clusterMDS: visualize clustering result using multi-dimensional scaling

Description Usage Arguments Details Value Author(s) References See Also Examples


'clusterMDS' takes clustering result returned by 'clusterCMP' and generate multi-dimensional scaling plot for visualization purpose.


clusterMDS(db, cls, size.cutoff, distmat = NULL, color.vector = NULL,
  cluster.result = 1, dimensions = 2, quiet = FALSE,
  highlight.compounds = NULL, highlight.color = NULL)



The desciptor database


The clustering result returned by 'clusterCMP'.


The cutoff size for clusters considered in this visualization. Clusters of size smaller than the cutoff will not be considered.


A distance matrix that corresponds to the 'db'. If not provided, it will be computed on-the-fly in an efficient manner.


Colors to be used in the plot. If the number of colors in the vector is not enough for the plot, colors will be reused. If not provided, color will be generated and randomly sampled from 'rainbow'.


Used to select the clustering result if multiple clustering results are present in 'cls'.


Dimensionality to be used in visualization. See details.


Whether to supress the progress bar.


A vector of compound IDs, corresponding to compounds to be highlighted in the plot. A highlighted compound is represented as a filled circle.


Color used for highlighted compounds. If not set, a highlighted compounds will have the same color as that used for other compounds in the same cluster.


'clusterMDS' internally calls the 'cmdscale' function to generate a set of points in 2-D for the compounds in selected clusters.Note that for compounds in clusters smaller than the cutoff size, they will not be considered in this calculation - their entries in 'distmat' will be discarded if 'distmat' is provided, and distances involving them will not be computed if 'distmat' is not provided. To determine the value for 'size.cutoff', you can use 'cluster.sizestat' to see the size distribution of clusters. Because 'clusterCMP' function allows you to perform multiple clustering processes simultaneously with different cutoff values, the 'cls' parameter may point to a data frame containing multiple clustering results. The user can use 'cluster.result' to specify which result to use. By default, this is set to 1, and the first clustering result will be used in visualization. Whatever the value is, in interactive mode (described below), all clustering result will be displayed when a compound is selected in the interactive plot. If the colors provided in 'color.vector' are not enough to distinguish clusters by colors, the function will silently reuse the colors, resulting multiple clusters colored in the same color. By default, 'dimensions' is set to 2, and the built-in 'plot' function will be used for plotting. If you need to do 3-Dimensional plotting, set 'dimensions' to 3, and pass the returned value to 3D plot utilities, such as 'scatterplot3d' or 'rggobi'. This package does not perform 3D plot on its own.


This function returns a data frame of MDS coordinates and clustering result. This value can be passed to 3D plot utilities such as 'scatterplot3d' and 'rggobi'.

The last column of the output gives whether the compounds have been clicked in the interactive mode.


Min-feng Zhu <>



See Also

See clusterCMP for cluster compounds using a descriptor database.


apbcl = convSDFtoAP(sdfbcl)
clusters <- clusterCMP(apbcl, cutoff = c(0.5, 0.4))
clusterMDS(apbcl, clusters, size.cutoff = 2, quiet = TRUE)

BioMedR documentation built on July 5, 2019, 9:03 a.m.