Perform hierarchical clustering to group gene expression data into a dendrogram.
This App organizes gene expression data into a dendrogram (cluster tree) by using distance and linkage criteria to analyze dissimilarity between observations in the data and sorting these observations into the branches of the tree. Agglomerative clustering is used to build a hierarchy of clusters by progressively merging individual clusters into groups. This is useful for understanding the similarities and dissimilarities between sets of data based on their grouping patterns throughout the cluster tree.
Begin by selecting or importing both the expression dataset to analyze and the genome associated with the expression dataset using the Add Data button. Next, provide a name for the output set of clusters. Then define the height to cut the branches of the tree, set the parameters for computing the dissimilarity between sets, and select the hierarchical clustering algorithm to use for the analysis.
The input is a .tsv file with "gene-id" listed in the A1 cell, the gene IDs listed in the A column, the sample/conditions identifiers in the first row, and the expression values that correspond to the gene-ids and sample throughout. For a comprehensive guide to formatting your expression data for import into KBase, see the Data Upload/Download Guide.
Description of hierarchical clustering algorithms:
- hclust (default): Performs a hierarchical cluster analysis using a set of dissimilarities for the number of objects being clustered.
- flashClust: A faster version of the hclust algorithm.
Team members who developed & deployed algorithm in KBase: Paramvir Dehal, Roman Sutormin, Michael Sneddon, Srividya Ramakrishnan, Pavel Novichkov, Keith Keller. For questions, please contact us.
- Arkin AP, Cottingham RW, Henry CS, Harris NL, Stevens RL, Maslov S, et al. KBase: The United States Department of Energy Systems Biology Knowledgebase. Nature Biotechnology. 2018;36: 566. doi: 10.1038/nbt.4163 , https://www.nature.com/articles/nbt.4163
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