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A dendrogram is a diagram representing a tree. This diagrammatic representation is frequently used in different contexts: in hierarchical clustering, it illustrates the arrangement of the clusters produced by the corresponding analyses. [4] in computational biology, it shows the clustering of genes or samples, sometimes in the margins of ...
The estimated cluster configurations can be post-processed in order to identify differentially expressed genes and for generating gene- and sample-wise dendrograms and heatmaps. [60] DiffSplice is a method for differential expression detection and visualization, not dependent on gene annotations. This method is supported on identification of ...
Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some specific sense defined by the analyst) to each other than to those in other groups (clusters).
The turnoff point for a star refers to the point on the Hertzsprung–Russell diagram where it leaves the main sequence after its main fuel is exhausted – the main sequence turnoff. By plotting the turnoff points of individual stars in a star cluster one can estimate the cluster's age .
Cluster of Differentiation. The CD system is commonly used as cell markers in immunophenotyping, allowing cells to be defined based on what molecules are present on their surface. These markers are often used to associate cells with certain immune functions. While using one CD molecule to define populations is uncommon (though a few examples ...
Cluster analysis has placed a group of down regulated genes in the upper left corner. In the field of molecular biology, gene expression profiling is the measurement of the activity (the expression) of thousands of genes at once, to create a global picture of cellular function. These profiles can, for example, distinguish between cells that are ...
The average silhouette of the data is another useful criterion for assessing the natural number of clusters. The silhouette of a data instance is a measure of how closely it is matched to data within its cluster and how loosely it is matched to data of the neighboring cluster, i.e., the cluster whose average distance from the datum is lowest. [8]
The inspiration for this method of community detection is the optimization of modularity as the algorithm progresses. Modularity is a scale value between −1 (non-modular clustering) and 1 (fully modular clustering) that measures the relative density of edges inside communities with respect to edges outside communities.