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

8.9K
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...
8.9K
Calibration Curves: Correlation Coefficient01:10

Calibration Curves: Correlation Coefficient

5.0K
In a linear calibration curve, there is a value called the calibration coefficient, denoted by 'r,' which measures the strength and the direction of association between two variables. The correlation coefficient value ranges from −1 to +1. A value of +1 indicates a perfect positive linear correlation, −1 denotes a perfect negative correlation, and 0 implies no correlation between the two variables. A positive correlation value establishes that as one variable increases, the...
5.0K
Calculating and Interpreting the Linear Correlation Coefficient01:11

Calculating and Interpreting the Linear Correlation Coefficient

8.3K
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. Hence, it is also known as the Pearson product-moment correlation coefficient. It can be calculated using the following equation:
8.3K
Correlation of Experimental Data01:23

Correlation of Experimental Data

499
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,...
499
Spearman's Rank Correlation Test01:20

Spearman's Rank Correlation Test

1.5K
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...
1.5K
Microsoft Excel: Pearson's Correlation01:18

Microsoft Excel: Pearson's Correlation

2.3K
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...
2.3K

You might also read

Related Articles

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

Sort by
Same author

Homologous recombination deficiency-driven genomic instability in ovarian cancer as an indicator of BRCA1 and BRCA2 variant pathogenicity.

American journal of human genetics·2026
Same author

Circulating tumor DNA-guided response evaluation in patients with previously treated gastroesophageal adenocarcinoma.

Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association·2026
Same author

Updated ENIGMA recommendations for reporting germline variants in cancer susceptibility genes and their translation into twenty languages.

Journal of medical genetics·2026
Same author

Somatic mutational landscape in von Hippel-Lindau familial hemangioblastoma.

Molecular oncology·2026
Same author

The Value of Patch Testing With Stoma Care Products Among Patients With an Ostomy.

Contact dermatitis·2026
Same author

[Autoimmune progesterone dermatitis].

Ugeskrift for laeger·2025

Related Experiment Video

Updated: Feb 25, 2026

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
05:22

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

4.1K

Generalized Correlation Coefficient for Non-Parametric Analysis of Microarray Time-Course Data.

Qihua Tan1, Mads Thomassen1, Mark Burton1

  • 1.

Journal of Integrative Bioinformatics
|July 29, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces a new non-parametric method using generalized correlation to analyze complex gene expression patterns over time in microarray data. The approach effectively detects and clusters heterogeneous patterns, aiding in understanding disease associations.

Keywords:
gene expression microarraygeneralized correlation coefficienttime-course

More Related Videos

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

3.4K
A Semiautomated ChIP-Seq Procedure for Large-scale Epigenetic Studies
08:04

A Semiautomated ChIP-Seq Procedure for Large-scale Epigenetic Studies

Published on: August 13, 2020

4.0K

Related Experiment Videos

Last Updated: Feb 25, 2026

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
05:22

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

4.1K
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

3.4K
A Semiautomated ChIP-Seq Procedure for Large-scale Epigenetic Studies
08:04

A Semiautomated ChIP-Seq Procedure for Large-scale Epigenetic Studies

Published on: August 13, 2020

4.0K

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Analyzing time-course gene expression data from microarrays presents challenges due to complex biological responses.
  • Existing methods may struggle to capture the full spectrum of dynamic gene expression changes.

Purpose of the Study:

  • To introduce a novel non-parametric method for analyzing heterogeneous time-course gene expression patterns.
  • To develop a combinatory approach for detecting, testing, and clustering these complex patterns.

Main Methods:

  • Introduction of the generalized correlation coefficient.
  • Application of a combinatory approach for pattern detection, testing, and clustering.
  • Comparison with parametric analysis for validation.

Main Results:

  • The proposed method successfully identified nonlinear time-course gene expression patterns.
  • Results showed high agreement between the non-parametric generalized correlation analysis and traditional parametric methods.
  • The approach demonstrated effectiveness in detecting and clustering heterogeneous patterns.

Conclusions:

  • The non-parametric generalized correlation analysis is a valuable tool for microarray time-course data.
  • This method efficiently explores complex relationships within omics data.
  • It aids in studying the association of gene expression patterns with disease and health outcomes.