Calculate p-values and adjusted p-values for quantification results - Proteome Discoverer Software - Calculate p-values and adjusted p-values for quantification results - Proteome Discoverer Software - Proteome Discoverer Software

After the analyses are complete, a final report presents the list of peptide groups and proteins with scaled abundances and selected ratios. The Proteome Discoverer application includes a feature for assessing the significance of differential expression by providing p-values and adjusted p-values for those ratios selected on the Grouping and Quantification page prior to the analysis.

The p-value displayed for these selected ratios for a given peptide group or protein is a measurement of how likely the abundance is unchanged between samples.

• A larger p-value indicates that the abundances are the same.
• A lower p-value indicates that the abundances are more likely to be significantly different.

A p-value threshold of 0.05 or 0.01 is typically applied as a threshold to determine whether a given peptide group or protein has significantly different abundances. The probability of a false-positive detection for one or more of the ratios increases with an increasing number of measurements. This error rate is managed by adjusting the p-value to lower significance based on the number of measurements using a correction such as the Benjamini-Hochberg method.

There are four different methods to calculate p-values for selected ratios:

• t-test (Background Based): To use this method, select it in the Precursor Ions Quantifier or Reporter Ions Quantifier nodes in the consensus workflow. This method uses the background population of ratios for all peptides and proteins to determine whether any given a single peptide or protein is significantly changing relative to that background.
• This method does not require that a given sample group contains replicates, but it does require that most of the protein abundances are unchanged between samples. If this is not the case or if there are fewer than 500 protein IDs in the sample, you must use the t-test (Background Based) method.
• To set up replicates samples, see Calculate p-values for replicate data by using biological replicate study factors. The ANOVA (Individual Proteins) method also calculates an adjusted p-value using the Benjamini-Hochberg method.
• ANOVA (Individual Proteins): This method performs an ANOVA for all conditions (or a t-test when there are only two conditions) and requires that there are replicates associated with the study factors used to create the ratio. This method also calculates an adjusted p-value using the Benjamini-Hochberg method. Minimum of three (biological) replicates are required in each condition of the ratio to calculate a p-value.
• Biological Replicates study factor using a Non-Nested Design: When the biological replicates are set up using a non-nested (or unpaired) design, the application uses either the t-test (Background Based) or the ANOVA (Individual Proteins) method to calculate the p-values and adjusted p-values. To set up biological replicate study factors for a non-nested design, see Calculate p-values for replicate data by using biological replicate study factors.
• Biological replicates study factors using a Nested Design: For a nested or paired design, the biological replicates are set up such that the measured effect is observed for the same individual or sample under multiple conditions. The application uses either the Paired (Background Based) or the ANOVA (Individual Proteins) method to calculate the p-values and adjusted p-values. To set up a nested design experiment using biological replicate study factors, see Calculate p-values for replicate data without using biological replicate study factors.

For more information on the Grouping & Quantification page, see Working with grouping and quantification.