### Description
Implement temporal quality degradation analysis to monitor and visualise changes in data quality across acquisition time in large-scale proteomics studies. This feature would help users identify systematic drifts or sudden changes in instrument performance over the course of an experiment.
Motivation
For large-scale studies where samples are acquired over extended periods (days, weeks, or months), it is critical to ensure that there is no degradation in quality as runs are acquired over time. Early detection of temporal quality issues can help users identify instrument maintenance needs, chromatography column degradation, or other systematic problems before they compromise an entire study. This feature was suggested during peer review of the pmultiqc manuscript and aligns with approaches used in tools such as MSstatsQC.
Proposed metrics to track over time
Identified peptides/proteins per run
MS1/MS2 signal intensity distributions
Retention time stability
Mass accuracy drift
Peak width trends
Identification rates
Implementation considerations
⚠️ Important: This feature will likely require access to raw MS files (e.g., .raw, .mzML) to extract acquisition timestamps and certain instrument-level metrics, which is currently outside pmultiqc's scope. We should discuss:
Whether to implement this as an optional module with raw file parsing dependencies
Integration with existing raw file readers (e.g., pyteomics, pymzml)
Alternative approaches using search engine output timestamps where available
References
MSstatsQC: https://www.bioconductor.org/packages/release/bioc/html/MSstatsQC.html
Metric Description
In the main issue
Calculation Method
In the main issue.
Reference Manuscript
No response
Expected Values
Range
Visualization Preference
No response
Use Cases
No response
Priority
Medium - Would be helpful
Additional Context
No response
Code of Conduct
### Description
Implement temporal quality degradation analysis to monitor and visualise changes in data quality across acquisition time in large-scale proteomics studies. This feature would help users identify systematic drifts or sudden changes in instrument performance over the course of an experiment.
Motivation
For large-scale studies where samples are acquired over extended periods (days, weeks, or months), it is critical to ensure that there is no degradation in quality as runs are acquired over time. Early detection of temporal quality issues can help users identify instrument maintenance needs, chromatography column degradation, or other systematic problems before they compromise an entire study. This feature was suggested during peer review of the pmultiqc manuscript and aligns with approaches used in tools such as MSstatsQC.
Proposed metrics to track over time
Identified peptides/proteins per run
MS1/MS2 signal intensity distributions
Retention time stability
Mass accuracy drift
Peak width trends
Identification rates
Implementation considerations
Whether to implement this as an optional module with raw file parsing dependencies
Integration with existing raw file readers (e.g., pyteomics, pymzml)
Alternative approaches using search engine output timestamps where available
References
MSstatsQC: https://www.bioconductor.org/packages/release/bioc/html/MSstatsQC.html
Metric Description
In the main issue
Calculation Method
In the main issue.
Reference Manuscript
No response
Expected Values
Range
Visualization Preference
No response
Use Cases
No response
Priority
Medium - Would be helpful
Additional Context
No response
Code of Conduct