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- Select the Normalized counts data node
- Select Statistical analysis from the task menu
- Select Detect differential expression (GSA) from the Statistical analysis section of the task menu (Figure 1)
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SubtitleText | Invoking GSA from the task menu |
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AnchorName | Invoking GSA |
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The Include attributes page shows all available attributes for analysis (Figure 2). Here, we only have one attribute, Treatment.
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SubtitleText | Selecting attributes to include |
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AnchorName | Include attributes |
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- Select Next to continue with Treatment as the selected attribute
The Comparison selector page will open (Figure 3).
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SubtitleText | The Comparison selector allows multiple comparisons to be designed and added |
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AnchorName | Comparison selector |
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It is easiest to think about comparisons as the questions we are asking. In this case, we want to know what are the differentially expressed genes between untreated and treated cells. We can ask this for each dose individually and for both collectively.
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- Select 5μM in the upper box
- Select 0μM in the lower box
- Select Add comparison to add 5μM vs. 0μM to the comparison table (Figure 4)
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SubtitleText | Designing a comparison to add |
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AnchorName | Adding comparisons |
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- Repeat to create comparisons for 10μM vs. 0μM and 5μM:10μM vs. 0μM (Figure 5)
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SubtitleText | Comparisons for 5uM vs. 0uM, 10uM vs. 0uM, and 5uM:10uM vs. 0uM have been added |
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AnchorName | Comparisons set up |
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By default, the GSA will use a lognormal with shrinkage model for its analysis of variance of each gene. This is appropriate for most data sets and will tend to give accurate and reproducible results. We will use the default settings under Advanced Options in this tutorial. To learn more about the GSA, please see the Differential Gene Expression - GSA user guide.
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A Gene analysis task node and a Feature list data node will be added to the pipeline (Figure 6).
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SubtitleText | Gene analysis task node and Feature list data nodes |
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AnchorName | Gene analysis task node |
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