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

Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

163
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...
163
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

191
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
191
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

957
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
957
RNA-seq03:21

RNA-seq

11.2K
RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...
11.2K
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

861
Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
861

You might also read

Related Articles

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

Sort by
Same author

Evaluation of a serum protein signature as monitoring biomarker for Duchenne muscular dystrophy in a long-term clinical trial with corticosteroids.

Skeletal muscle·2026
Same author

Tracking COVID-19 Severity and Progression Through Amines and Lipid Mediators.

Journal of medical virology·2026
Same author

Creatine/Creatinine Ratio and Myostatin as Biomarkers to Monitor Muscle Function in Duchenne Muscular Dystrophy Patients.

Journal of cachexia, sarcopenia and muscle·2026
Same author

Quantitative tandem mass tag-based serum proteomics for longitudinal biomarker monitoring in Duchenne muscular dystrophy.

Clinical proteomics·2026
Same author

MDBiomarkers: A queryable biomarkers database integrating multiple serum and tissue datasets for Duchenne muscular dystrophy.

Journal of neuromuscular diseases·2026
Same author

Quantification of a serum titin fragment reflects dystrophin restoration in mdx mice and disease severity in patients with dystrophinopathies.

Journal of neuromuscular diseases·2026

Related Experiment Video

Updated: Dec 2, 2025

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.6K

Negative Binomial mixed models estimated with the maximum likelihood method can be used for longitudinal RNAseq data.

Roula Tsonaka1, Pietro Spitali2

  • 1Medical Statistics section, Department of Biomedical Data Sciences, Leiden University Medical Center.

Briefings in Bioinformatics
|November 5, 2020
PubMed
Summary

Accurate numerical integration with adaptive Gaussian quadrature improves maximum likelihood estimation for longitudinal RNAseq data. This method enhances statistical power in small sample settings, crucial for analyzing complex biological time-course experiments.

Keywords:
Adaptive Gaussian quadrature integrationBootstrapNegative Binomial mixed effects modelRandom effects models

More Related Videos

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2
10:10

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

Published on: September 18, 2021

40.0K
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

11.6K

Related Experiment Videos

Last Updated: Dec 2, 2025

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.6K
Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2
10:10

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

Published on: September 18, 2021

40.0K
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

11.6K

Area of Science:

  • Biostatistics
  • Genomics
  • Bioinformatics

Background:

  • Longitudinal RNAseq studies are increasingly common, offering insights into dynamic biological processes.
  • Modeling challenges include serial correlations, missing data, non-linear progression, and small sample sizes.
  • Negative Binomial mixed models are suitable but often face convergence issues with maximum likelihood estimation.

Purpose of the Study:

  • To address convergence issues in Negative Binomial mixed models for time-course RNAseq data.
  • To improve parameter estimation accuracy using robust numerical integration.
  • To ensure reliable statistical inference in small sample settings.

Main Methods:

  • Employed adaptive Gaussian quadrature for accurate integration of random-effects terms.
  • Utilized maximum likelihood estimation for parameter estimation.
  • Applied bootstrap methods to maintain type I error rates in small samples.

Main Results:

  • Accurate integration successfully resolved convergence issues in mixed models.
  • The proposed method demonstrated improved parameter estimation for RNAseq data.
  • Bootstrap method effectively preserved type I error rates, outperforming other approaches in small sample evaluations.

Conclusions:

  • Adaptive Gaussian quadrature is a viable solution for accurate modeling of longitudinal RNAseq data.
  • The enhanced methodology provides reliable statistical analysis for time-course omics studies.
  • This approach is valuable for analyzing complex biological dynamics, such as in Duchenne Muscular Dystrophy research.