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Related Concept Videos

Cluster Sampling Method01:20

Cluster Sampling Method

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Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
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After budding out from the ER membrane, some COPII vesicles lose their coat and fuse with one another to form larger vesicles and interconnected tubules called vesicular tubular clusters or VTCs. These clusters constitute a compartment at the ER-Golgi interface known as ERGIC (Endoplasmic Reticulum Golgi Intermediate Compartment). The ERGIC is a mobile membrane-bound cargo transport system that sorts proteins secreted from ER and delivers them to the Golgi.
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What is an Experiment?01:12

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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...
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In a beam of charged particles created by a heated cathode, the particles move at different speeds. However, many applications need a beam with uniform particle speeds. An arrangement known as a velocity selector uses electric and magnetic fields to pick particles with a particular speed from the beam.
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When conducting an experiment, it is crucial to have control to reduce bias and accurately measure the dependent variables. It also marks the results more reliable. Controls are elements in an experiment that have the same characteristics as the treatment groups but are not affected by the independent variable. By sorting these data into control and experimental conditions, the relationship between the dependent and independent variables can be drawn. A randomized experiment always includes a...
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Randomized Experiments01:13

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The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
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Related Experiment Video

Updated: Feb 2, 2026

An Integrated Workflow of Identification and Quantification on FDR Control-Based Untargeted Metabolome
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Cluster Analysis of Untargeted Metabolomic Experiments.

Joshua Heinemann1,2

  • 1Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA, USA. josvanhei@gmail.com.

Methods in Molecular Biology (Clifton, N.J.)
|November 14, 2018
PubMed
Summary

Untargeted metabolite profiling using liquid chromatography-mass spectrometry (LC-MS) and unsupervised data analysis reveals distinct metabolic phenotypes. This approach aids in quality control and understanding cellular responses to stress, disease, or environmental factors.

Keywords:
Cluster analysisClusteringData miningPattern recognitionPhenotypingUntargeted metabolomics

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Area of Science:

  • Metabolomics
  • Bioinformatics
  • Systems Biology

Background:

  • Untargeted metabolite profiling identifies unique metabolic phenotypes.
  • These phenotypes can be associated with stress, disease, or environmental exposures.
  • Unsupervised data analysis is crucial for quality control and biological insights.

Purpose of the Study:

  • To demonstrate the utility of unsupervised data analysis in metabolomics.
  • To show how to format untargeted mass spectrometry data for R.
  • To explore the predictive power of data visualization techniques in biological samples.

Main Methods:

  • Liquid chromatography-mass spectrometry (LC-MS) for untargeted metabolite profiling.
  • Data formatting for import into the R statistical environment.
  • Hierarchical clustering and principal component analysis (PCA) for data transformation and visualization.

Main Results:

  • Unsupervised data analysis effectively identifies unique metabolic phenotypes.
  • Visual representations of data using PCA and hierarchical clustering highlight biological sample variations.
  • The methods allow for predictive insights into environmental stress and health.

Conclusions:

  • Unsupervised data analysis is a powerful tool for quality control in metabolomics.
  • Metabolite profiling combined with R-based analysis provides significant biological and health insights.
  • This approach facilitates the understanding of cellular responses to various conditions.