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Related Concept Videos

Variation01:19

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An important characteristic of any set of data is the variation in the data. In some data sets, the data values are concentrated closely near the mean; in other data sets, the data values are more widely spread out from the mean. The most common measure of variation, or spread, is the standard deviation, which is the square root of variance.
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In the field of psychology, there are several ways to organize measurements of a trait, feature, or characteristic (i.e., variables). Qualitative data, such as ethnicity, can be tabulated into a frequency count to provide information about the proportion, as well as the variety of groups in a sample or population. On the other hand, researchers can perform a wider set of calculations on quantitative data. The mean, mode, and median, for instance, are central tendency measures to identify a...
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A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
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Eigen-R2 for dissecting variation in high-dimensional studies.

Lin S Chen1, John D Storey

  • 1Lewis-Sigler Institute and Department of Molecular Biology, Princeton University, Princeton, NJ 08544, USA.

Bioinformatics (Oxford, England)
|August 23, 2008
PubMed
Summary
This summary is machine-generated.

We developed eigen-R(2), a new statistical algorithm and software, to analyze variation in high-dimensional biological data. This method offers a more robust approach compared to averaging R(2) across features.

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Area of Science:

  • Bioinformatics
  • Statistical genetics
  • Computational biology

Background:

  • High-dimensional biological datasets present challenges for variation analysis.
  • Existing methods like averaging R(2) may not fully capture complex relationships.

Purpose of the Study:

  • To introduce a novel statistical algorithm and software package, eigen-R(2).
  • To provide a method for dissecting variation in high-dimensional biological data concerning other measured variables.

Main Methods:

  • Development of the eigen-R(2) statistical algorithm.
  • Application of eigen-R(2) to two real-life biological datasets.
  • Comparison of eigen-R(2) performance against the average R(2) method.

Main Results:

  • The eigen-R(2) algorithm effectively dissects variation in complex biological datasets.
  • Demonstrated utility through application to two distinct real-world biological examples.
  • Eigen-R(2) shows advantages over simple averaging of R(2) across features.

Conclusions:

  • Eigen-R(2) is a powerful new tool for analyzing high-dimensional biological data.
  • The software package facilitates the application of this advanced statistical method.
  • This approach enhances the understanding of variable relationships in biological systems.