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Related Concept Videos

Correlation of Experimental Data01:23

Correlation of Experimental Data

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, and...
Coefficient of Correlation01:12

Coefficient of Correlation

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 strength of the linear...
Factors Affecting Protein-Drug Binding: Protein-Related Factors01:20

Factors Affecting Protein-Drug Binding: Protein-Related Factors

Drug binding to proteins is a key aspect of pharmacokinetics and can influence a drug's distribution, absorption, and elimination in the body. Several factors, including the drug's physiochemical properties, protein concentration, disease states, and the number of binding sites on the protein, influence this process.
The physicochemical properties of a drug play a significant role in its ability to bind to proteins. Lipophilic drugs, which dissolve in fats, oils, and lipids, can be bound by...
Calculating and Interpreting the Linear Correlation Coefficient01:11

Calculating and Interpreting the Linear Correlation Coefficient

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:
Correlations02:20

Correlations

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...
Correlation01:09

Correlation

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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Related Experiment Video

Updated: Jun 20, 2026

Analyzing Tumor Gene Expression Factors with the CorExplorer Web Portal
08:00

Analyzing Tumor Gene Expression Factors with the CorExplorer Web Portal

Published on: October 11, 2019

Correlating gene and protein expression data using Correlated Factor Analysis.

Chuen Seng Tan1, Agus Salim, Alexander Ploner

  • 1Lewis-Sigler Institute, Princeton University, New Jersey, USA. chuentan@princeton.edu

BMC Bioinformatics
|September 3, 2009
PubMed
Summary
This summary is machine-generated.

Correlated Factor Analysis (CFA) effectively models gene and protein correlations, unlike gSVD. CFA identifies biologically meaningful co-regulated genes and proteins in cancer data, revealing insights into processes like blood vessel development.

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Last Updated: Jun 20, 2026

Analyzing Tumor Gene Expression Factors with the CorExplorer Web Portal
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Published on: October 11, 2019

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Application of Unsupervised Multi-Omic Factor Analysis to Uncover Patterns of Variation and Molecular Processes Linked to Cardiovascular Disease
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Application of Unsupervised Multi-Omic Factor Analysis to Uncover Patterns of Variation and Molecular Processes Linked to Cardiovascular Disease

Published on: September 20, 2024

Area of Science:

  • Genomics
  • Proteomics
  • Systems Biology

Background:

  • Integrating transcriptomic and proteomic data offers insights into complex biological mechanisms.
  • Current methods often require one-to-one gene-protein matching, which doesn't reflect biological pathway complexity.
  • Novel approaches are needed to model the non-one-to-one relationships between genes and proteins.

Purpose of the Study:

  • To investigate Correlated Factor Analysis (CFA) for modeling genome-scale gene and protein data correlations.
  • To compare the performance of CFA with Generalized Singular Value Decomposition (gSVD).
  • To identify biologically meaningful co-regulated gene-protein patterns.

Main Methods:

  • Correlated Factor Analysis (CFA) was employed to model correlations between gene and protein expression data.
  • CFA considers all possible gene-protein pairs and utilizes all available data.
  • Generalized Singular Value Decomposition (gSVD) was used as a comparative method.

Main Results:

  • Simulation studies showed CFA estimates consistently capture dominant correlation patterns, while gSVD estimates do not.
  • Application to cancer data identified co-regulated genes and proteins with biologically relevant interpretations.
  • Key correlated processes identified include blood vessel morphogenesis and development.

Conclusions:

  • Correlated Factor Analysis (CFA) is a valuable tool for integrating and modeling gene-protein expression data.
  • CFA effectively identifies patterns of gene expression correlated with protein expression.
  • The method facilitates the discovery of biologically significant relationships between transcriptomic and proteomic data.