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

Factorial Design02:01

Factorial Design

15.5K
Factorial Analysis is an experimental design that applies Analysis of Variance (ANOVA) statistical procedures to examine a change in a dependent variable due to more than one independent variable, also known as factors. Changes in worker productivity can be reasoned, for example, to be influenced by salary and other conditions, such as skill level. One way to test this hypothesis is by categorizing salary into three levels (low, moderate, and high) and skills sets into two levels (entry level...
15.5K
Experimental Designs01:16

Experimental Designs

18.6K
An experimental design is a systematic process that allows researchers to evaluate the relationship between dependent and independent variables. There are three widely used types of experimental design - pre-experimental design, true experimental design, and quasi-experimental design. In pre-experimental design, the researcher compares the data before and after some interventions or treatments. The true-experimental design has more than one purposefully created group, a commonly measured...
18.6K
Methods of Medium Optimization01:28

Methods of Medium Optimization

45
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...
45
What is an Experiment?01:12

What is an Experiment?

19.7K
An experiment is a planned activity carried out under controlled conditions. The purpose of an experiment is to investigate the relationship between two variables. When one variable causes change in another, we call the first variable the explanatory or independent variable. The affected variable is called the response or dependent variable. In a randomized experiment, the researcher manipulates values of the explanatory variable and measures the resulting changes in the response variable. The...
19.7K
Response Surface Methodology01:16

Response Surface Methodology

822
Response Surface Methodology (RSM) is a collection of statistical and mathematical techniques used to develop, improve, and optimize processes. It is particularly valuable when many input variables or factors potentially influence a response variable.
The process of RSM involves several key steps:
822
Study Design in Statistics01:15

Study Design in Statistics

10.3K
A study design is a set of techniques that allow a researcher to collect and analyze data from different variables defined for a specific research problem. Statistics is commonly for effective study design and more robust experiments,
Does aspirin reduce the risk of heart attacks? Is one brand of fertilizer more effective at growing roses than another? Is fatigue as dangerous to a driver as the influence of alcohol? Questions like these are answered using randomized experiments with proper...
10.3K

You might also read

Related Articles

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

Sort by
Same author

Challenges and Future Directions in Lipoprotein Measurement for Atherosclerosis Prevention and Treatment.

International journal of molecular sciences·2025
Same author

Distinctive features of lipoprotein profiles in stroke patients.

PloS one·2023
Same author

Theanine, a Tea-Leaf-Specific Amino Acid, Alleviates Stress through Modulation of Npas4 Expression in Group-Housed Older Mice.

International journal of molecular sciences·2023
Same author

Human lipoproteins comprise at least 12 different classes that are lognormally distributed.

PloS one·2022
Same author

Mutations in SARS-CoV-2 are on the increase against the acquired immunity.

PloS one·2022
Same author

Continuous mutation of SARS-CoV-2 during migration via three routes at the beginning of the pandemic.

PeerJ·2022

Related Experiment Video

Updated: Mar 28, 2026

Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study
20:24

Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study

Published on: January 31, 2014

17.3K

Principal component analysis for designed experiments.

Tomokazu Konishi

    BMC Bioinformatics
    |December 19, 2015
    PubMed
    Summary

    This study enhances principal component analysis (PCA) for biological data by improving component generality and interpretability. New methods reduce noise and bias, allowing for more objective and comparable analytical results across experiments.

    Area of Science:

    • Multivariate data analysis in omics and medical research.
    • Statistical methods for biological data interpretation.

    Background:

    • Principal component analysis (PCA) is widely used for dimensionality reduction in complex datasets like transcriptomics and proteomics.
    • Traditional PCA suffers from poor component generality, sensitivity to experimental noise and bias, and difficulties in interpretation.
    • Existing PCA methods do not adequately account for experimental design to manage noise and bias.

    Purpose of the Study:

    • To address the limitations of traditional PCA by introducing modifications for improved generality, objectivity, and interpretability.
    • To enhance the robustness of PCA to experimental noise and bias in biological datasets.
    • To enable cross-experiment comparisons of analytical results.

    Main Methods:

    More Related Videos

    Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow
    08:58

    Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow

    Published on: October 17, 2025

    808
    A Workflow for Lipid Nanoparticle LNP Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models SVEM
    13:54

    A Workflow for Lipid Nanoparticle LNP Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models SVEM

    Published on: August 18, 2023

    6.2K

    Related Experiment Videos

    Last Updated: Mar 28, 2026

    Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study
    20:24

    Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study

    Published on: January 31, 2014

    17.3K
    Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow
    08:58

    Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow

    Published on: October 17, 2025

    808
    A Workflow for Lipid Nanoparticle LNP Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models SVEM
    13:54

    A Workflow for Lipid Nanoparticle LNP Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models SVEM

    Published on: August 18, 2023

    6.2K
  • Utilized training datasets to identify principal axes, ensuring shared validity across experiments.
  • Incorporated experimental design principles to determine the center of rotation for principal axes.
  • Scaled resulting principal components to standardize their units for unified comparison.

    • Main Results:

    • Modified PCA demonstrated improved group separation and enhanced robustness against noise in microarray experiments.
    • Pre-arranged axes facilitated accurate classification of unknown samples.
    • Scaled components and shared axes enabled meaningful comparisons across different experiments, with axes reflecting experimental group characteristics.

    Conclusions:

    • The introduced modifications significantly enhance the generality and objectivity of PCA results.
    • The refined methodology offers improved interpretability of principal axes, facilitating deeper biological insights.
    • The approach transforms PCA into a more robust analytical tool, akin to multiple regression analyses for independent model specification.