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

Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

301
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...
301
Per-Unit Sequence Models01:26

Per-Unit Sequence Models

119
An ideal Y-Y transformer, grounded through neutral impedances, displays per-unit sequence networks akin to those of a single-phase ideal transformer when subjected to balanced positive- or negative-sequence currents. These currents do not produce neutral currents, and their associated voltage drops.
Zero-sequence currents, which are identical in magnitude and phase, generate a neutral current, resulting in voltage drops across the neutral impedance and the low-voltage winding. If the...
119
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

87
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
87
Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

6.2K
Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
6.2K
Biostatistics: Overview01:20

Biostatistics: Overview

377
Biostatistics plays a crucial role in understanding and analyzing data in healthcare and biology. Biostatisticians conduct experiments, gather evidence, and draw meaningful conclusions using statistical methods and techniques. Different variables form the foundation of biostatistical analysis, allowing researchers to understand and interpret data effectively. These variables are classified into different types, each serving a specific purpose in statistical analysis.
Discrete variables are...
377
Binomial Probability Distribution01:15

Binomial Probability Distribution

11.4K
A binomial distribution is a probability distribution for a procedure with a fixed number of trials, where each trial can have only two outcomes.
The outcomes of a binomial experiment fit a binomial probability distribution. A statistical experiment can be classified as a binomial experiment if the following conditions are met:
There are a fixed number of trials. Think of trials as repetitions of an experiment. The letter n denotes the number of trials.
There are only two possible outcomes,...
11.4K

You might also read

Related Articles

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

Sort by
Same author

Immunohistochemical characterization of nerve fibers supplying canine elbow joint capsule.

BMC veterinary research·2026
Same author

metaGEENOME: an integrated framework for differential abundance analysis of microbiome data in cross-sectional and longitudinal studies.

BMC bioinformatics·2025
Same author

Detection of Outlying Correlation Coefficients in Multicenter Clinical Trials.

Pharmaceutical statistics·2025
Same author

Investigation of the canine elbow joint innervation in 100 joints.

PloS one·2025
Same author

Computational Tools for Hydrogen-Deuterium Exchange Mass Spectrometry Data Analysis.

Chemical reviews·2024
Same author

Using an early outcome as the sole source of information of interim decisions regarding treatment effect on a long-term endpoint: The non-Gaussian case.

Pharmaceutical statistics·2024

Related Experiment Video

Updated: Sep 15, 2025

Rare Event Detection Using Error-corrected DNA and RNA Sequencing
10:36

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Published on: August 3, 2018

12.2K

A hierarchical negative-binomial model for analysis of correlated sequencing data: practical implementations.

Katarzyna Górczak1,2, Tomasz Burzykowski1,3,4, Jürgen Claesen1,5

  • 1Data Science Institute, Hasselt University, Hasselt 3500, Belgium.

Bioinformatics Advances
|July 15, 2025
PubMed
Summary

This study presents a hierarchical negative-binomial model to analyze overdispersed, correlated biological count data from complex experiments. Software implementations are discussed for practical application in biomedical research.

More Related Videos

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

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

Published on: December 10, 2012

11.4K
Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
08:03

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

2.3K

Related Experiment Videos

Last Updated: Sep 15, 2025

Rare Event Detection Using Error-corrected DNA and RNA Sequencing
10:36

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Published on: August 3, 2018

12.2K
A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

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

Published on: December 10, 2012

11.4K
Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
08:03

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

2.3K

Area of Science:

  • Bioinformatics and Computational Biology
  • Statistical Genomics
  • Biomedical Data Analysis

Background:

  • High-throughput techniques generate biological read counts for downstream analysis.
  • Complex experimental designs yield correlated count data from matched or longitudinal samples.
  • Biological count data often exhibit overdispersion (variance > mean).

Purpose of the Study:

  • To address the challenge of analyzing overdispersed and correlated count data in complex biological experiments.
  • To propose and evaluate a hierarchical negative-binomial model for such data.
  • To explore practical software implementations of the proposed statistical model.

Main Methods:

  • Utilized a hierarchical negative-binomial regression model.
  • Incorporated normally distributed random effects to capture within- and between-sample correlations.
  • Focused on assessing various software packages for model implementation.

Main Results:

  • The hierarchical negative-binomial model effectively accounts for correlation and overdispersion in count data.
  • Demonstrated the feasibility of applying this model to complex experimental designs.
  • Identified and discussed available software tools for practical use.

Conclusions:

  • Hierarchical models provide a robust framework for analyzing complex, correlated, and overdispersed biological count data.
  • The negative-binomial distribution with random effects is suitable for this type of data.
  • Availability of software facilitates the application of these advanced statistical methods in biological research.