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Werner Heisenberg considered the limits of how accurately one can measure properties of an electron or other microscopic particles. He determined that there is a fundamental limit to how accurately one can measure both a particle’s position and its momentum simultaneously. The more accurate the measurement of the momentum of a particle is known, the less accurate the position at that time is known and vice versa. This is what is now called the Heisenberg uncertainty principle. He...
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Counting is the type of measurement that is free from uncertainty, provided the number of objects being counted does not change during the process. Such measurements result in exact numbers. By counting the eggs in a carton, for instance, one can determine exactly how many eggs are there in the carton. Similarly, the numbers of defined quantities are also exact. For example, 1 foot is exactly 12 inches, 1 inch is exactly 2.54 centimeters, and 1 gram is exactly 0.001 kilograms. Quantities...
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The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor...
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Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value. 
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Sampling uncertainty versus method uncertainty: A general framework with applications to omics biomarker selection.

Simon Klau1, Marie-Laure Martin-Magniette2,3,4, Anne-Laure Boulesteix1

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Statistical uncertainty in omics research is high, especially sampling uncertainty from different samples. Method uncertainty from varied analysis strategies also impacts biomarker discovery, requiring careful consideration.

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Area of Science:

  • Statistics
  • Bioinformatics
  • Genomics

Background:

  • Uncertainty is a critical statistical concept with implications in scientific research.
  • Two main types of uncertainty exist: sampling uncertainty (variability across samples) and method uncertainty (variability across analysis strategies).
  • Omics research heavily relies on identifying molecular biomarkers, often involving complex analytical choices.

Purpose of the Study:

  • To introduce a general resampling-based framework for quantifying and comparing sampling and method uncertainty.
  • To apply this framework to biomarker selection and ranking in omics data.
  • To assess the impact of different analysis strategies on uncertainty in omics research.

Main Methods:

  • Developed a resampling-based framework to quantify uncertainty.
  • Applied the framework to scenarios in acute myeloid leukemia omics data.
  • Evaluated variable selection, biomarker ranking, and differential gene expression analysis.

Main Results:

  • Findings indicate highly unstable results when applying the same analysis strategy to independent samples, highlighting significant sampling uncertainty.
  • Method uncertainty was comparatively smaller but non-negligible.
  • The degree of method uncertainty was dependent on the specific methods compared.

Conclusions:

  • Omics biomarker discovery is susceptible to substantial sampling uncertainty.
  • Method uncertainty, while smaller, significantly influences results and depends on chosen analytical methods.
  • The developed framework aids in understanding and managing uncertainty in omics research.