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 Experiment Videos

Analyzing multivariate flow cytometric data in aquatic sciences.

S Demers1, J Kim, P Legendre

  • 1Institut Maurice-Lamontagne, Pêches et Océans, Mont-Joli, Québec, Canada.

Cytometry
|January 1, 1992
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Distinct roles of lymphotoxin alpha and the type I tumor necrosis factor (TNF) receptor in the establishment of follicular dendritic cells from non-bone marrow-derived cells.

The Journal of experimental medicine·1998
Same author

Ca2+-channel-dependent and -independent inhibition of exocytosis by extracellular ATP in voltage-clamped rat adrenal chromaffin cells.

Pflugers Archiv : European journal of physiology·1998
Same author

Protein disulfide isomerase as a regulator of chloroplast translational activation.

Science (New York, N.Y.)·1998
Same author

Amplification and sequencing of end fragments from bacterial artificial chromosome clones by single-primer polymerase chain reaction.

Analytical biochemistry·1997
Same author

Phenobarbital alters protein binding to the CYP2B1/2 phenobarbital-responsive unit in native chromatin.

The Journal of biological chemistry·1997
Same author

Brucella abortus arginase and ornithine cyclodeaminase genes are similar to Ti plasmid arginase and ornithine cyclodeaminase.

Biochimica et biophysica acta·1997
Same journal

Measurement of the distribution of red blood cell deformability using an automated rheoscope.

Cytometry·2002
Same journal

Flow cytometric-based isolation of nucleated erythroid cells during maturation: an approach to cell surface antigen studies.

Cytometry·2002
Same journal

Flow cytometric immunophenotyping test for staging/monitoring neuroblastoma patients.

Cytometry·2002
Same journal

Determination of bronchoalveolar lavage leukocyte populations by flow cytometry in patients investigated for respiratory disease.

Cytometry·2002
Same journal

Multicenter clinical experience with flow cytometric method for fetomaternal hemorrhage detection.

Cytometry·2002
Same journal

Separation of live cells in different phases of the cell cycle for gene expression analysis.

Cytometry·2002
See all related articles

Flow cytometry in aquatic ecology now offers advanced analysis. This new method uses clustering to reveal temporal changes in aquatic cell types, enhancing ecological understanding.

Area of Science:

  • Aquatic ecology
  • Analytical chemistry
  • Environmental science

Background:

  • Flow cytometry is a recent tool in aquatic ecology, measuring multiple cell characteristics simultaneously.
  • Traditional analysis methods (histograms, scatter plots) underutilize the multivariate data from flow cytometry.
  • The multivariate potential of flow cytometry data remains largely unexploited in ecological studies.

Purpose of the Study:

  • To present a novel method for analyzing multivariate flow cytometry data in aquatic ecology.
  • To enable the study of ecologically relevant questions using advanced data analysis techniques.
  • To demonstrate the temporal evolution of cell types in aquatic environments.

Main Methods:

  • Clustering algorithms are used to reduce high-dimensional flow cytometry data into a smaller set of categories.

Related Experiment Videos

  • The reduced categorical data allows for multivariate pairwise comparisons between samples.
  • The method is tested on a time-series dataset of aquatic samples.

    • Main Results:

    • The clustering approach effectively categorizes cells based on their multivariate characteristics.
    • Multivariate comparisons reveal significant differences and patterns among samples over time.
    • The temporal evolution of distinct cell types within the aquatic environment can be accurately studied.

    Conclusions:

    • This novel method enhances the analytical capabilities of flow cytometry in aquatic ecology.
    • By reducing data complexity, it facilitates deeper ecological insights into aquatic microbial communities.
    • The approach is valuable for studying temporal dynamics and changes in aquatic cell populations.