Hierarchical Clustering
Hierarchical Clustering - Related Videos
10:37
Spatial Separation of Molecular Conformers and Clusters
0 Views •
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.
06:50
Synthesis of Hierarchical ZnO/CdSSe Heterostructure Nanotrees
0 Views •
2016
Here, we prepare and characterize novel tree-like hierarchical ZnO/CdSSe nanostructures, where CdSSe branches are grown on vertically aligned ZnO nanowires. The resulting nanotrees are a potential material for solar energy conversion and other opto-electronic devices.
12:27
Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
0 Views •
Cited by 3 •
2017
This protocol describes large-scale reconstructions of selective neuronal populations, labeled following retrograde infection with a modified rabies virus expressing fluorescent markers, and independent, unbiased cluster analyses that enable comprehensive characterization of morphological metrics among distinct neuronal subclasses.
01:20
Cluster Sampling Method
0 Views •
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...
01:45
Vesicular Tubular Clusters
0 Views •
2023
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. With the help of motor proteins such...
FAQs
The distance metric determines how similarity or dissimilarity is calculated between observations or clusters. Because the algorithm merges the most similar available groups, changing this metric can alter which observations join first and how the final relationships appear. In immunology, that choice can affect whether patient profiles, immune-cell patterns, or pathogen features form similar groupings.
The linkage rule specifies how similarity is evaluated when comparing clusters rather than individual observations. It therefore influences the sequence of mergers and the shape of the resulting dendrogram. Two analyses using the same biological observations but different linkage rules may emphasize different relationships, so the selected rule is an important part of interpreting group structure.
A dendrogram should be read as a map of relationships across multiple levels, not as a single fixed classification. Early or closely connected branches indicate relationships formed during the clustering sequence, while higher branches represent broader groupings. This structure allows researchers to examine fine-scale and larger-scale organization in complex immune or infection-related datasets.
Researchers first represent observations, such as patients, immune-cell profiles, pathogens, or gene-expression patterns, using relevant features. They then calculate similarities or distances, apply a linkage rule, and generate the nested grouping structure. The resulting dendrogram can be examined to identify related observations and to compare organization across biological datasets.
It is useful when researchers want to discover patient groupings without assigning patients to predefined classes in advance. By organizing profiles according to shared features, the method can reveal patterns that suggest disease subtypes. These groupings provide a way to examine whether patients with similar biological profiles also show distinct patterns within an immunology or infection study.
The method can organize immune-cell profiles, gene-expression patterns, or pathogen-related observations according to shared characteristics. Researchers can use these relationships to compare immune responses, examine similarities among biological samples, and interpret complex datasets. Its relevance extends across immunology and infection because the same framework can analyze different types of observations and features.