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Related Concept Videos

Extraction: Partition and Distribution Coefficients01:14

Extraction: Partition and Distribution Coefficients

The distribution law or Nernst's distribution law is the law that governs the distribution of a solute between two immiscible solvents. This law, also known as the partition law, states that if a solute is added to the mixture of two immiscible solvents at a constant temperature, the solute is distributed between the two solvents in such a way that the ratio of solute concentrations in the solvents remains constant at equilibrium.
For extracting a solute from an aqueous phase into an organic...
Residual Plots01:07

Residual Plots

A residual plot is a statistical representation of data used to analyze correlation and regression results. It helps verify the requirements for drawing specific conclusions about correlation and regression. To obtain the residual plot, first, the residual for each data value is calculated, which is simply the vertical distance between the observed and the predicted value obtained from the regression equation.
When the residual values are plotted against the variable x, it is called a residual...
Boxplot01:12

Boxplot

Box plots (also called box-and-whisker plots or box-whisker plots) give an excellent graphical image of the concentration of the data. They also show how far the extreme values are from most data. A box plot is constructed from five values: the minimum value, the first quartile, the median, the third quartile, and the maximum value. We use these values to compare how close other data values are to them. To construct a box plot, use a horizontal or vertical number line and a rectangular box. The...
Modified Boxplots00:57

Modified Boxplots

A standard box and whisker plot informs us about the spread of the data in a given sample. One can identify the minimum value, maximum value, first quartile value, second quartile or median value, and third quartile.
However, the box plot does not tell the reader about outliers - values that lie far from the center of the data. We can modify the standard box and whisker plot to identify the outliers and visualize the actual spread of the data in a sample.
Initially, we calculate the adjusted...
Review and Preview01:10

Review and Preview

In statistics, several tools are used to interpret the data. Measures of central tendency represent the characteristics of the data, such as mean, median, and mode. Additionally, measures of variance like standard deviation and range are used to find the spread of data from the mean. Relative standing measures the distance between data locations. Commonly used measures of relative standings are percentile, z score, and quartiles.
Percentiles are a type of fractile that partition data into...
Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
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The Terroir Concept Interpreted through Grape Berry Metabolomics and Transcriptomics
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Utilities for quantifying separation in PCA/PLS-DA scores plots.

Bradley Worley1, Steven Halouska, Robert Powers

  • 1Department of Chemistry, University of Nebraska-Lincoln, NE 68588-0304, USA.

Analytical Biochemistry
|October 20, 2012
PubMed
Summary

This study enhances metabolic fingerprinting analysis by updating PCAtoTree software. It provides new statistical methods for visualizing and quantifying differences between experimental groups in spectral data.

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Area of Science:

  • Metabolomics
  • Chemometrics
  • Bioinformatics

Background:

  • Metabolic fingerprinting uses multivariate analysis for spectral data interpretation.
  • Low-dimensional scores plots are crucial for analyzing large datasets.
  • Quantitative statistical measures are needed to assess group differences.

Purpose of the Study:

  • To improve the visualization and quantification of separations in metabolic fingerprinting scores plots.
  • To provide robust statistical methods for assessing significance in group comparisons.
  • To update the PCAtoTree software with enhanced analytical capabilities.

Main Methods:

  • Utilized principal components analysis (PCA) and projection to latent structures discriminant analysis (PLS-DA).
  • Developed dendrograms with nonparametric and parametric hypothesis testing for node significance.
  • Incorporated 95% confidence ellipsoids for experimental group visualization in scores plots.

Main Results:

  • The updated PCAtoTree software reliably visualizes and quantifies separations in scores plots.
  • Dendrograms effectively assess node significance using hypothesis testing.
  • Confidence ellipsoids accurately identify group separations in spectral data.

Conclusions:

  • The enhanced PCAtoTree software offers improved statistical rigor for metabolic fingerprinting.
  • The new methods facilitate reliable interpretation of differences between experimental groups.
  • This advancement supports more accurate data-driven conclusions in metabolomics research.