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

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...

DNA Microarrays

DNA Microarrays

Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...

Statistical Hypothesis Testing

Statistical Hypothesis Testing

Hypothesis testing is a critical statistical procedure facilitating informed, evidence-based decisions. It begins with a hypothesis, which is a tentative explanation, or a prediction about a population parameter. This hypothesis can be either a null hypothesis (H0), indicating no effect or difference, or an alternative hypothesis (Ha), suggesting an effect or difference.
Statistical significance measures the probability that an observed result occurred by chance. If this probability, known as...

Expected Frequencies in Goodness-of-Fit Tests

Expected Frequencies in Goodness-of-Fit Tests

A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n) to the number of categories (k).

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...

Significance Testing: Overview

Significance Testing: Overview

Significance testing is a set of statistical methods used to test whether a claim about a parameter is valid. In analytical chemistry, significance testing is used primarily to determine whether the difference between two values comes from determinate or random errors. The effect of a particular change in the measurement protocol, analyst, or sample itself can cause a deviation from the expected result. In the case of a suspected deviation/outlier, we need to be able to confirm mathematically...

You might also read

Related Articles

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

Sort by

Same author

Genome-scale mapping of variant, enhancer and gene function in primary human CD4+ T cells.

bioRxiv : the preprint server for biology·2026

Same author

Multimodal plasma and urinary cell-free DNA profiling improves risk stratification in newly diagnosed prostate cancer.

NPJ precision oncology·2026

Same author

Federated single-cell QTL meta-analysis reveals novel disease mechanisms.

bioRxiv : the preprint server for biology·2026

Same author

Interpretation, extrapolation and perturbation of single cells.

Nature reviews. Genetics·2026

Same author

Genetic architecture and mechanisms of host-microbiome interactions from a multi-cohort analysis of outbred laboratory rats.

Nature communications·2025

Same author

Escape from X inactivation is directly modulated by Xist noncoding RNA.

Nature cell biology·2025

Search research articles

Related Experiment Video

Updated: Jun 14, 2026

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2

Published on: September 18, 2021

A robust Bayesian two-sample test for detecting intervals of differential gene expression in microarray time series.

Oliver Stegle¹, Katherine J Denby, Emma J Cooke

¹Interdepartmental Bioinformatics Group, Max Planck Institute for Developmental Biology, Max Planck Institute for Biological Cybernetics, Tübingen, Germany. oliver.stegle@tuebingen.mpg.de

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology

|April 10, 2010

Summary

This study introduces a new method to find when gene expression changes occur over time during environmental responses. The approach accurately identifies differential gene expression intervals in time-series data.

More Related Videos

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

Related Experiment Videos

Last Updated: Jun 14, 2026

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2

Published on: September 18, 2021

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

Area of Science:

Molecular Biology
Genomics
Bioinformatics

Background:

Understanding gene regulation in response to environmental changes is crucial in molecular biology.
Detecting differential gene expression is a key step, but identifying *when* expression changes occur is often overlooked.

Purpose of the Study:

To develop a novel statistical test for identifying specific time intervals of differential gene expression in microarray time series data.
To provide a method that offers insights into the temporal dynamics of gene regulatory programs.

Main Methods:

A two-sample test based on Gaussian process regression is proposed.
The method is designed to handle multiple replicates and is robust to outliers.
Applied to a microarray time series dataset of Arabidopsis thaliana in response to fungal infection.

Main Results:

The proposed test effectively identifies intervals of differential gene expression.
The algorithm demonstrates favorable performance in classification experiments compared to existing methods.
Provides deeper insights into time-dependent differential gene expression patterns.

Conclusions:

The developed method enhances the analysis of time-course gene expression data.
It offers a valuable tool for dissecting regulatory responses to environmental stimuli.
Facilitates a more comprehensive understanding of gene function and regulation over time.