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

Correlation of Experimental Data01:23

Correlation of Experimental Data

523
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,...
523
Coefficient of Correlation01:12

Coefficient of Correlation

9.2K
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...
9.2K
Microsoft Excel: Pearson's Correlation01:18

Microsoft Excel: Pearson's Correlation

2.7K
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...
2.7K
Correlations02:20

Correlations

37.1K
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...
37.1K
Correlation and Regression00:53

Correlation and Regression

4.2K
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...
4.2K
Spearman's Rank Correlation Test01:20

Spearman's Rank Correlation Test

1.6K
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...
1.6K

You might also read

Related Articles

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

Sort by
Same author

Inverse Association between the ω-3 Index and Neutrophil-Lymphocyte Ratio: Pooled Results from Four Supplementation Trials.

The Journal of nutrition·2026
Same author

Omega-6 polyunsaturated fatty acids and adiposity in the UK Biobank Cohort: a cross-sectional and longitudinal prospective analysis.

The British journal of nutrition·2026
Same author

Demographic Associations with GPS-Inferred Routine Activity Spaces: Data from the Everyday Environments and Experiences (E3) Study.

Sensors (Basel, Switzerland)·2026
Same author

Network divergence analysis identifies adaptive gene modules and two orthogonal vulnerability axes in pancreatic cancer.

Molecular oncology·2026
Same author

Development of a blood-based lipidomic fat quality score for the risk of ischemic stroke.

European stroke journal·2026
Same author

Blood omega-3 is inversely related to risk of early-onset dementia.

Clinical nutrition (Edinburgh, Scotland)·2026

Related Experiment Video

Updated: Apr 7, 2026

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
05:22

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

4.1K

Cautions about the reliability of pairwise gene correlations based on expression data.

Scott Powers1, Matt DeJongh2, Aaron A Best3

  • 1Department of Statistics, Stanford University Stanford, CA, USA.

Frontiers in Microbiology
|July 14, 2015
PubMed
Summary

Gene expression data analysis can be biased by sampling noise. Genes with small expression differences often yield unreliable association metrics, impacting bacterial transcriptional studies.

Keywords:
Pearson correlationco-regulationmutual informationoperon predictionregulatory network inference

More Related Videos

Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes
05:07

Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes

Published on: November 7, 2025

530
An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations
10:17

An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations

Published on: November 3, 2010

23.4K

Related Experiment Videos

Last Updated: Apr 7, 2026

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
05:22

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

4.1K
Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes
05:07

Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes

Published on: November 7, 2025

530
An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations
10:17

An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations

Published on: November 3, 2010

23.4K

Area of Science:

  • Genomics
  • Systems Biology
  • Bioinformatics

Background:

  • Genome-wide transcript abundance data is rapidly growing via microarrays and RNA-Seq.
  • This data holds potential for deep biological insights into single-celled organisms.
  • However, realizing this potential has been challenging.

Purpose of the Study:

  • To investigate the causes of variation in pairwise gene association estimates from genome-wide expression data.
  • To identify factors contributing to unreliable gene association metrics in bacterial transcriptomics.

Main Methods:

  • Analyzed 2782 genome-wide expression samples from six bacterial species.
  • Computed pairwise gene associations using correlation and mutual information.
  • Investigated the impact of sampling bias and small expression differences on association estimates.

Main Results:

  • Found significant variation in pairwise gene association estimates across diverse bacteria and datasets.
  • Identified sampling bias, specifically small gene expression differences, as the primary cause.
  • Demonstrated that small expression differences are often noise, leading to biased association metrics.

Conclusions:

  • Proposed flagging genes with small absolute expression differences (e.g., standard deviation < 0.5) to mitigate bias.
  • This strategy can enhance confidence in genome-wide conclusions for bacterial transcriptional behavior.
  • Further research is needed to refine methods for identifying such genes before association analysis.