Cluster Gram
Cluster Gram - Related Videos
09:31
Gram Staining of Bacteria from Environmental Sources
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2023
Source: Laboratories of Dr. Ian Pepper and Dr. Charles Gerba - The University of Arizona Demonstrating Author: Luisa Ikner The spectrum of research in environmental microbiology is broad in scope and application potential. Whether the work is bench-scale with known bacterial isolates, or in the field collecting soil or water samples containing unknown bacterial isolates, the ability to quickly and visually discern culturable populations of interest remains of great import to environmental...
11:33
Microscopy and Staining: Gram, Capsule, and Endospore Staining
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2023
Source: Rhiannon M. LeVeque1, Natalia Martin1, Andrew J. Van Alst1, and Victor J. DiRita1 1 Department of Microbiology and Molecular Genetics, Michigan State University, East Lansing, Michigan, United States of America Bacteria are diverse microorganisms found nearly everywhere on Earth. Many properties help distinguish them from each other, including but not limited to Gram-staining type, shape and arrangement, production of capsule, and formation of spores. To observe these properties, one...
04:02
Generation of Spheroplasts from Gram-Negative Bacteria
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2026
Source: Figueroa, D. et. al. Production and Visualization of Bacterial Spheroplasts and Protoplasts to Characterize Antimicrobial Peptide Localization. J. Vis. Exp. (2018).This video demonstrates the preparation of spheroplasts from rod-shaped, gram-negative bacteria. Bacteria are grown in the presence of an antibiotic to form elongated filaments. These are washed with a hypertonic solution and treated with a lysis buffer to remove the outer cell wall. Hypotonic treatment forms spheroplasts,...
10:37
Spatial Separation of Molecular Conformers and Clusters
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Cited by 3 •
2014
We present a technique that allows the spatial separation of different conformers or clusters present in a molecular beam. An electrostatic deflector is used to separate species by their mass-to-dipole moment ratio, leading to the production of gas-phase ensembles of a single conformer or cluster stoichiometry.
01:20
Cluster Sampling Method
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2023
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...
FAQs
Similarity or distance calculations determine which rows or columns are treated as related. Items with closer similarity or smaller distance are arranged nearer in the hierarchical clustering structure, while less similar items separate. This ordering makes branches reflect relationships in the measured data and helps readers connect numerical comparisons with visible groupings.
Row and column dendrograms provide two complementary views of the same data matrix. Row branches can group genes or other measured features with related profiles, whereas column branches can group biological samples with similar measurements. Examining both structures helps researchers recognize relationships among features and relationships among samples without relying on only one direction of comparison.
The color-coded heat map preserves the measured values while the dendrograms organize them. Color differences can expose coordinated high or low measurements across groups, making patterns easier to see within and between branches. This combination allows researchers to interpret both the relationships among items and the numerical variation underlying those relationships.
A clustergram can be applied to measurements involving biological samples, genes, or other features. In the subject-specific context of biology, it supports comparison of cellular or tissue profiles and examination of genomic, proteomic, and other high-throughput datasets. The same visual approach can therefore connect feature-level patterns with differences among biological samples.
Researchers begin with a data matrix containing measurements for samples and features. They then calculate similarity or distance for the rows and columns, apply hierarchical clustering to organize related items into branching structures, and display the matrix as a color-coded heat map. The resulting visualization can be examined for groups, contrasts, and recurring measurement patterns.
A clustergram is useful when researchers need to explore complex biological variation across many measurements at once. It can reveal groups of co-expressed genes, compare cellular or tissue profiles, and identify patterns in genomic or proteomic data. These observations support exploratory analysis and hypothesis generation, while helping researchers interpret high-throughput datasets in a structured visual form.