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A combination of both clustering approaches, known as biclustering, has been used to simultaneously cluster by genes and cells to find genes that behave similarly within cell clusters. [ 20 ] Clustering methods applied can be K-means clustering , forming disjoint groups or Hierarchical clustering , forming nested partitions.
Compositional bias and low hydrophobic cluster content. No MD (Meta-Disorder predictor) [19] Regions of different "types"; for example, unstructured loops and regions containing few stable intra-chain contacts A neural-network based meta-predictor that uses different sources of information predominantly obtained from orthogonal approaches Yes
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The first two steps are performed individually on each sample and the last step looks at the overlap in all samples. However, the analysis can be run on one sample as well. SplicePlot is a tool for visualizing alternative splicing and the effects of splicing quantitative trait loci (sQTLs) from RNA-seq data. It provides a simple command line ...
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).
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Single-cell sequencing examines the nucleic acid sequence information from individual cells with optimized next-generation sequencing technologies, providing a higher resolution of cellular differences and a better understanding of the function of an individual cell in the context of its microenvironment. [1]
In graph theory, a clustering coefficient is a measure of the degree to which nodes in a graph tend to cluster together. Evidence suggests that in most real-world networks, and in particular social networks, nodes tend to create tightly knit groups characterised by a relatively high density of ties; this likelihood tends to be greater than the average probability of a tie randomly established ...