The q-value is a measure of statistical significance in large-scale data analysis, such as proteome-wide analysis. It is similar to the well known p-value, except it is a measure of significance in terms of the false discovery rate (FDR) rather than the false positive rate.

The false positive rate and FDR are often mistakenly equated, but their difference is actually very important. Given a rule for calling features significant, the false positive rate is the rate that truly null features are called significant. The FDR is the rate that significant features are truly null. For example, a false positive rate of 5% means that on average 5% of the truly null features in the study will be called significant. A FDR of 5% means that among all features called significant, 5% of these are truly null on average.

The q-values can be used to filter your data according to the error rate among your accepted entries. So if you set a threshold of q-value ≤ 0.01, you are applying an FDR threshold of 1%. Be aware that Biognosys' software uses q-values for two different purposes:

- For identification confidence, mainly in the
**Review Perspective**and in the reports. - For differential abundance significance, mainly in the
**Post Analysis Perspective,**under the**Differential Abundance**node**.**

If you are looking for information about the q-values that tells you how significant a difference in abundance is after the statistical testing, keep on reading.

### Differential abundance q-values

**Spectronaut™**

**default settings currently perform Student's t‑tests to assess differential abundances. Then it will apply the multi‑test correction algorithm described by Storey (2002). This q-value is equivalent to the FDR of the comparison. The q-values will be reported on the**

**Post Analysis Perspective**. By default, the candidate list is filtered at a cut-off of q-value ≤ 0.05 and absolute log2 fold change ≥ 0.58, i.e. a fold change ≥ 1.5 or ≤ 0.67.

### Changing the default threshold

You can change both thresholds in two ways, always on the **Post Analysis Perspective** and on the **Differential Abundance node** (figure 1).

**Figure 1. Adjusting the difference abundance significance q-value threshold on the candidate list**

**1.** On the **Candidates** subsection, you can see a table with the candidate list. Look for the q-value column and change the filter by directly clicking on the field and writing your preferred threshold (figure 1).

**2.** On the **Volcano Plot** subsection, you will see a volcano plot with the candidates marked in red. The dashed lines represent the thresholds applied and on the Y-axis, you find the q-value expressed as -Log10(q-value). You can manually move these thresholds by dragging them with the mouse (figure 2).

**Figure 2. Adjusting the difference abundance significance q-value threshold on the volcano plot**

Please note that these two elements (the candidate table and the volcano plot) are connected. This means that, if you change either the q-value threshold or the fold change threshold on one, the other one will adapt accordingly.

To read about the q-value for peptide and protein identification, click here. To learn how Spectronaut calculates the candidates, read here.

**References:**

Storey JD (2002) A direct approach to false discovery rates. *J R Stat Soc Ser B Statistical Methodol* 64:479–498.

**Related content:**

*How are the candidates in the Post Analysis Perspective assessed?*

*What does the q-value for peptide and protein mean?*

*How does Spectronaut™ calculate the quantitative values?*

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*Created by SEZ. Last update 2018-03-14 by SEZ*