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

Calibration Curves: Correlation Coefficient01:10

Calibration Curves: Correlation Coefficient

2.9K
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...
2.9K
Calculating and Interpreting the Linear Correlation Coefficient01:11

Calculating and Interpreting the Linear Correlation Coefficient

6.6K
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:
6.6K
Chebyshev's Theorem to Interpret Standard Deviation01:15

Chebyshev's Theorem to Interpret Standard Deviation

4.5K
Chebyshev’s theorem, also known as Chebyshev’s Inequality, states that the proportion of values of a dataset for K standard deviation is calculated using the equation:
4.5K
Extraction: Partition and Distribution Coefficients01:14

Extraction: Partition and Distribution Coefficients

3.5K
The distribution law or Nernst's distribution law is the law that governs the distribution of a solute between two immiscible solvents. This law, also known as the partition law, states that if a solute is added to the mixture of two immiscible solvents at a constant temperature, the solute is distributed between the two solvents in such a way that the ratio of solute concentrations in the solvents remains constant at equilibrium.
For extracting a solute from an aqueous phase into an...
3.5K
Coefficient of Correlation01:12

Coefficient of Correlation

6.6K
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...
6.6K
Kendall's Coefficient of Concordance01:20

Kendall's Coefficient of Concordance

583
Kendall's Coefficient of Concordance (W), also known as Kendall's W, is a non-parametric statistical measure used to assess the agreement or concordance between multiple raters or judges when they rank a set of items. It is often used when you have ordinal data (ranks) and you want to see if there is consistency or consensus among the raters. It is widely applied in research areas such as psychology, medicine, and social sciences, where multiple judges are asked to rank or rate subjects...
583

You might also read

Related Articles

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

Sort by
Same author

Inflammation rewires the enteric nervous system through neurogenic monocyte recruitment.

The Journal of experimental medicine·2026
Same author

The Faculty Voice: Perceptions of Durable Learning.

The Journal of nursing education·2025
Same author

Anion transport across lipid bilayers by a hydrogen bonding homo[2]catenane.

Chemical communications (Cambridge, England)·2025
Same author

Gut microbiota of dogs with cancer receiving anti-EGFR/HER2 immunization reveals potential biomarkers of patient survival.

bioRxiv : the preprint server for biology·2025
Same author

Imprinting Electrically Switchable Scalar Spin Chirality by Anisotropic Strain in a Kagome Antiferromagnet.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2025
Same author

COVID-19 and patient-reported experience of general practice in England: an evaluation study.

BJGP open·2025

Related Experiment Video

Updated: Oct 3, 2025

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

15.8K

The C-SHIFT Algorithm for Normalizing Covariances.

Evgenia Chunikhina, Paul Logan, Yevgeniy Kovchegov

    IEEE/ACM Transactions on Computational Biology and Bioinformatics
    |February 15, 2022
    PubMed
    Summary
    This summary is machine-generated.

    A new method called covariance shift (C-SHIFT) normalization effectively removes technical noise from gene expression data. This technique improves gene network analysis by accurately recovering the covariance matrix, outperforming existing methods.

    More Related Videos

    VDJ-Seq: Deep Sequencing Analysis of Rearranged Immunoglobulin Heavy Chain Gene to Reveal Clonal Evolution Patterns of B Cell Lymphoma
    15:07

    VDJ-Seq: Deep Sequencing Analysis of Rearranged Immunoglobulin Heavy Chain Gene to Reveal Clonal Evolution Patterns of B Cell Lymphoma

    Published on: December 28, 2015

    26.9K
    Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases
    09:33

    Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

    Published on: July 28, 2013

    28.6K

    Related Experiment Videos

    Last Updated: Oct 3, 2025

    Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
    14:27

    Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

    Published on: June 26, 2013

    15.8K
    VDJ-Seq: Deep Sequencing Analysis of Rearranged Immunoglobulin Heavy Chain Gene to Reveal Clonal Evolution Patterns of B Cell Lymphoma
    15:07

    VDJ-Seq: Deep Sequencing Analysis of Rearranged Immunoglobulin Heavy Chain Gene to Reveal Clonal Evolution Patterns of B Cell Lymphoma

    Published on: December 28, 2015

    26.9K
    Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases
    09:33

    Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

    Published on: July 28, 2013

    28.6K

    Area of Science:

    • Genomics
    • Bioinformatics
    • Systems Biology

    Background:

    • Omics technologies generate large-scale gene expression data.
    • Technical noise in experimental data obscures biological patterns.
    • Normalization is crucial for accurate statistical analysis and gene network inference.

    Purpose of the Study:

    • Introduce a novel normalization technique, covariance shift (C-SHIFT).
    • Address the challenge of recovering the covariance matrix from noisy gene expression data.
    • Improve the accuracy of gene network analysis.

    Main Methods:

    • Developed the C-SHIFT normalization algorithm.
    • Utilized optimization techniques, the blessing of dimensionality, and energy minimization.
    • Applied the method to logarithmic gene expression data for bias removal.

    Main Results:

    • C-SHIFT demonstrated superior performance in recovering the covariance matrix compared to Rank, Quantile, cyclic LOESS, and MAD methods.
    • Numerical experiments on synthetic data validated C-SHIFT's advantages.
    • The algorithm's effectiveness was also confirmed on real biological datasets.

    Conclusions:

    • C-SHIFT is a robust normalization method for gene expression data.
    • The technique enhances the accuracy of gene network analysis by effectively mitigating technical noise.
    • C-SHIFT offers a significant improvement over traditional normalization approaches.