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

Poisson Probability Distribution01:09

Poisson Probability Distribution

11.7K
A Poisson probability distribution is a discrete probability distribution. It gives the probability of a number of events occurring in a fixed interval of time or space if these events happen at a known average rate and independently of the time since the last event. For example, a book editor might be interested in the number of words spelled incorrectly in a particular book. It might be that, on average, there are five words spelled incorrectly in 100 pages. The interval is 100 pages.
The...
11.7K
Cluster Sampling Method01:20

Cluster Sampling Method

14.2K
Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
14.2K
Vesicular Tubular Clusters01:45

Vesicular Tubular Clusters

3.1K
After budding out from the ER membrane, some COPII vesicles lose their coat and fuse with one another to form larger vesicles and interconnected tubules called vesicular tubular clusters or VTCs. These clusters constitute a compartment at the ER-Golgi interface known as ERGIC (Endoplasmic Reticulum Golgi Intermediate Compartment). The ERGIC is a mobile membrane-bound cargo transport system that sorts proteins secreted from ER and delivers them to the Golgi.
With the help of motor proteins such...
3.1K
Maxwell-Boltzmann Distribution: Problem Solving01:20

Maxwell-Boltzmann Distribution: Problem Solving

2.8K
Individual molecules in a gas move in random directions, but a gas containing numerous molecules has a predictable distribution of molecular speeds, which is known as the Maxwell-Boltzmann distribution, f(v).
This distribution function f(v) is defined by saying that the expected number N (v1,v2) of particles with speeds between v1 and v2 is given by
2.8K
Data: Types and Distribution01:19

Data: Types and Distribution

1.6K
In biostatistics, data are the observations collected for analysis. There are two main types: parametric and non-parametric. Parametric data, which include continuous (e.g., weight) and discrete numerical data (e.g., number of tablets), assume a particular distribution pattern, often the normal distribution. Non-parametric data do not adhere to a specific distribution and typically comprise nominal (e.g., gender) and ordinal categorical data (e.g., pain scale ratings).
Distributions in...
1.6K
Maxwell's Equation Of Electromagnetism01:29

Maxwell's Equation Of Electromagnetism

4.0K
James Clerk Maxwell (1831–1879) was one of the major contributors to physics in the nineteenth century. Although he died young, he made major contributions to the development of the kinetic theory of gases, to the understanding of color vision, and to understanding the nature of Saturn's rings. He is probably best known for having combined existing knowledge on the laws of electricity and magnetism with his insights into a complete overarching electromagnetic theory, which is...
4.0K

You might also read

Related Articles

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

Sort by
Same author

Sociodemographic trends in prostate cancer: Insights from the all of Us research program.

JNCI cancer spectrum·2026
Same author

Meta-analysis of survival by phased-variant ctDNA and PET response in large B-cell lymphoma.

Blood advances·2026
Same author

Sustained mucosal delivery of SAMT-247 via an intravaginal ring reduces the risk of SIV<sub>mac251</sub> acquisition in vaccinated macaques.

Cell reports. Medicine·2026
Same author

A Novel Bioinformatics Pipeline and a Machine-Learning Approach for Antimicrobial Resistance Phenotypic Prediction.

Bioinformatics and biology insights·2026
Same author

Unpacking X (formerly Twitter) discourse on fluoride and related topics during the 2024 US presidential election.

Journal of the American Dental Association (1939)·2026
Same author

Nonparametric estimation of a state entry time distribution conditional on a "past" state occupation in a progressive multistate model with current status data.

Lifetime data analysis·2026

Related Experiment Video

Updated: Jan 25, 2026

Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore
06:01

Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore

Published on: December 12, 2019

8.9K

Analyzing clustered count data with a cluster specific random effect zero-inflated Conway-Maxwell-Poisson

Hyoyoung Choo-Wosoba1, Somnath Datta2

  • 1Department of Bioinformatics and Biostatistics, University of Louisville, Louisville, KY 40202, U.S.A.

Journal of Applied Statistics
|May 14, 2019
PubMed
Summary

This study introduces a flexible statistical model for biological count data, handling under- and overdispersion common in genetic sequencing. The new zero-inflated Conway-Maxwell-Poisson regression with random effects improves analysis of complex biological data.

Keywords:
Gaussian-Hermite (G-H) quadratureMixed effects modelNext- generation sequencing (NGS) dataPoisson distributionUnder- and over-dispersions

More Related Videos

Spatial Separation of Molecular Conformers and Clusters
10:37

Spatial Separation of Molecular Conformers and Clusters

Published on: January 9, 2014

11.7K
Differentiation of Human Pluripotent Stem Cells into Insulin-Producing Islet Clusters
08:41

Differentiation of Human Pluripotent Stem Cells into Insulin-Producing Islet Clusters

Published on: June 23, 2023

4.3K

Related Experiment Videos

Last Updated: Jan 25, 2026

Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore
06:01

Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore

Published on: December 12, 2019

8.9K
Spatial Separation of Molecular Conformers and Clusters
10:37

Spatial Separation of Molecular Conformers and Clusters

Published on: January 9, 2014

11.7K
Differentiation of Human Pluripotent Stem Cells into Insulin-Producing Islet Clusters
08:41

Differentiation of Human Pluripotent Stem Cells into Insulin-Producing Islet Clusters

Published on: June 23, 2023

4.3K

Area of Science:

  • Biostatistics
  • Genomics
  • Statistical Modeling

Background:

  • Biological and medical research often involves count data with excessive zeros.
  • Standard count distributions like Poisson and negative binomial have limitations in modeling dispersion.
  • The Conway-Maxwell-Poisson (CMP) distribution offers flexibility for under- and overdispersed data.

Purpose of the Study:

  • To develop and evaluate a novel statistical regression model for zero-inflated count data.
  • To address underdispersion and overdispersion simultaneously in genetic data analysis.
  • To incorporate cluster-specific random effects for clustered count data.

Main Methods:

  • Development of a zero-inflated Conway-Maxwell-Poisson (CMP) regression model.
  • Implementation of Gaussian quadrature for numerical approximation of the likelihood.
  • Introduction of a cluster-specific random effect term for handling clustered observations.
  • Development of a statistical test for zero-inflation.

Main Results:

  • The proposed model effectively handles both underdispersion and overdispersion in genetic data.
  • Simulations demonstrate good finite sample properties of the estimators and the zero-inflation test.
  • The methodology is successfully applied to analyze next-generation sequencing data from maize hybrids.

Conclusions:

  • The developed zero-inflated CMP regression model with random effects provides a robust framework for analyzing complex biological count data.
  • This approach enhances the analysis of genetic data, particularly from next-generation sequencing, by accommodating various dispersion patterns and data structures.
  • The study offers a valuable tool for researchers dealing with overdispersed, underdispersed, and zero-inflated count data in biological and medical fields.