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Multiple comparison test, abbreviated as MCT, is a post hoc analysis generally performed after comparing multiple samples with one or more tests. An MCT will help identify a significantly different sample among multiple samples or a factor among multiple factors.
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Inductively coupled plasma–mass spectrometry (ICP–MS) is a highly selective and sensitive technique for accurate elemental analysis. Though the analysis of ICP–MS mass spectra is comparatively straightforward, it is affected by spectroscopic and non-spectroscopic interferences. Spectroscopic interferences arise when the plasma contains ionic species with an m/z value the same as the analyte ion. Spectroscopic interference can be categorized as isobaric, polyatomic ions, and...
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Multiple two-sample testing under arbitrary covariance dependency with an application in imaging mass spectrometry.

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This summary is machine-generated.

This study introduces a new statistical method for analyzing imaging mass spectrometry (IMS) data in high-dimensional settings. The approach accurately estimates false discovery proportion (FDP) for molecular mass associations with cancer subtypes.

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Area of Science:

  • High-dimensional statistical inference
  • Bioinformatics
  • Oncology

Background:

  • Large-scale hypothesis testing is crucial in fields like imaging mass spectrometry (IMS) association studies.
  • Identifying molecular masses linked to phenotypes (e.g., cancer subtypes) requires simultaneous testing.
  • Mass spectra from matrix-assisted laser desorption/ionization (MALDI) experiments exhibit statistical dependencies.

Purpose of the Study:

  • To develop a robust statistical procedure for evaluating associations between binary outcomes and multiple predictors from MALDI measurements.
  • To address the challenge of false discovery proportion (FDP) estimation and control under arbitrary dependency structures in high-dimensional data.
  • To apply the proposed method to real-world oncological MALDI IMS data.

Main Methods:

  • Utilizing the correlation matrix of test statistics derived from multiple marginal models.
  • Fitting individual marginal logistic regression models for each predictor.
  • Establishing asymptotic joint normality of regression coefficients and estimating their limiting correlation matrix.
  • Extracting common factors from the empirical correlation matrix to refine FDP estimation.

Main Results:

  • A novel inference procedure is proposed for high-dimensional data analysis.
  • The method effectively estimates the realized false discovery proportion (FDP) for marginal p-values.
  • The workflow demonstrates practical utility in an oncological MALDI IMS context.

Conclusions:

  • The developed method provides a reliable approach for hypothesis testing in dependent, high-dimensional IMS data.
  • Accurate FDP control is achieved by incorporating the correlation structure of test statistics.
  • This work advances statistical methodologies for biomarker discovery in cancer research using MALDI IMS.