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 you see on the Review Perspective or about what is considered as confidently quantified, keep on reading.
Identification confidence q-values
Q-values for precursor and protein identification guarantee that only high‑quality data is used for quantification. Further, they can be combined with four modes of data filtering:
- Qvalue (default): only those precursors passing the q-value cut‑offs will be reported (considered as quantified) and, accordingly, used for statistical testing of differential abundance. This filter is the only one producing a data matrix containing missing values, tagged as Filtered or NaN (figure 1).
- Qvalue sparse: if a peptide precursor was identified passing the cut‑off in at least one of the samples, it will be reported for all the samples. In the samples where it was not identified below the significance threshold (default ≤ 0.01) the best picked signal will be reported. This value can correspond to the real signal or to noise, and can be considered similar to an imputation. Qvalue sparse filtering is less stringent than q-value filtering. Qvalue sparse and Qvalue filters should produce data matrices of equal dimensions.
- Qvalue percentile: this is a modified version of the Qvalue sparse in which you define in how many of your samples the peptide precursor needs to pass the q-value threshold. For instance, if you set a 50th percentile cutoff, the peptide precursor needs to pass the q-value in 50% or more of your samples to be reported
- Qvalue complete: the peptide precursor needs to pass the q-value threshold in all the samples to be reported. This is the most stringent filter and produces the smallest data matrix.
Figure 1. Example of data matrix (pivot report) produced with q-value filter
Changing the default q-value filter
The default setting in Biognosys software is q-value filter. You can change these settings in three ways:
1. Creating a new predefined DIA analysis schema with your preferred data filtering option.
Open the software and go to the Settings Perspective and to the Analysis page (DIA Analysis or directDIATM in SpectronautTM, see figure 2). On the BGS Factory Settings (default), click on the Quantification node. In Data Filtering, choose the option you prefer from the drop-down menu. Now, save this schema clicking on Save As at the bottom left corner. Give a name to your schema and click OK.
Figure 2. Changing and saving the Data Filtering options on the Settings Perspective
2. Changing the settings on the go.
When you start an analysis on the Review Perspective, after loading your raw files, you can change the Data Filtering options on the Experiment Setup (see figure 3). At the bottom half of the window, you can see the Analysis Settings. In the Quantification node, you will find the Data Filtering settings where you can choose your preferred option from the drop-down menu. After assigning a library to your runs, click start. In this case, the changes in the schema won’t be saved for future analyses.
Figure 3. Changing the Data Filtering options on the Experimental Setup window
3. Recalculating an already analyzed data-set.
On the Review Perspective, right click on the experiment tab and choose Settings (figure 4). At the bottom half of the window, you can see the Analysis Settings (figure 5). In the Quantification node, you will find the Data Filtering settings where you can choose your preferred option from the drop-down menu. Click OK to start the recalculation.
Figure 4. Applying a new Data Filtering option on an already analyzed experiment
Figure 5. Changing the Data Filtering options on the Experiment Setup window
To learn about the q-value used for differential abundance, keep on reading here.
Created by SEZ. Last update 2018-03-14 by SEZ