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Related Concept Videos

Spearman's Rank Correlation Test01:20

Spearman's Rank Correlation Test

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Spearman's rank correlation test, also known as Spearman's rho, is a nonparametric method for assessing the strength and direction of association between two variables. This test is particularly valuable when the data distribution is unknown or when the assumption of normality does not hold. Named after the English psychologist and statistician Dr. Charles Edward Spearman, it serves as the nonparametric counterpart to Pearson's correlation coefficient.
Spearman's test calculates correlation by...
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Correlation means that there is a relationship between two or more variables (such as ice cream consumption and crime), but this relationship does not necessarily imply cause and effect. When two variables are correlated, it simply means that as one variable changes, so does the other. We can measure correlation by calculating a statistic known as a correlation coefficient. A correlation coefficient is a number from -1 to +1 that indicates the strength and direction of the relationship between...
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Dimensional analysis simplifies complex physical problems and guides experimental investigations, but it does not provide complete solutions. It identifies the dimensionless groups that influence a phenomenon, but experimental data is needed to establish the specific relationships and validate theoretical predictions.
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Statistical tests can calculate whether there is a relationship, or correlation, between independent and dependent variables. An indirect relationship of the variables signifies a correlation, while a direct relationship shows causation. If it is determined that no connection exists between the variables, then the correlation is a coincidence.
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Correlation01:09

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In statistics, two variables are said to be correlated if the values of one variable are associated with the other variable. Depending on the relationship between two variables, correlation can be of three types– positive correlation, negative correlation, and zero correlation.
Two variables, for example, a and b, are said to be positively correlated if both variables move in the same direction. In other words, a positive correlation exists between two variables, a and b, if:
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In statistics, correlation describes the degree of association between two variables. In the subfield of linear regression, correlation is mathematically expressed by the correlation coefficient, which describes the strength and direction of the relationship between two variables. The coefficient is symbolically represented by 'r' and ranges from -1 to +1. A positive value indicates a positive correlation where the two variables move in the same direction. A negative value suggests a...
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Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks
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Microbial Networks in SPRING - Semi-parametric Rank-Based Correlation and Partial Correlation Estimation for

Grace Yoon1, Irina Gaynanova1, Christian L Müller2

  • 1Department of Statistics, Texas A&M University, College Station, TX, United States.

Frontiers in Genetics
|June 28, 2019
PubMed
Summary

We developed SPRING, a new method for analyzing quantitative microbiome data. It accurately infers microbial association networks, even with excess zeros, providing insights into microbial ecosystems.

Keywords:
absolute abundanceamplicon sequencingassociation networkcopulagraphical modelgut microbiomezero inflation

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Area of Science:

  • Microbiology
  • Bioinformatics
  • Computational Biology

Background:

  • High-throughput sequencing provides microbial community data, but often sparse relative abundances.
  • Quantitative microbiome profiling pairs sequencing with cell counts for absolute abundance data.
  • Excess zeros in quantitative microbiome data pose statistical challenges for correlation analysis.

Purpose of the Study:

  • To develop a statistical method for estimating correlations and partial correlations from quantitative microbiome data.
  • To infer microbial association networks from quantitative microbiome data.
  • To address the challenge of excess zeros in microbiome datasets.

Main Methods:

  • Proposed a semi-parametric rank-based approach for correlation estimation.
  • Combined the rank-based estimator with sparse graphical modeling techniques.
  • Developed the Semi-Parametric Rank-based approach for INference in Graphical model (SPRING).
  • Introduced a novel data generation mechanism for quantitative microbiome data.

Main Results:

  • SPRING effectively handles excess zeros in quantitative microbiome data.
  • SPRING enables reliable inference of microbial association networks.
  • Demonstrated superior network recovery performance on benchmark problems.
  • Showed robustness to misspecifications in total cell count estimates.

Conclusions:

  • SPRING provides a robust tool for analyzing quantitative microbiome data.
  • The method facilitates the inference of microbial ecosystem structures and interactions.
  • SPRING is applicable to diverse quantitative microbiome datasets, including human gut microbiome data.