This option is only available when Cufflinks quantification node is selected. Detailed implementation information can be found in the Cuffdiff manual [15].

When the task is selected, the dialog will display all the categorical attributes more than one subgroups (Figure 1).

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• Class-fpkm: library size factor is set to 1, no scaling applied to FPKM values

• Geometric: FPKM are scaled via the median of the geometric means of the fragment counts across all libraries [26]. This is the default option (and is identical to the one used by DESeq)

• Quartile: FPKMs are scaled via the ratio of the 75 quartile fragment counts to the average 75 quartile value across all libraries

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The report of the cuffdiff task is a table of a feature list p-values, q-value and log2 fold-change information for all the comparisons (Figure 20).

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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

References

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

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