Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Coefficient of Correlation01:12

Coefficient of Correlation

9.3K
The correlation coefficient, r, developed by Karl Pearson in the early 1900s, is numerical and provides a measure of strength and direction of the linear association between the independent variable x and the dependent variable y.
If you suspect a linear relationship between x and y, then r can measure how strong the linear relationship is.
What the VALUE of r tells us:
The value of r is always between –1 and +1: –1 ≤ r ≤ 1.
The size of the correlation r indicates the...
9.3K
Correlation of Experimental Data01:23

Correlation of Experimental Data

528
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.
For example, a spherical particle moving through a viscous fluid experiences drag. Dimensional analysis shows that the drag force depends on the particle's diameter, velocity,...
528
Correlation and Regression00:53

Correlation and Regression

4.4K
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...
4.4K
Microsoft Excel: Pearson's Correlation01:18

Microsoft Excel: Pearson's Correlation

2.8K
Microsoft Excel is a powerful tool for statistical analysis, including calculating Pearson's correlation coefficient, which measures the strength and direction of a linear relationship between two continuous variables. Pearson's correlation coefficient, often denoted as "r," ranges from -1 to 1. A value close to 1 indicates a strong positive correlation, meaning as one variable increases, the other does too. A value close to -1 indicates a strong negative correlation, implying...
2.8K
Spearman's Rank Correlation Test01:20

Spearman's Rank Correlation Test

1.7K
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...
1.7K
Correlation01:09

Correlation

16.2K
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:
16.2K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Post-Transcriptional Size-Dependent Expression of the Fission Yeast Cdc13 Cyclin.

bioRxiv : the preprint server for biology·2026
Same author

Extending Biopython to combine multiple sequence alignments with the same reference into a Multiple Sequence Alignment.

microPublication biology·2026
Same author

Genetic and environmental determinants of multicellular-like phenotypes in fission yeast.

Genetics·2026
Same author

PomBase in 2026: expanding knowledge, modeling connections.

Genetics·2026
Same author

Mitochondrial Translation Inhibition Triggers an Rst2-Controlled Transcriptional Reprogramming of Carbon Metabolism in Stationary-Phase Cells of Fission Yeast.

Biomolecules·2025
Same author

Axonal injury is a targetable driver of glioblastoma progression.

Nature·2025
Same journal

Another 10 years of PLOS Computational Biology: A data-driven reflection on trends in genomics research.

PLoS computational biology·2026
Same journal

Mobility data resolution needed to inform predictive models of spatial epidemic spread from mobile phone data.

PLoS computational biology·2026
Same journal

DeepMethylation: A deep learning framework for tissue-specific DNA methylation prediction and functional variant annotation.

PLoS computational biology·2026
Same journal

Redefining and estimating the early-phase reproduction ratio for epidemic outbreaks in spatially structured populations.

PLoS computational biology·2026
Same journal

Optimized phenotype definitions boost GWAS power.

PLoS computational biology·2026
Same journal

Detection, communication, and individual identification with deep audio embeddings: A case study with North Atlantic right whales.

PLoS computational biology·2026
See all related articles

Related Experiment Video

Updated: Apr 16, 2026

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis
07:11

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

Published on: November 10, 2023

3.5K

Proportionality: a valid alternative to correlation for relative data.

David Lovell1, Vera Pawlowsky-Glahn2, Juan José Egozcue3

  • 1Queensland University of Technology, Brisbane, Australia.

Plos Computational Biology
|March 17, 2015
PubMed
Summary
This summary is machine-generated.

Correlation is misleading for relative data in life sciences. Proportionality, measured by statistic ϕ, offers a valid alternative for analyzing compositional data and building co-expression networks.

More Related Videos

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

3.1K
Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine
07:05

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine

Published on: October 27, 2016

9.8K

Related Experiment Videos

Last Updated: Apr 16, 2026

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis
07:11

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

Published on: November 10, 2023

3.5K
A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

3.1K
Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine
07:05

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine

Published on: October 27, 2016

9.8K

Area of Science:

  • Life Sciences
  • Bioinformatics
  • Genomics

Background:

  • Many life science measurements produce relative abundances (compositional data).
  • Standard statistical methods like correlation are inappropriate for analyzing compositional data.
  • Misinterpretation of differential expression and associations can arise from using incorrect methods.

Purpose of the Study:

  • To demonstrate the limitations of correlation with relative data.
  • To introduce proportionality as a valid statistical approach for compositional data analysis.
  • To present a new statistic, ϕ, for quantifying proportionality.

Main Methods:

  • Analysis of yeast gene expression data.
  • Application of the new proportionality statistic (ϕ).
  • Comparison of proportionality with traditional correlation methods.

Main Results:

  • Correlation can yield misleading results when applied to relative abundances.
  • Proportionality provides a meaningful measure of association for compositional data.
  • The statistic ϕ effectively quantifies the strength of proportionality.
  • Proportionality enables the construction of co-expression networks and clustered heatmaps.

Conclusions:

  • Proportionality is a suitable alternative to correlation for analyzing relative biological data.
  • The statistic ϕ offers a robust tool for association analysis in compositional datasets.
  • This approach facilitates more accurate interpretation of gene expression and other relative measurements.
  • Further research into the molecular mechanisms of proportional gene regulation is warranted.