Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Strategies for Assessing and Addressing Confounding

Strategies for Assessing and Addressing Confounding

Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
Confounding can be addressed at both the design phase of a study and through analytical methods after data...

Confounding in Epidemiological Studies

Confounding in Epidemiological Studies

Confounding in statistical epidemiology represents a pivotal challenge, referring to the distortion in the perceived relationship between an exposure and an outcome due to the presence of a third variable, known as a confounder. This variable is associated with both the exposure and the outcome but is not a direct link in their causal chain. Its presence can lead to erroneous interpretations of the exposure's effect, either exaggerating or underestimating the true association. This phenomenon...

Multiple Allele Traits

Multiple Allele Traits

The Concept of Multiple Allelism

Multiple Allele Traits

Multiple Allele Traits

The Concept of Multiple Allelism

Comparing the Survival Analysis of Two or More Groups

Comparing the Survival Analysis of Two or More Groups

Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and Cox...

Friedman Two-way Analysis of Variance by Ranks

Friedman Two-way Analysis of Variance by Ranks

Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures from...

You might also read

Related Articles

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

Sort by

Same author

Empowering Kidney Care: An ANNA-ASN Conference with Nurse-Physician Partners - Home Dialysis.

Nephrology nursing journal : journal of the American Nephrology Nurses' Association·2026

Same author

10-step systemic safety assessment of extractables and leachables associated with drug products integrating topline, traditional, and next generation risk assessment.

Regulatory toxicology and pharmacology : RTP·2026

Same author

Acquisition of ampliconic sequences marks a selfish mouse t-haplotype.

Nature communications·2025

Same author

How I Treat Nocturnal Home Hemodialysis-An Underprescribed but Essential Home Dialysis Modality in the United States.

Clinical journal of the American Society of Nephrology : CJASN·2025

Same author

High-resolution modeling of extreme heat events with socioeconomic consideration: a real-case WRF-LES approach.

Environmental science and pollution research international·2025

Same author

Safety assessment of endotoxin related to drug products and medical devices: A review.

Toxicology letters·2025

Search research articles

Related Experiment Video

Updated: Jul 2, 2026

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

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

Published on: September 20, 2024

conMItion: an R package adjusting confounding factors for associations in multi-omics.

Gaojianyong Wang¹, Frank Liu¹, Ze Chen¹

¹Institute for Systems Genetics and Department of Biochemistry and Molecular Pharmacology, NYU Grossman School of Medicine, 435 E 30th St, 10016, NY, USA.

Bioinformatics (Oxford, England)

|June 30, 2026

Summary

This study introduces conMItion, an R package for analyzing cancer multi-omics data. It uses conditional mutual information (CMI) to accurately identify gene associations, accounting for confounding factors in tumor data.

Keywords:

Conditional mutual information copy number alteration expression tumor microenvironment tumor purity

More Related Videos

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

Related Experiment Videos

Last Updated: Jul 2, 2026

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

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

Published on: September 20, 2024

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

Area of Science:

Genomics
Bioinformatics
Cancer Research

Background:

Mutual information (MI) is crucial for analyzing cancer multi-omics data.
Confounding factors like tumor purity can bias MI measurements, misclassifying genetic events.
Conditional mutual information (CMI) offers a robust method to address confounding factors.

Purpose of the Study:

To introduce the conMItion R package for estimating CMI in multi-omics data.
To provide a flexible tool for adjusting for one or two confounding factors.
To demonstrate the package's utility in cancer research.

Main Methods:

Development of the conMItion R package for CMI estimation.
Application of CMI to identify somatic copy number alteration-expression associations in bladder cancer.
Utilizing CMI to find associated cell types in the lung cancer tumor microenvironment using single-cell RNA sequencing data.

Main Results:

The conMItion package effectively estimates CMI and its statistical significance.
Identified interchromosomal somatic copy number alteration-expression associations in bladder cancer.
Discovered associated cell types within the lung cancer tumor microenvironment.

Conclusions:

CMI is a powerful approach for robustly analyzing cancer multi-omics data.
The conMItion package facilitates the identification of true biological associations by controlling for confounders.
This methodology aids in understanding tumor development, progression, and treatment strategies.