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| SubtitleText | Choosing attributes to include in the statistical test by selecting the corresponding check button |
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| AnchorName | Attribute selection |
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Click Next to display the levels of each attribute to be selected for sub-group comparisons (contrasts).
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| SubtitleText | Specifying attribute levels for sub-group comparisons (contrast): Select A for Cell type on the top, B for Cell type on the bottom, and click Add comparison to compare A vs B |
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| AnchorName | Subgroup attribute selection |
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To compare Time point 5 vs. 0, select 5 for Time on the top, 0 for Time on the bottom, and click Add comparison (Figure 3).
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| SubtitleText | Specifying attribute levels for sub-group comparisons (contrast): Select 5 for Time on the top, 0 for Time on the bottom, click Add comparison to compare 5 vs 0 |
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| AnchorName | Subgroup Attribute comparison |
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To compare cell types at a certain time point, e.g. time point 5, select A and 5 on the top, and B and 5 on the bottom. Thereafter click Add comparison (Figure 4).
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| SubtitleText | Specifying attribute levels for subgroup comparisons (contrast): Select A and 5 on the top, B and 5 on the bottom, click Add comparison to compare A*5 vs B*5 |
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| AnchorName | Subgroup attribute comparrison contrast |
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Multiple comparisons can be computed in one GSA run; Figure 5 shows the above three comparisons are added in the computation.
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| SubtitleText | Three comparisons included in GSA computation: A vs B; 5 vs 0; and A*5 vs B*5 |
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| AnchorName | Comparison table |
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In terms of design pool, i.e. choices of model designs to select from, two 2 factors in this example data will lead to seven possibilities in the design pool:
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| SubtitleText | Applying default normalization if differential gene detection dialog is invoked from a quantification output data node (see text for details) |
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| AnchorName | Default normalization |
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If advanced normalization needs to be applied, perform the Normalize counts task on a quantification data node before doing differential expression detection (GSA or ANOVA).
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| SubtitleText | Configuring advanced GSA options |
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| AnchorName | GSA advanced options |
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Low-expression feature
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| SubtitleText | Five response distribution types for each design model |
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| AnchorName | Design model distribution types |
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If multiple distribution types are selected, then the number of total models that is evaluated for each feature is the product of the number of design models and the number of distribution types. In the above example, suppose we have only compared A vs B in Cell type as in Figure 2, then the design model pool will have the following three models:
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| SubtitleText | Pie charts of proportion of genes using each model and distribution in gene-specific analysis calculation |
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| AnchorName | Gene proportion |
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Feature list with p-value and fold change generated from the best model selected is displayed in a table with other statistical information (Figure 10).
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| SubtitleText | Feature list on the gene-specific analysis result. Clicking on the column header sorts the table. Panel on the left filters the table |
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| AnchorName | Feature list |
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The following information is included in the table by default:
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On the right of each contrast header, there is volcano plot icon ( 
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| SubtitleText | Volcano plot in comparison A vs B. X-axis represents fold change (linear scale), Y-axis represents negative logged p-value (unadjusted), each dot is a feature. The horizontal line represents p-value of 0.05, two vertical lines represent fold change of -2 and 2. Lower left corner displays number of features passing the fold-change and p-value criteria |
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| AnchorName | Volcano plot |
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Feature list filter panel is on the left of the table (Figure 12). Click on the black triangle ( ) to collapse and expand the panel.
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| SubtitleText | Feature list filter panel |
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| AnchorName | Feature list filter panel |
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The filtered result can be saved into a filtered data node by selecting the Generate list button at the lower-left corner of the table ( ). Selecting the Download button at the lower-right corner of the table downloads the table as a text file to the local computer.
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| SubtitleText | Shrinkage plot generated on longnormal with shrinkage model. X-axis is represents average coverage in log2 scale; Y-axis represents log2 standard deviation of error term. Green dot represents standard deviation of residual error obtained from lognormal linear model on a gene; black line represents the trend how the errors change depending on the average gene expression; red dot represents adjusted (shrunk) standard deviation of error on a gene |
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| AnchorName | Shrinkage plot |
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References
- Benjamini, Y., Hochberg, Y. (1995). Controlling the false discovery rate: a practical and powerful approach to multiple testing, JRSS, B, 57, 289-300.
- Storey JD. (2003) The positive false discovery rate: A Bayesian interpretation and the q-value. Annals of Statistics, 31: 2013-2035.
- Auer, 2011, A two-stage Poisson model for testing RNA-Seq
- Burnham, Anderson, 2010, Model selection and multimodel inference
- Law C, Voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biology, 2014 15:R29.
- http://cole-trapnell-lab.github.io/cufflinks/cuffdiff/index.html#cuffdiff-output-files
- Anders S, Huber W: Differential expression analysis for sequence count data. Genome Biology, 2010
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