You don't have to apply fold-change filtering to detect significant differentially expressed genes - it may be interesting to do so, but you don't necessarily have to.
CLC Main Workbench version 7.8 manual has a section about "Microarray-based Expression Analysis" (chapter 4, pages 120-136), and a section "Expression analysis" (chapter 26, pages 602-655). For CLC Main Workbench version 8.0.1 manual, there is only the "Expression analysis" section (chapter 23, pages 467-524). The manual is quite detailed in both cases, if you plan to stick to CLC Main Workbench, it is worth reading it. Both manuals have a paragraph about which column to choose in case you want to filter on fold-change:
Note! It is very common to filter features on fold change values in expression analysis and fold change values are also used in volcano
plots, see section 23.5.5. There are different definitions of 'Fold
Change' in the literature. The definition that is used typically
depends on the original scale of the data that is analyzed. For data
whose original scale is not the log scale the standard definition is
the ratio of the group means [Tusher et al., 2001]. This is the value
you find in the 'Fold Change' column of the experiment. However, for
data whose original is the log scale, the difference of the mean
expression levels is sometimes referred to as the fold change [Guo et
al., 2006], and if you want to filter on fold change for these data
you should filter on the values in the 'Difference' column.
As we don't the scale of your data, we can't say if your choice is correct or not.
By the way, CLC used to have an outstanding customer support (at least before being acquired by Qiagen). If you are a paying customer, I recommend you contact customer support.
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