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

71
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...
71
Cross-Sectional Research01:50

Cross-Sectional Research

11.4K
In cross-sectional research, a researcher compares multiple segments of the population at the same time. If they were interested in people's dietary habits, the researcher might directly compare different groups of people by age. Instead of following a group of people for 20 years to see how their dietary habits changed from decade to decade, the researcher would study a group of 20-year-old individuals and compare them to a group of 30-year-old individuals and a group of 40-year-old...
11.4K
Longitudinal Studies01:26

Longitudinal Studies

195
Longitudinal studies are also widely used in other medical and social science fields. For instance, in cardiovascular research, they can monitor patients' health over decades to identify risk factors for heart disease, such as high cholesterol or smoking, and evaluate the long-term effectiveness of preventive measures. Similarly, in mental health studies, researchers might follow individuals from adolescence into adulthood to understand the development and progression of conditions like...
195
Longitudinal Research02:20

Longitudinal Research

12.0K
Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again...
12.0K
Observational Studies01:11

Observational Studies

8.8K
Observational studies are a type of analytical study where researchers observe events without any interventions. In other words, the researcher does not influence the response variable or the experiment's outcome.
There are three types of observational studies – Prospective, retrospective, and cross-sectional.
Prospective Study
Prospective studies, also known as longitudinal or cohort studies, are carried out by collecting future data from groups sharing similar characteristics. One...
8.8K

You might also read

Related Articles

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

Sort by
Same author

Utilizing co-abundances of antimicrobial resistance genes to identify potential co-selection in the resistome.

Microbiology spectrum·2024
Same author

Microbiome compositional data analysis for survival studies.

NAR genomics and bioinformatics·2024
Same author

Obesity status and obesity-associated gut dysbiosis effects on hypothalamic structural covariance.

International journal of obesity (2005)·2021
Same author

Microbiota and Metabolomic Patterns in the Breast Milk of Subjects with Celiac Disease on a Gluten-Free Diet.

Nutrients·2021
Same author

Estimating Player Positions from Padel High-Angle Videos: Accuracy Comparison of Recent Computer Vision Methods.

Sensors (Basel, Switzerland)·2021
Same author

Variable selection in microbiome compositional data analysis.

NAR genomics and bioinformatics·2021
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: Aug 7, 2025

Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing
11:22

Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing

Published on: October 15, 2019

28.7K

coda4microbiome: compositional data analysis for microbiome cross-sectional and longitudinal studies.

M Luz Calle1, Meritxell Pujolassos2, Antoni Susin3

  • 1Biosciences Department, Faculty of Sciences, Technology and Engineering, University of Vic - Central University of Catalonia, Carrer de La Laura, 13, 08500, Vic, Spain. malu.calle@uvic.cat.

BMC Bioinformatics
|March 6, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces coda4microbiome, an R package for analyzing microbiome data. It identifies microbial signatures in cross-sectional and longitudinal studies using compositional data analysis, improving predictive accuracy.

Keywords:
Compositional data analysisLog-ratio analysisLongitudinal studiesMicrobial signaturesMicrobiome analysisPenalized regression

More Related Videos

Oral Biofilm Sampling for Microbiome Analysis in Healthy Children
10:42

Oral Biofilm Sampling for Microbiome Analysis in Healthy Children

Published on: December 31, 2017

17.2K
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

4.4K

Related Experiment Videos

Last Updated: Aug 7, 2025

Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing
11:22

Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing

Published on: October 15, 2019

28.7K
Oral Biofilm Sampling for Microbiome Analysis in Healthy Children
10:42

Oral Biofilm Sampling for Microbiome Analysis in Healthy Children

Published on: December 31, 2017

17.2K
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

4.4K

Area of Science:

  • Microbiome research
  • Bioinformatics
  • Statistical modeling

Background:

  • Microbiome data is compositional, requiring specialized analysis to avoid spurious results.
  • Longitudinal microbiome studies present unique challenges due to changing sub-compositions over time.

Purpose of the Study:

  • To develop a novel R package, coda4microbiome, for analyzing microbiome data.
  • To identify predictive microbial signatures in both cross-sectional and longitudinal study designs.
  • To provide tools for robust microbiome data analysis within the Compositional Data Analysis (CoDA) framework.

Main Methods:

  • Utilizes the Compositional Data Analysis (CoDA) framework.
  • Employs penalized regression on all-pairs log-ratios for variable selection.
  • Infers dynamic microbial signatures in longitudinal data using log-ratio trajectories.

Main Results:

  • Identifies microbial signatures as a balance between two groups of taxa.
  • Demonstrates predictive modeling capabilities for microbiome data.
  • Successfully applied to Crohn's disease and infant microbiome datasets.

Conclusions:

  • coda4microbiome offers a new algorithm for microbial signature identification.
  • The R package is available on CRAN with comprehensive documentation and tutorials.
  • Facilitates interpretation of microbiome data through graphical representations.