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

Multiple Regression01:25

Multiple Regression

Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
Variability: Analysis01:11

Variability: Analysis

Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
The range is a simple measure of variability, indicating the difference between the highest and...
Performing a Simple Data Analysis using MS-Excel Function01:17

Performing a Simple Data Analysis using MS-Excel Function

Microsoft Excel offers a suite of functions and tools ideal for statistical analysis, making it accessible to students and researchers. This article outlines fundamental Excel functions pivotal for data analysis.
SUM: This function calculates the total sum of a range of values. It's the foundation for aggregating data, essential for determining overall trends and totals in datasets.
AVERAGE: It computes the mean value of a given set of numbers, providing a quick insight into the central...
Manipulation and Analysis01:21

Manipulation and Analysis

GIS manipulation and analysis functions are vital for decision-making and planning. These activities range from data retrieval tasks, such as selecting information based on specific criteria, to advanced analytical techniques that address complex spatial problems.One critical GIS analysis method is overlaying, which combines multiple data layers to examine impacts. For example, overlaying a river-dammed lake boundary with road networks can identify affected infrastructure. Another common...
Combining Functions01:16

Combining Functions

Functions can be combined to form new mathematical models that describe interactions between variables. These combinations are fundamental in understanding relationships between changing quantities and are commonly encountered in scientific and engineering contexts. The combination methods—addition, subtraction, multiplication, division, and composition—each have unique implications for the resulting function’s domain and behavior.When combining functions through arithmetic operations, such...
Methods of Medium Optimization01:28

Methods of Medium Optimization

Optimizing growth media enhances microbial proliferation and maximizes product yield. Statistical experimental design methodologies provide structured and reproducible approaches, offering progressively higher levels of robustness and efficiency.The One-Factor-at-a-Time (OFAT) MethodThe One-Factor-at-a-Time (OFAT) method involves adjusting a single variable while keeping all others constant. However, it cannot detect interactions between variables, often leading to suboptimal outcomes when...

You might also read

Related Articles

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

Sort by
Same author

Effects of 100% oxygen during deferred cord clamping on oxidative stress markers: a sub-study of a randomized controlled trial.

Pediatric research·2026
Same author

A lipidomics roadmap: from basic research to societal challenges.

Nature communications·2026
Same author

Distinct protein and metabolic profiles in patients with advanced clear-cell renal cell carcinoma treated with sunitinib: a study of the Spanish oncology genitourinary group.

Frontiers in oncology·2026
Same author

Author Correction: TORC1 controls G<sub>1</sub>-S cell cycle transition in yeast via Mpk1 and the greatwall kinase pathway.

Nature communications·2026
Same author

3D Glioblastoma Molecular Responses to Carbon Dot-Delivered Riluzole Probed by Synchrotron FTIR.

Analytical chemistry·2026
Same author

Activation of anti-inflammatory pathways by polyunsaturated fatty acid signaling may protect neurodevelopment in children prenatally exposed to methylmercury.

Environmental health : a global access science source·2026
Same journal

Smartphone-assisted fluorescence and colorimetric dual-mode sensor for visual quantitative detection of nitrite and nitrate in real samples.

Analytica chimica acta·2026
Same journal

Folding integrated all-paper photoelectrochemical immunoassay using annealed ZnO for point-of-care detection of ferritin.

Analytica chimica acta·2026
Same journal

Dual-mode electrochemical-SERS detection of chloramphenicol based on dual-signal enhancement.

Analytica chimica acta·2026
Same journal

Multi-screening of beta-lactam antibiotics in milk based on Fe<sub>3</sub>O<sub>4</sub>@phage/bacteria system and aggregation induced emission luminogen.

Analytica chimica acta·2026
Same journal

A porous phosphate-rich β-cyclodextrin polymer for efficient and broad-spectrum enrichment of antibiotics.

Analytica chimica acta·2026
Same journal

Corrigendum to "LUMIN: A novel algorithm for automated mixture quantification using 1D <sup>1</sup>H NMR spectra" [Analytica Chimica Acta 1411 (2026) 345639].

