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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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Low-expression feature

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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.

Select the check box of the field and specify the cutoff, and press Enter to apply. After the filter has been applied, the total number of included features will be updated on the top of the panel (Result).

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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Start by choosing a factor or interaction from the Factor drop-down list. The subgroups of the factor or interaction will be displayed in the left panel; click to select a subgroup name and move it to one of the panels on the right. The fold change calculation on the comparison will use the group in the top panel as numerator, and the group in the bottom panel as the denominator. Click on Add comparison button to add one comparison to the comparisons table. Note that multiple comparisons can be added to the specified model.

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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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There are three library normalization methods:

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1. Benjamini, Y., Hochberg, Y. (1995). Controlling the false discovery rate: a practical and powerful approach to multiple testing, JRSS, B, 57, 289-300.
2. Storey JD. (2003) The positive false discovery rate: A Bayesian interpretation and the q-value. Annals of Statistics, 31: 2013-2035.
3. Auer, 2011, A two-stage Poisson model for testing RNA-Seq
4. Burnham, Anderson, 2010, Model selection and multimodel inference
5. Law C, Voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biology, 2014 15:R29.
6. http://cole-trapnell-lab.github.io/cufflinks/cuffdiff/index.html#cuffdiff-output-files
7. Anders S, Huber W: Differential expression analysis for sequence count data. Genome Biology, 2010