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

Binomial Probability Distribution01:15

Binomial Probability Distribution

15.4K
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,...
15.4K
DNA Base Pairing02:27

DNA Base Pairing

33.0K
Erwin Chargaff’s rules on DNA equivalence paved the way for the discovery of base pairing in DNA. Chargaff’s rules state that in a double-stranded DNA molecule,
33.0K
Negative Regulator Molecules01:23

Negative Regulator Molecules

38.3K
Positive regulators allow a cell to advance through cell cycle checkpoints. Negative regulators have an equally important role as they terminate a cell’s progression through the cell cycle—or pause it—until the cell meets specific criteria.
38.3K
Per-Unit Sequence Models01:26

Per-Unit Sequence Models

440
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...
440
VSEPR Theory and the Effect of Lone Pairs04:01

VSEPR Theory and the Effect of Lone Pairs

52.8K
Effect of Lone Pairs of Electrons on Molecule Geometry
52.8K
Transcription Factors02:16

Transcription Factors

82.3K
Tissue-specific transcription factors contribute to diverse cellular functions in mammals. For example, the gene for beta globin, a major component of hemoglobin, is present in all cells of the body. However, it is only expressed in red blood cells because the transcription factors that can bind to the promoter sequences of the beta globin gene are only expressed in these cells. Tissue-specific transcription factors also ensure that mutations in these factors may impair only the function of...
82.3K

You might also read

Related Articles

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

Sort by
Same author

An Exponential Scale Mixture Model for Metatranscriptomics Data with Application to Inflammatory Bowel Disease.

bioRxiv : the preprint server for biology·2026
Same author

Residual-Based Sieve Maximum Full Likelihood Estimation for the Proportional Hazards Model.

Communications in statistics: theory and methods·2026
Same author

Cytoplasmic versus nuclear localization of androgen receptor splice variant 7 as a predictor of benefit from androgen receptor pathway inhibitors in metastatic castration-resistant prostate cancer (PROPHECY trial).

Prostate cancer and prostatic diseases·2026
Same author

Stratifying Risk and Treatment Benefit: A Model Predicting Overall Survival in Men with Metastatic De Novo Hormone-sensitive Prostate Cancer in Trials Investigating Docetaxel (the STOPCAP Collaboration).

European urology focus·2026
Same author

A negative binomial latent factor model for paired microbiome sequencing data.

bioRxiv : the preprint server for biology·2024
Same author

Evaluation of primary HPV-DNA testing in relation to visual inspection methods for cervical cancer screening in rural China: an epidemiologic and cost-effectiveness modelling study.

BMC cancer·2011
Same journal

SNPio: a Python interface for population genomic data processing.

BMC bioinformatics·2026
Same journal

SpaHNR: a spatial domain identification method via sparse attention-based hierarchical node representation and multi-view contrastive learning.

BMC bioinformatics·2026
Same journal

OpenIMC: an open-source platform for analyzing single-cell and spatial proteomics by imaging mass cytometry.

BMC bioinformatics·2026
Same journal

NAP: an open source pipeline for cross-domain microbiome profiling using Nanopore sequencing-derived amplicon data.

BMC bioinformatics·2026
Same journal

SurvGME: an R package for survival analysis with graphical and measurement error models.

BMC bioinformatics·2026
Same journal

SimMapNet: a Bayesian framework for gene regulatory network inference using gene ontology similarities as external hint.

BMC bioinformatics·2026
See all related articles

Related Experiment Video

Updated: Jan 24, 2026

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

13.4K

A negative binomial latent factor model for paired microbiome sequencing data.

Hyotae Kim1, Nazema Y Siddiqui2, Lisa Karstens3

  • 1Department of Biostatistics & Bioinformatics, Duke University, 2424 Erwin Road, Durham, NC, 27705, USA. hyotae.kim@duke.edu.

BMC Bioinformatics
|January 22, 2026
PubMed
Summary
This summary is machine-generated.

Analyzing multi-site microbiome data requires accounting for cross-site dependencies. Our latent factor model captures these associations, improving analysis accuracy and enabling microbiome prediction between body sites.

Keywords:
Bayesian modelingLatent factor modelsPaired microbiome sequencing dataPólya-Gamma augmentation

More Related Videos

Enhanced Reduced Representation Bisulfite Sequencing for Assessment of DNA Methylation at Base Pair Resolution
13:47

Enhanced Reduced Representation Bisulfite Sequencing for Assessment of DNA Methylation at Base Pair Resolution

Published on: February 24, 2015

26.3K
Exploring the Root Microbiome: Extracting Bacterial Community Data from the Soil, Rhizosphere, and Root Endosphere
09:55

Exploring the Root Microbiome: Extracting Bacterial Community Data from the Soil, Rhizosphere, and Root Endosphere

Published on: May 2, 2018

28.1K

Related Experiment Videos

Last Updated: Jan 24, 2026

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

13.4K
Enhanced Reduced Representation Bisulfite Sequencing for Assessment of DNA Methylation at Base Pair Resolution
13:47

Enhanced Reduced Representation Bisulfite Sequencing for Assessment of DNA Methylation at Base Pair Resolution

Published on: February 24, 2015

26.3K
Exploring the Root Microbiome: Extracting Bacterial Community Data from the Soil, Rhizosphere, and Root Endosphere
09:55

Exploring the Root Microbiome: Extracting Bacterial Community Data from the Soil, Rhizosphere, and Root Endosphere

Published on: May 2, 2018

28.1K

Area of Science:

  • Microbiome research
  • Statistical modeling
  • Bioinformatics

Background:

  • Microbiome sequencing data frequently involves multiple body sites.
  • These multi-site data often exhibit inherent dependencies.
  • Existing models may not fully capture these cross-site correlations.

Purpose of the Study:

  • To develop a statistical model for joint analysis of multi-site microbiome data.
  • To capture and leverage underlying cross-site dependencies.
  • To improve accuracy and efficiency in microbiome data analysis.

Main Methods:

  • A latent factor model incorporating shared factors across sites.
  • Modeling common subject effects and cross-site correlations.
  • Utilizing mixtures of latent factors for subject heterogeneity in associations.

Main Results:

  • Ignoring site dependencies leads to significant efficiency loss in regression analysis.
  • The proposed model detected significant covariate associations between vaginal and urine microbiomes in a female urogenital study.
  • These associations were not significant when sites were analyzed separately.

Conclusions:

  • A novel latent factor model for multi-site microbiome data is proposed.
  • The model accurately captures cross-site associations without compromising statistical efficiency.
  • It enhances predictive performance by allowing prediction of microbial abundance between sites.
  • An extended framework enables subject clustering and classification by association strength.