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

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

1.0K
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...
1.0K
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

3.4K
Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
3.4K
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

182
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...
182

You might also read

Related Articles

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

Sort by
Same author

Theoretical Guarantees for Sparse Principal Component Analysis based on the Elastic Net.

IEEE transactions on information theory·2025
Same author

Integrating axis quantitative trait loci looks beyond cell types and offers insights into brain-related traits.

Nature communications·2025
Same author

Safety and efficacy of AirSeal® continuous pressure insufflator for pneumoperitoneum maintenance in minimally invasive pediatric urologic surgery.

Journal of pediatric urology·2025
Same author

Association of Hospital, Surgical, and Patient Factors With Cost of Pyeloplasty Admission in Children: Retrospective Analysis of the Pediatric Health Information System.

Urology practice·2025
Same author

A LATENT VARIABLE MIXTURE MODEL FOR COMPOSITION-ON-COMPOSITION REGRESSION WITH APPLICATION TO CHEMICAL RECYCLING.

The annals of applied statistics·2025
Same author

NONLINEAR GLOBAL FRÉCHET REGRESSION FOR RANDOM OBJECTS VIA WEAK CONDITIONAL EXPECTATION.

Annals of statistics·2025

Related Experiment Video

Updated: Dec 14, 2025

Tick Microbiome Characterization by Next-Generation 16S rRNA Amplicon Sequencing
07:21

Tick Microbiome Characterization by Next-Generation 16S rRNA Amplicon Sequencing

Published on: August 25, 2018

13.3K

Compositional knockoff filter for high-dimensional regression analysis of microbiome data.

Arun Srinivasan1, Lingzhou Xue1, Xiang Zhan2

  • 1Department of Statistics, Pennsylvania State University, University Park, Pennsylvania.

Biometrics
|July 20, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a novel two-step filter for analyzing microbiome data, effectively identifying microbial taxa associated with host gene expression while controlling false discoveries. The method enhances microbiome fine-mapping and aids in understanding host-microbe interactions.

Keywords:
FDR controlcompositional constraintcompositional screeningknockoff filterlog-contrast modelmicrobiome

More Related Videos

Assisted Selection of Biomarkers by Linear Discriminant Analysis Effect Size LEfSe in Microbiome Data
04:57

Assisted Selection of Biomarkers by Linear Discriminant Analysis Effect Size LEfSe in Microbiome Data

Published on: May 16, 2022

16.9K
Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks
09:49

Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks

Published on: September 25, 2021

4.7K

Related Experiment Videos

Last Updated: Dec 14, 2025

Tick Microbiome Characterization by Next-Generation 16S rRNA Amplicon Sequencing
07:21

Tick Microbiome Characterization by Next-Generation 16S rRNA Amplicon Sequencing

Published on: August 25, 2018

13.3K
Assisted Selection of Biomarkers by Linear Discriminant Analysis Effect Size LEfSe in Microbiome Data
04:57

Assisted Selection of Biomarkers by Linear Discriminant Analysis Effect Size LEfSe in Microbiome Data

Published on: May 16, 2022

16.9K
Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks
09:49

Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks

Published on: September 25, 2021

4.7K

Area of Science:

  • Microbiome analysis
  • Bioinformatics
  • Statistical genetics

Background:

  • Microbiome data analysis involves exploring associations between scalar responses and numerous microbial taxa.
  • Compositional data at various taxonomic levels present challenges in high-dimensional regression.
  • Fine-mapping the microbiome requires robust methods for identifying relevant microbial taxa.

Purpose of the Study:

  • To propose a two-step compositional knockoff filter for effective false discovery rate (FDR) control.
  • To enable high-dimensional linear log-contrast regression analysis of microbiome compositional data.
  • To identify microbial taxa associated with host gene expressions in an inflammatory bowel disease dataset.

Main Methods:

  • A two-step procedure involving a compositional screening and an extended knockoff filter.
  • The first step removes insignificant taxa while preserving the sum-to-zero constraint.
  • The second step identifies significant taxa within a sparse regression model for compositional data.

Main Results:

  • The proposed method provides effective finite-sample FDR control.
  • Theoretical properties include sure screening and robust false discovery control.
  • Numerical simulations show power gains compared to existing methods while maintaining nominal FDR control.

Conclusions:

  • The two-step compositional knockoff filter is a powerful tool for microbiome association studies.
  • It enables precise identification of microbial taxa influencing host phenotypes.
  • The method has practical applications in understanding diseases like inflammatory bowel disease.