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Related Concept Videos

Confidence Intervals01:21

Confidence Intervals

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An unbiased point estimate is often insufficient to predict a population estimate, such as population mean or population proportion. In this scenario, a confidence interval is used. A confidence interval is an estimate similar to a  sample proportion. However, unlike the point estimate which is a single value, the confidence interval  contains a range of values. These values have lower and upper limits, known as confidence limits, and can be designated as L1 and L2, respectively.
A...
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Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

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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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Interpretation of Confidence Intervals01:19

Interpretation of Confidence Intervals

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A confidence interval is a better estimate of the population than a point estimate, as it uses a range of values from a sample instead of a single value.
Confidence intervals have confidence coefficients that are crucial for their interpretation. The most common confidence coefficients are 0.90, 0.95, and 0.99, which can be written as percentages–90%, 95%, and 99%, respectively.
Suppose a person calculates a confidence interval with a confidence coefficient of 0.95. In that case, they can...
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Confidence Interval for Estimating Population Mean01:25

Confidence Interval for Estimating Population Mean

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A point estimate of the population mean is obtained from a single sample. Such a point estimate does not represent a population well because it needs to account for variability in the population. Single point estimate can also be biased despite the sample being selected randomly. Thus, a point estimate is often unreliable. A confidence interval is needed to reduce this unreliability.
A confidence interval for the mean is a range of values that provides an estimate of the population mean. As the...
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Statistical Significance01:50

Statistical Significance

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Once data is collected from both the experimental and the control groups, a statistical analysis is conducted to find out if there are meaningful differences between the two groups. A statistical analysis determines how likely any difference found is due to chance (and thus not meaningful). In psychology, group differences are considered meaningful, or significant, if the odds that these differences occurred by chance alone are 5 percent or less. Stated another way, if we repeated this...
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Confidence Coefficient01:24

Confidence Coefficient

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The confidence coefficient is also known as the confidence level or degree of confidence. It is the percent expression for the probability, 1-α, that the confidence interval contains the true population parameter assuming that the confidence interval is obtained after sufficient unbiased sampling; for example, if the CL = 90%, then in 90 out of 100 samples the interval estimate will enclose the true population parameter. Here α is the area under the curve, distributed equally under...
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Quantification of Proteins Using Peptide Immunoaffinity Enrichment Coupled with Mass Spectrometry
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Bayesian Confidence Intervals for Multiplexed Proteomics Integrate Ion-statistics with Peptide Quantification

Leonid Peshkin1, Meera Gupta2, Lillia Ryazanova2

  • 1Department of Systems Biology, Harvard Medical School, Boston, MA 02115.

Molecular & Cellular Proteomics : MCP
|July 18, 2019
PubMed
Summary

This study introduces BACIQ, a novel statistical method for multiplexed proteomics. BACIQ rigorously estimates protein ratios and confidence, improving data interpretation and resource prioritization in quantitative proteomics experiments.

Keywords:
AlgorithmsBiostatisticsCell biologyCellular organellesMass SpectrometryMultiplexed ProteomicsNuclear TranslocationQuantificationStatisticsSubcellular analysisTMTTandem Mass SpectrometryXenopus

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Area of Science:

  • Proteomics
  • Bioinformatics
  • Quantitative Biology

Background:

  • Multiplexed proteomics measures relative protein expression using isobaric tags.
  • Current methods average peptide ratios, lacking confidence estimation.
  • Existing approaches often use arbitrary signal thresholds, limiting data yield.

Purpose of the Study:

  • To develop a mathematically rigorous method (BACIQ) for estimating protein ratios and confidence in multiplexed proteomics.
  • To integrate peptide signal strength and measurement agreement for more accurate protein quantification.
  • To improve data interpretation and resource prioritization in quantitative proteomics.

Main Methods:

  • Developed BACIQ, a Bayesian approach integrating peptide signal strength and measurement agreement.
  • BACIQ estimates true protein ratios and associated confidence levels.
  • The method is implemented as a Python/Stan package for accessibility.

Main Results:

  • BACIQ removes the need for arbitrary peptide signal thresholds, increasing reported measurements.
  • Confidence can be assigned without replicates, and confidence intervals are more predictive for repeated experiments.
  • Reanalysis of published data identified ~2x more significant protein movers, including subtle subcellular localization changes (~1%).

Conclusions:

  • BACIQ significantly enhances the value of quantitative proteomics data.
  • The method provides more accurate protein ratio estimates and reliable confidence measures.
  • BACIQ facilitates better interpretation of experimental results and resource allocation for researchers.