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The resolution of a mass spectrometer depends on the efficiency of separating ions with different ion masses. The mass of an atom is approximated to the sum of the masses of protons and neutrons inside, considering the masses of protons and neutrons as equal. However, the masses of the proton (1.6726 × 10−24 g) and neutron (1.6749 × 10−24 g) are not truly equal. There is a minor error in the expression of atomic masses relative to the simplest atom of hydrogen. For...
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Tandem mass spectrometry is a technique that uses multiple mass analyzers in series to obtain a higher selectivity and reduce chemical noise during analyte detection. Instruments with multiple analyzers separated by an interaction cell enable secondary fragmentation and selected study of the fragment ions.Secondary fragmentations occur in the interaction cell and can be induced by various factors. Fragmentation induced by collision with inert gases, such as N2, Ar, He, etc., is called...
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Structure-preserving multivariate hypothesis testing for mass spectrometry imaging and single-cell data.

Keziah E Liebenberg1, Erin Craig2,3, Robert Tibshirani3

  • 1Department of Surgery, Baylor College of Medicine, Houston, TX 77030, United States.

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|March 21, 2026
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Summary
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A new statistical method, block-SAM, improves analysis of mass spectrometry imaging (MSI) and single-cell RNA sequencing (scRNA-seq) data. It reduces false discoveries by accounting for sample structure, yielding more reliable biological insights.

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Area of Science:

  • Computational biology
  • Bioinformatics
  • Statistical genomics

Background:

  • Mass spectrometry imaging (MSI) and single-cell RNA sequencing (scRNA-seq) provide high-resolution views of biological samples.
  • Conventional statistical methods often ignore the nested structure of data from pixels or cells within samples, leading to inflated sample sizes and false discoveries.
  • Averaging data at the pixel or cell level sacrifices crucial spatial or cellular resolution.

Purpose of the Study:

  • To introduce and evaluate block-SAM, a novel statistical approach designed to address the limitations of traditional methods in analyzing complex biological datasets.
  • To demonstrate the efficacy of block-SAM in reducing false positives while preserving biologically relevant signals in MSI and scRNA-seq data.

Main Methods:

  • Evaluation of block-SAM using simulated datasets.
  • Application of block-SAM to real-world DESI-MSI data from kidney, lung, and ovarian tumors.
  • Analysis of a metastatic renal cell carcinoma (RCC) scRNA-seq dataset comparing immune checkpoint blockade (ICB)-treated and untreated patients.

Main Results:

  • Block-SAM consistently identified fewer, more reliable differential features compared to traditional SAM across diverse datasets.
  • In kidney tumor MSI data, block-SAM reduced features from 569 to 186, excluding 383 likely false positives.
  • In an RCC scRNA-seq dataset, block-SAM reduced differentially expressed genes from over 19,000 to 19, demonstrating significant reduction in false discoveries.

Conclusions:

  • Block-SAM effectively mitigates false discoveries inherent in conventional statistical analyses of MSI and scRNA-seq data.
  • The method retains biologically meaningful signals, offering improved accuracy and interpretability for high-dimensional biological data.
  • Block-SAM represents a significant advancement for analyzing complex, nested biological data structures.