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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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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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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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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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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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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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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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The following information is included in the table by default:
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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 (
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Differential gene expression (ANOVA)
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When more than one factor is selected, Add interaction button will be enabled to allow you to specify interaction.
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When more than one factor is added to the model, click on the Cross tabulation link at the bottom to view the relationship between the factors (Figure 15).
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Once the model is set, click on Next button to setup comparisons (contrasts) (Figure 16).
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Low-expression feature and Multiple test correction sections are the same as the matching GSA advanced option, see above GSA advanced options
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The table can be downloaded as a text file when clicking the Download button on the lower-right corner of the table.
References
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- Benjamini, Y., Hochberg, Y. (1995). Controlling the false discovery rate: a practical
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- and powerful approach to multiple testing, JRSS, B, 57, 289-300.
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- Storey JD. (2003) The positive false discovery rate: A Bayesian interpretation and
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- the q-value. Annals of Statistics, 31: 2013-2035.
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- Auer, 2011, A two-stage Poisson model for testing RNA-Seq
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- Burnham, Anderson, 2010, Model selection and multimodel inference
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- Law C, Voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biology, 2014 15:R29.
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- Anders S, Huber W: Differential expression analysis for sequence count data. Genome Biology, 2010
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