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Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
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Addressing Sample Mix-Ups: Tools and Approaches for Large-Scale Multi-Omics Studies.

Yingxue Fu1, Zuo-Fei Yuan1, Long Wu1

  • 1Center for Proteomics and Metabolomics, St. Jude Children's Research Hospital, Memphis, Tennessee, USA.

Proteomics
|December 11, 2024
PubMed
Summary

Sample mix-ups are a major problem in large multi-omics studies. This review covers tools for detecting and correcting these errors, especially in proteomics, to ensure reliable biomedical research.

Keywords:
bioinformaticsmulti‐omicsproteogenomicsproteomicssample mix‐ups

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Area of Science:

  • Biotechnology
  • Genomics
  • Proteomics

Background:

  • High-throughput omics technologies enable multi-level biological sample analysis (genome, transcriptome, proteome).
  • Increasing sample sizes in large-scale studies lead to prevalent sample mix-ups, compromising data integrity and conclusions.

Purpose of the Study:

  • Review methodologies and tools for detecting and correcting sample mix-ups in multi-omics data.
  • Focus on approaches applicable to proteomics data and the proteogenomics approach.

Main Methods:

  • Categorization of existing tools into three groups: expression/protein quantitative trait loci-based, genotype concordance-based, and gene/protein expression correlation-based.
  • Evaluation of tools for sample mix-up detection and correction.

Main Results:

  • Few tools currently use the proteogenomics approach for proteomics-level sample mix-up correction.
  • Existing tools can be integrated to develop more versatile solutions.

Conclusions:

  • Verifying sample identity is crucial for reducing bias and increasing precision in multi-omics analyses.
  • Utilizing these tools enhances the reliability and reproducibility of biomedical research.