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Calibration Curves: Correlation Coefficient

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In a linear calibration curve, there is a value called the calibration coefficient, denoted by 'r,' which measures the strength and the direction of association between two variables. The correlation coefficient value ranges from −1 to +1. A value of +1 indicates a perfect positive linear correlation, −1 denotes a perfect negative correlation, and 0 implies no correlation between the two variables. A positive correlation value establishes that as one variable increases, the other increases, and...

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Related Experiment Video

Updated: Jun 25, 2026

Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements

Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements

Published on: September 7, 2019

Statistical calibration of the SEQUEST XCorr function.

Aaron A Klammer¹, Christopher Y Park, William Stafford Noble

¹Department of Genome Sciences, University of Washington, Seattle, Washington 98195, USA.

Journal of Proteome Research

|March 12, 2009

Summary

Accurate peptide identification in mass spectrometry relies on robust scoring. This study introduces a novel calibration protocol for scoring functions, improving peptide-spectrum match accuracy and eliminating the need for decoy databases.

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High-Resolution Respirometry in a Small-Volume Chamber

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Last Updated: Jun 25, 2026

Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements

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Published on: September 7, 2019

High-Resolution Respirometry in a Small-Volume Chamber

High-Resolution Respirometry in a Small-Volume Chamber

Published on: July 25, 2025

Area of Science:

Proteomics
Mass Spectrometry
Bioinformatics

Background:

Accurate peptide identification in shotgun proteomics liquid chromatography tandem mass spectrometry (LC-MS/MS) is crucial.
Existing scoring functions, like Sequest's Xcorr, struggle to differentiate correct from incorrect peptide-spectrum matches (PSMs) due to spectrum-specific score distributions.
This limitation hinders reliable peptide identification.

Purpose of the Study:

To develop and validate a protocol for calibrating peptide-spectrum match (PSM) scoring functions.
To improve the accuracy and reliability of peptide identifications in LC-MS/MS data.
To provide a method for estimating false discovery rates without relying on decoy databases.

Main Methods:

A novel protocol for calibrating PSM score functions was developed.
The protocol calculates spectrum-specific p-values using individual spectrum score distributions.
The method was applied to Sequest's Xcorr and Sp scoring functions.

Main Results:

The calculated p-values were demonstrated to be uniform under a null distribution, accurately measuring significance.
The p-values effectively estimate the false discovery rate, removing the need for decoy databases.
P-values showed superior discrimination between correct and incorrect PSMs compared to raw scores.

Conclusions:

The developed calibration protocol accurately measures significance and improves PSM discrimination.
This method enhances the reliability of peptide identifications in LC-MS/MS proteomics.
The protocol is broadly applicable to various PSM scoring functions.