| pca2tsne {MOSS} | R Documentation |
Mapping principal components onto a 2D map via tSNE.
Description
This function is called by moss whenever 'moss(tSNE=TRUE)' to project latent factors onto two dimensions via t-stochastic neighbor embedding (tSNE) However, it can be used on any generic data matrix. The function uses the Barnes-Hut tSNE algorithm from Rtsne package, and uses an iterative procedure to select a tSNE map minimizing the projection cost across several random initial conditions. The function is inspired by the iterative procedure discussed in Taskesen et al. 2016 and code originally provided with the publication.
Usage
pca2tsne(Z, perp = 50, n.samples = 1, n.iter = 1000, parallel = FALSE)
Arguments
Z |
A matrix with axes of variation (typically PCs) as columns and subjects as rows. |
perp |
Perplexity value. Defaults to 50. |
n.samples |
Number of times the algorithm starts from different random initial conditions. Defaults to 1. |
n.iter |
Number of iterations for each run of the algorithm. |
parallel |
Should random starts be done in parallel? Logical. Default to FALSE. Defaults to 1000. |
Value
Returns output of function 'Rtsne::Rtsne' from the random initial condition with the smallest 'reconstruction error'.
References
van der Maaten L, Hinton G. Visualizing Data using t-SNE. J Mach Learn Res. 2008;9: 2579–2605
Krijthe JH. Rtsne: T-Distributed Stochastic Neighbor Embedding using a Barnes-Hut Implementation. 2015
Taskesen, Erdogan, Sjoerd M. H. Huisman, Ahmed Mahfouz, Jesse H. Krijthe, Jeroen de Ridder, Anja van de Stolpe, Erik van den Akker, Wim Verheagh, and Marcel J. T. Reinders. 2016. Pan-Cancer Subtyping in a 2D-Map Shows Substructures That Are Driven by Specific Combinations of Molecular Characteristics. Scientific Reports 6 (1):24949.
Examples
library("MOSS")
sim_blocks <- simulate_data()$sim_blocks
# Example of pca2tsne usage.
Z <- pca2tsne(sim_blocks$`Block 3`,
perp = 50,
n.samples = 1,
n.iter = 1e3)$Y
plot(Z, xlab = "x_tSNE(X)", ylab = "y_tSNE(X)")
# Example of usage within moss.
set.seed(34)
moss(sim_blocks[-4],
tSNE = list(
perp = 50,
n.samples = 1,
n.iter = 1e3
),
plot = TRUE
)$tSNE_plot