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Related Concept Videos

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:
Correlation and Regression00:53

Correlation and Regression

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 negative...
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...
Time-Series Graph00:54

Time-Series Graph

A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
Scatter Plot01:15

Scatter Plot

The most common and easiest way to display the relationship between two variables, x and y, is a scatter plot. A scatter plot shows the direction of a relationship between the variables. A clear direction happens when there is either:

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CorrelationCalculator and Filigree: Tools for Data-Driven Network Analysis of Metabolomics Data
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CorrelationCalculator and Filigree: Tools for Data-Driven Network Analysis of Metabolomics Data

Published on: November 10, 2023

Correlation networks visualization.

Nicholas Provart1

  • 1Department of Cell and Systems Biology/Centre for the Analysis of Genome Evolution and function, University of Toronto Toronto, ON, Canada.

Frontiers in Plant Science
|November 28, 2012
PubMed
Summary
This summary is machine-generated.

New computational methods analyze large plant biology datasets for hypothesis generation. Gene coexpression and network analysis, combined with visualization tools, advance biological research.

Keywords:
coexpression analysishypothesis generationnetwork visualizationprotein–protein interactionstranscriptomics

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Area of Science:

  • Computational Biology
  • Plant Science
  • Bioinformatics

Background:

  • Large-scale biological datasets, particularly transcriptomic data, are increasingly available.
  • These datasets offer opportunities to study plant biology at various resolutions, from whole tissues to single cells.

Purpose of the Study:

  • To highlight the utility of in silico methods for generating hypotheses in plant biology.
  • To discuss the application of computational approaches like gene coexpression and network analysis.
  • To emphasize the future importance of cross-level correlation and advanced visualization tools.

Main Methods:

  • Utilizing publicly available large datasets for hypothesis generation.
  • Applying gene coexpression analysis to identify relationships between genes.
  • Employing network analysis for visualization and refinement of biological insights.
  • Integrating diverse data types, including gene expression and protein-protein interactions.

Main Results:

  • In silico methods enable hypothesis generation for experimental validation (e.g., phenotyping, genetic analysis).
  • Gene coexpression and network analyses reveal complex biological relationships.
  • Integration of multi-level data enhances biological understanding.

Conclusions:

  • Computational approaches are powerful tools for advancing plant biology research.
  • Network visualization and analysis are crucial for interpreting complex biological data.
  • Future research will benefit from tools for visualizing cross-level correlations.