KODAMA (version 2.4)

KODAMA.visualization: Visualization of KODAMA output

Description

Provides a simple function to transform the KODAMA dissimilarity matrix in a low-dimensional space.

Usage

KODAMA.visualization(kk,
                     method=c("t-SNE","MDS","UMAP"),
                     perplexity=min(30,floor((kk$knn_Armadillo$neighbors+1)/3)-1), 
                     ...)

Value

The function returns a matrix contains the coordinates of the datapoints in a low-dimensional space.

Arguments

kk

output of KODAMA.matrix function.

method

method to be considered for transforming the dissimilarity matrix in a low-dimensional space. Choices are "t-SNE", "MDS", and "UMAP".

perplexity

Perplexity parameter. (optimal number of neighbors) for "t-SNE" only.

...

other parameters for "t-SNE", "MDS", or "UMAP".

Author

Stefano Cacciatore and Leonardo Tenori

References

Cacciatore S, Luchinat C, Tenori L
Knowledge discovery by accuracy maximization.
Proc Natl Acad Sci U S A 2014;111(14):5117-22. doi: 10.1073/pnas.1220873111. Link

Cacciatore S, Tenori L, Luchinat C, Bennett PR, MacIntyre DA
KODAMA: an updated R package for knowledge discovery and data mining.
Bioinformatics 2017;33(4):621-623. doi: 10.1093/bioinformatics/btw705. Link

L.J.P. van der Maaten and G.E. Hinton.
Visualizing High-Dimensional Data Using t-SNE.
Journal of Machine Learning Research 9 (Nov) : 2579-2605, 2008.

L.J.P. van der Maaten.
Learning a Parametric Embedding by Preserving Local Structure.
In Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS), JMLR W&CP 5:384-391, 2009.

McInnes L, Healy J, Melville J.
Umap: Uniform manifold approximation and projection for dimension reduction.
arXiv preprint:1802.03426. 2018 Feb 9.

See Also

KODAMA.visualization

Examples

# \donttest{

 data(iris)
 data=iris[,-5]
 labels=iris[,5]
 kk=KODAMA.matrix(data,FUN="KNN",f.par=2)
 cc=KODAMA.visualization(kk,"t-SNE")
 plot(cc,col=as.numeric(labels),cex=2)

# }