Description Usage Arguments Value Author(s) References See Also Examples
Given the matrix consisting of metagenomic counts or measure of dissimilarity between samples, multidimensional scaling models are constructed to visualize the samples in the Euclidean space. Clustering methods such as PAM are used to classify the samples into various clusters to study similarity amongst samples.
1 | GraphMetagen(MDSdata)
|
MDSdata |
A list containing the following items
|
Name |
Name of the model constructed. |
Coords |
A N \times p matrix containing the coordinates of the samples obtained by MDS methods, where N is the number of samples and p is the dimension of the model. |
ClusMem |
A vector which gives the cluster membership of the samples determined using PAM. |
TrueMem |
The vector of true cluster membership provided to the function through |
OptimClust |
A integer value giving the optimal number of clusters determined by |
SilPlot |
A vector of length 2 √{N} - 1, where N is the number of samples. It contains the average silhouette width when the number of clusters is between 2 and 2 √{N}. |
Deepak Nag Ayyala <deepaknagayyala@gmail.com>
Chen, J., et.al. (2012) Associating microbiome composition with environmental covariates using generalized UniFrac distance, Bioinformatics, 28(16).
1 2 3 4 5 6 | ## Not run: data(metagencounts)
X <- list(Contents = metagencounts$Counts, Clust = metagencounts$CommMemshp,
DataType = "Counts", DistType = "Kendall's tau-distance",
Dimensions = c(2,3,4), Norms = c(1,2), Penalty = 0.5, MinClust = 2);
GraphMetagen(X);
## End(Not run)
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