Analytica chimica acta·2026
See all related articles

Related Experiment Video

Updated: Jun 28, 2026

Large Scale Non-targeted Metabolomic Profiling of Serum by Ultra Performance Liquid Chromatography-Mass Spectrometry UPLC-MS
07:34

Large Scale Non-targeted Metabolomic Profiling of Serum by Ultra Performance Liquid Chromatography-Mass Spectrometry UPLC-MS

Published on: March 14, 2013

12.7K

Improving insights from metabolomic functional analysis combining multivariate tools.

Julia Kuligowski1, Marta Moreno-Torres2, Guillermo Quintás3

  • 1Neonatal Research Group, Health Research Institute La Fe (IISLAFE), Avda Fernando Abril Martorell 106, 46026, Valencia, Spain; Primary Care Interventions to Prevent Maternal and Child Chronic Diseases of Perinatal and Developmental Origin Network (RICORS-SAMID), Instituto de Salud Carlos III, Madrid, Spain; Servicio de Análisis de Vesículas Extracelulares (SAVE), Health Research Institute La Fe (IISLAFE), Avda Fernando Abril Martorell 106, 46026, Valencia, Spain.

Analytica Chimica Acta
|August 25, 2024
PubMed
Summary
This summary is machine-generated.

New methods improve metabolomic data interpretation by integrating multivariate and functional analyses. These approaches enhance understanding of biological effects and identify altered pathways for deeper biological insights.

Keywords:
Cluster cross validationFunctional analysisMetabolomicsMultivariate analysisOPLS-DA

More Related Videos

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis
07:11

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

Published on: November 10, 2023

2.2K
Identification and Quantification of Deranged Metabolites in Critically Ill Patients Using NMR-Based Metabolomics
11:02

Identification and Quantification of Deranged Metabolites in Critically Ill Patients Using NMR-Based Metabolomics

Published on: November 29, 2024

500

Related Experiment Videos

Last Updated: Jun 28, 2026

Large Scale Non-targeted Metabolomic Profiling of Serum by Ultra Performance Liquid Chromatography-Mass Spectrometry UPLC-MS
07:34

Large Scale Non-targeted Metabolomic Profiling of Serum by Ultra Performance Liquid Chromatography-Mass Spectrometry UPLC-MS

Published on: March 14, 2013

12.7K
Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis
07:11

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

Published on: November 10, 2023

2.2K
Identification and Quantification of Deranged Metabolites in Critically Ill Patients Using NMR-Based Metabolomics
11:02

Identification and Quantification of Deranged Metabolites in Critically Ill Patients Using NMR-Based Metabolomics

Published on: November 29, 2024

500

Area of Science:

  • Biochemistry
  • Systems Biology
  • Bioinformatics

Background:

  • Metabolomics offers direct insights into biological system functions through comprehensive metabolite analysis.
  • Current analysis methods (univariate, multivariate, pathway analysis) face challenges in integrating results for biological interpretation.
  • This integration gap limits the application of multivariate analysis in metabolomics.

Purpose of the Study:

  • To develop straightforward methods for interpreting results from multivariate and functional metabolomic analyses.
  • To enhance the biological significance assessment of metabolomic data.
  • To overcome limitations in current metabolomic data interpretation.

Main Methods:

  • Utilized p-values from multivariate tests as input for functional analysis.
  • Implemented cluster-CV to evaluate pathway-level impact on multivariate model predictive performance.
  • Analyzed four simulated datasets using univariate tests and Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA).

Main Results:

  • Proposed methods facilitate interpretation of biological effects driving multivariate models.
  • Successfully identified altered metabolic pathways not detectable by univariate analysis alone.
  • Demonstrated enhanced biological insights through integrated statistical and functional analysis.

Conclusions:

  • The developed approaches improve the interpretation of biological effects in multivariate metabolomic models.
  • These methods aid in identifying key metabolic pathways driving observed biological differences.
  • Enhanced understanding of metabolic phenotypes can significantly improve future metabolomic study interpretations.