KODAMA (version 3.0)

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("UMAP", "t-SNE", "MDS"),
                     config=NULL)

Value

The function returns a matrix that 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 into a low-dimensional space. Choices are "t-SNE", "MDS", and "UMAP".

config

object of class umap.config or tsne.config.

Author

Stefano Cacciatore and Leonardo Tenori

References

Abdel-Shafy EA, Kassim M, Vignol A, et al.
KODAMA enables self-guided weakly supervised learning in spatial transcriptomics.
bioRxiv 2025. doi: 10.1101/2025.05.28.656544. tools:::Rd_expr_doi("10.1101/2025.05.28.656544")

Cacciatore S, Luchinat C, Tenori L
Knowledge discovery by accuracy maximization.
Proc Natl Acad Sci U S A 2014;111(14):5117-5122. doi: 10.1073/pnas.1220873111. tools:::Rd_expr_doi("10.1073/pnas.1220873111")

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. tools:::Rd_expr_doi("10.1093/bioinformatics/btw705")

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,ncomp=2)
 cc=KODAMA.visualization(kk,"t-SNE")
 plot(cc,col=as.numeric(labels),cex=2)

# }