchorName | Opening task report |
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The Single cell QA/QC report opens in a new data viewer session. There are interactive violin plots showing the most commonly used quality metrics for each cell from all samples combined (Figure 8). For this data set, there are two relevant plots: the total count per cell and the number of detected genes per cell. Each point on the plots is a cell and the violins illustrate the distribution of values for the y-axis metric. Typically, there is a third plot showing the percentage of mitochondrial counts per cell, but mitochondrial transcripts were not included in the data set by the study authors, so this plot is not informative for this data set.
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SubtitleText | Each cell is shown as a point on the plot. Remove the % mitochondrial counts and empty text box using the X icons |
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AnchorName | Single cell QA/QC report |
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The plots are highly customizable and can be used to explore the quality of cells in different samples.
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SubtitleText | Click and drag the Sample name attribute onto the X-axis for each plot |
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AnchorName | Separating cells by sample |
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The cells are now separated into different samples along the x-axis (Figure 10)
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SubtitleText | Counts and detected genes plots can be customized to compare cells from different samples |
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AnchorName | Single cell QAQC samples on x-axis |
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Note how both plots were modified at the same time.
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SubtitleText | Previewing a filter using the Single cell QA/QC violin plots |
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AnchorName | Filtering cells by read counts |
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Because this data set was already filtered by the study authors to include only high-quality cells, this count filter is sufficient.
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SubtitleText | After the Apply filter button is selected, you will be presented with a preview of your pipeline. You need to select the appropriate data node to apply the filtering to |
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AnchorName | Select Single cell count data node as input for filtering task |
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A new task, Filter counts, is added to the Analyses tab. This task produces a new Filter counts data node (Figure 13).
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SubtitleText | Applying a cell quality filter |
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AnchorName | Output of Filter cells |
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Most tasks can be queued up on data nodes that have not yet been generated, so you can wait for filtering step to complete, or proceed to the next section.
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SubtitleText | Invoking Filter features |
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AnchorName | Invoking Filter features |
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There are four categories of filter available - noise reduction, statistics based, feature metadata, and feature list (Figure 15).
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SubtitleText | Viewing the filtering options |
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AnchorName | Filter types |
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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.
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SubtitleText | Configuring a noise reduction filter to exclude genes not expressed in the data set |
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AnchorName | Configuring a noise reduction filter |
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This produces a Filtered counts data node. This will be the starting point for the next stage of analysis - identifying cell types in the data using the interactive t-SNE plot.
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