Qvalue sparse, complete, and percentile: what's the difference?

Qvalue sparse, complete, and percentile: what's the difference?

Qvalues can be used to filter your data according to the error rate among your accepted entries. For example, if you set a threshold of Qvalue  0.01, you are applying an FDR threshold of 1%.

Biognosys' software uses Qvalues for two different purposes:

1.     For identification confidence, mainly in the Analysis Perspective and in reports.

2.     For differential abundance significance, mainly in the Post Analysis Perspective, under the Differential Abundance node.


If you are looking for information about the Qvalues you see in the Analysis Perspective or about what is considered as confidently identified, keep reading.

Identification confidence Qvalues

Qvalues 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 sparse (default setting): if a peptide precursor was identified as passing the cut‑off in at least one of the samples, a quantitative value will be reported for that precursor in every sample. In samples where it was not identified below the significance threshold (default ≤ 0.01), the values will be imputed under default settings. Alternatively, you can choose to get the best picked signal as the quantitative value. The best picked signal can correspond to the real signal from the precursor, but can also be a background signal (noise).

·    Qvalue: when using this filter only those precursors passing the Qvalue cut‑offs will be reported (considered as quantified) and used for statistical testing of differential abundance. This filter is the only one producing a data matrix containing missing values, tagged as Filtered (Figure 1). Qvalue sparse filtering is the least stringent data filtering strategy. Qvalue sparse and Qvalue filters should produce data matrixes of equal dimensions.

·    Qvalue percentile: this filter 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 Qvalue threshold. For instance, if you set a 50th percentile cut-off, the peptide precursor needs to pass the Qvalue in 50% or more of your samples to be reported.

·    Qvalue complete: the peptide precursor needs to pass the Qvalue threshold in all the samples to be reported. This is the most stringent filter and produces the smallest data matrix.

Figure 1. Example of a data matrix (pivot report) produced with the Qvalue filter

Changing the default Qvalue filter

The default setting in Biognosys' software is the Qvalue Sparse filter. You can change these settings in three ways:

1. Creating a nw 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 directDIA in Spectronaut, see Figure 2). In the BGS Factory Settings (default), click the Quantification node. Under Data Filtering, choose your preferred option from the dropdown menu. Now, save this schema clicking on Save As in the bottom-left corner. Give a name to your schema and click OK.

Figure 2. Changing and saving the Data Filtering options in the Settings Perspective

2. Changing the settings during analysis

When you start an analysis in the Analysis Perspective,  you can change the Data Filtering options in the Experiment Setup (see Figure 3), after loading your raw files. In the Quantification node of analysis settings, you will find the Data Filtering settings where you can choose your preferred option from the dropdown menu. After choosing all the preferred settings, click Finish. Changes to the schema won’t be saved for future analyses.

Figure 3. Changing the Data Filtering options in the Experimental Setup window

3. Recalculating a previosuly analyzed dataset

In the Analysis Perspective, click on the settings icon (Figure 4). At the bottom half of the window, you will find the Analysis Settings (Figure 5). From there you can select the Quantification node and choose your preferred option from the Data Filtering dropdown menu. Click Confirm to start the recalculation.


Figure 4. Applying a new Data Filtering option in a previously analyzed experiment.


Figure 5. Changing the Data Filtering options in the Experiment Setup window

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