| tsne2clus {MOSS} | R Documentation |
t-Stochastic Neighbor Embedding to Clusters
Description
Finds clusters on a 2 dimensional map using Density-based spatial clustering of applications with noise (DBSCAN; Esther et al. 1996).
Usage
tsne2clus(
S.tsne,
ann = NULL,
labels,
aest = NULL,
eps_res = 100,
eps_range = c(0, 4),
min.clus.size = 10,
group.names = "Groups",
xlab = "x: tSNE(X)",
ylab = "y: tSNE(X)",
clus = TRUE
)
Arguments
S.tsne |
Outcome of function "pca2tsne" |
ann |
Subjects' annotation data. An incidence matrix assigning subjects to classes of biological relevance. Meant to tune cluster assignation via Biological Homogeneity Index (BHI). If ann=NULL, the number of clusters is tuned with the Silhouette index instead of BHI. Defaults to NULL. |
labels |
Character vector with labels describing subjects. Meant to assign aesthetics to the visual display of clusters. |
aest |
Data frame containing points shape and color. Defaults to NULL. |
eps_res |
How many eps values should be explored between the specified range? |
eps_range |
Vector containing the minimum and maximum eps values to be explored. Defaults to c(0, 4). |
min.clus.size |
Minimum size for a cluster to appear in the visual display. Defaults to 10 |
group.names |
The title for the legend's key if 'aest' is specified. |
xlab |
Name of the 'xlab'. Defaults to "x: tSNE(X)" |
ylab |
Name of the 'ylab'. Defaults to "y: tSNE(X)" |
clus |
Should we do clustering? Defaults to TRUE. If false, only point aesthetics are applied. |
Details
The function takes the outcome of pca2tsne (or a list containing any two-columns matrix) and finds clusters via DBSCAN. It extends code from the MEREDITH (Taskesen et al. 2016) and clValid (Datta & Datta, 2018) R packages to tune DBSCAN parameters with Silhouette or Biological Homogeneity indexes.
Value
A list with the results of the DBSCAN clustering and (if argument 'plot'=TRUE) the corresponding graphical displays.
dbscan.res: a list with the results of the (sparse) SVD, containing:
cluster: Cluster partition.
eps: Optimal eps according to the Silhouette or Biological Homogeneity indexes criteria.
SIL: Maximum peak in the trajectory of the Silhouette index.
BHI: Maximum peak in the trajectory of the Biological Homogeneity index.
clusters.plot: A ggplot object with the clusters' graphical display.
References
Ester, Martin, Martin Ester, Hans-Peter Kriegel, Jorg Sander, and Xiaowei Xu. 1996. "A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise," 226_231.
Hahsler, Michael, and Matthew Piekenbrock. 2017. "Dbscan: Density Based Clustering of Applications with Noise (DBSCAN) and Related Algorithms." https://cran.r-project.org/package=dbscan.
Datta, Susmita, and Somnath Datta. 2006. Methods for Evaluating Clustering Algorithms for Gene Expression Data Using a Reference Set of Functional Classes. BMC Bioinformatics 7 (1). BioMed Central:397.
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)
library(viridis)
library(cluster)
library(annotate)
# Using the 'iris' data tow show cluster definition via BHI criterion.
set.seed(42)
data(iris)
# Scaling columns.
X <- scale(iris[, -5])
# Calling pca2tsne to map the three variables onto a 2-D map.
Z <- pca2tsne(X, perp = 30, n.samples = 1, n.iter = 1000)
# Using 'species' as previous knoledge to identify clusters.
ann <- model.matrix(~ -1 + iris[, 5])
# Getting clusters.
tsne2clus(Z,
ann = ann,
labels = iris[, 5],
aest = aest.f(iris[, 5]),
group.names = "Species",
eps_range = c(0, 3)
)
# Example of usage within moss.
set.seed(43)
sim_blocks <- simulate_data()$sim_blocks
out <- moss(sim_blocks[-4],
tSNE = TRUE,
cluster = list(eps_range = c(0, 4), eps_res = 100, min_clus_size = 1),
plot = TRUE
)
out$clus_plot
out$clusters_vs_PCs