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Related Concept Videos

RNA-seq03:21

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while microarray-based...
Calibration Curves: Correlation Coefficient01:10

Calibration Curves: Correlation Coefficient

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 other increases, 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...
Spearman's Rank Correlation Test01:20

Spearman's Rank Correlation Test

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...
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...
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:

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Related Experiment Video

Updated: May 13, 2026

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
05:22

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

Canonical correlation analysis for RNA-seq co-expression networks.

Shengjun Hong1, Xiangning Chen, Li Jin

  • 1State Key Laboratory of Genetic Engineering and MOE Key Laboratory of Contemporary Anthropology, School of Life Sciences and Institutes of Biomedical Sciences, Fudan University, Shanghai 200433, China.

Nucleic Acids Research
|March 6, 2013
PubMed
Summary
This summary is machine-generated.

New methods for RNA-sequencing (RNA-seq) data construct gene co-expression networks, revealing crucial biological insights. These approaches significantly outperform existing methods for analyzing gene expression variations in diseases.

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Identification of Circular RNAs using RNA Sequencing
08:25

Identification of Circular RNAs using RNA Sequencing

Published on: November 14, 2019

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Last Updated: May 13, 2026

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
05:22

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

Identification of Circular RNAs using RNA Sequencing
08:25

Identification of Circular RNAs using RNA Sequencing

Published on: November 14, 2019

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Next-generation sequencing (NGS) reveals extensive mRNA variants, crucial for understanding biological processes and diseases.
  • Current co-expression network methods, designed for microarray data, often ignore significant gene expression variations present in RNA-sequencing (RNA-seq) data.

Purpose of the Study:

  • To develop novel component-based methods for constructing co-expression networks using RNA-seq data.
  • To leverage information on exon, genomic position, and allele-specific expression for more comprehensive network analysis.

Main Methods:

  • Development of single and bivariate canonical correlation analysis (CCA) methods for co-expression network inference.
  • Application of these novel CCA methods to lung squamous cell cancer data from The Cancer Genome Atlas (TCGA) and a bipolar disorder/schizophrenia RNA-seq study.

Main Results:

  • Co-expression networks constructed using CCA and RNA-seq data provide rich genetic and molecular information.
  • Preliminary results indicate these networks offer valuable insights into biological processes and disease mechanisms.
  • The novel CCA-based methods demonstrate superior performance compared to current statistical methods for both microarray and RNA-seq data.

Conclusions:

  • Canonical correlation analysis offers a powerful approach for building co-expression networks from RNA-seq data.
  • These new methods enhance the understanding of gene expression variations and their role in complex diseases.
  • The developed techniques significantly improve upon existing methods for co-expression network construction.