OptiMissP: a dashboard to assess missingness in proteomic data-independent acquisition mass spectrometry

Angelica Arioli, Arianna Dagliati, Bethany Geary, Niels Peek, Philip A Kalra, Anthony D Whetton, Nophar Geifman

Research output: Contribution to journalArticlepeer-review


BACKGROUND: Missing values are a key issue in the statistical analysis of proteomic data. Defining the strategy to address missing values is a complex task in each study, potentially affecting the quality of statistical analyses.

RESULTS: We have developed OptiMissP, a dashboard to visually and qualitatively evaluate missingness and guide decision making in the handling of missing values in proteomics studies that use data-independent acquisition mass spectrometry. It provides a set of visual tools to retrieve information about missingness through protein densities and topology-based approaches, and facilitates exploration of different imputation methods and missingness thresholds.

CONCLUSIONS: OptiMissP provides support for researchers' and clinicians' qualitative assessment of missingness in proteomic datasets in order to define study-specific strategies for the handling of missing values. OptiMissP considers biases in protein distributions related to the choice of imputation method and helps analysts to balance the information loss caused by low missingness thresholds and the noise introduced by selecting high missingness thresholds. This is complemented by topological data analysis which provides additional insight to the structure of the data and their missingness. We use an example in Chronic Kidney Disease to illustrate the main functionalities of OptiMissP.

Original languageEnglish
Article numbere0249771
JournalPL o S One
Issue number4
Publication statusPublished - 15 Apr 2021


  • Bias
  • Computer Simulation
  • Data Interpretation, Statistical
  • Humans
  • Mass Spectrometry/methods
  • Models, Statistical
  • Proteomics/methods
  • Software

Research Beacons, Institutes and Platforms

  • Lydia Becker Institute


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