ClustPhy is an R package for clustering phylogenetic trees (using PAM
or EM clustering), comparing different clusterings (using gap
statistics), and visualizing the clusters (in a phylogenetic tree or in
a 2D biplot). This package wsas developed using
R version 4.1.1 (2021-08-10), platform:
x86_64-w64-mingw32/x64 (64-bit), running under:
Windows 10 x64 (build 19042).
To install the latest version of the package:
require("devtools")
devtools::install_github("rainali475/ClustPhy", build_vignettes = TRUE)
library("ClustPhy")To run the Shiny app:
runClustPhy()ls("package:ClustPhy")
data(package = "ClustPhy")There are 6 functions available in this package. There are 2 clustering functions: clustPAM and clustEM. They allow users to input phylogenetic trees in newick format either as a character string or a file path and performs clustering via either PAM (k-medoids) or EM (expectation maximization) algorithms. Users can specify the number of clusters they want. The functions plotClustersTree and plotClusters2D can be used to visualize tree clusters on a phylogram or a 2D biplot, respectively. Users can specify whether or not to show a number of designated cluster centers, the symbols used to represent these centers, and the text size for these symbols. plotClusters2D first converts the distance matrix of the tree to a coordinate matrix, then uses principle component analysis to reduce dimensionality of the matrix to plot data points on a 2-dimensional plot. The compareGap function takes as input a distance matrix representation of phylogenetic tree and outputs a set of gap statistics for a range from 1 cluster to k.max clusters. This can be used to select the best clustering scheme for the target tree. The plotGapStat function takes the gap statistics output from compareGap and produces a plot of the gap statistics with a vertical dashed line representing the best number of clusters.
Refer to package vignettes for more details.
browseVignettes("ClustPhy")An overview of the package is illustrated below.
The author of this package is Yuzi (Raina) Li. The clustPAM
function makes use of the PAM clustering function in cluster to find
clusterings and clusteri statistics. The clustEM function makes
use of the mclust package for EM clustering algorithms. The
plotClustersTree uses plot.phylo function from ape.
plotClusters2D uses the algorithm described in
https://math.stackexchange.com/questions/156161/finding-the-coordinates-of-points-from-distance-matrix.
to convert the distance matrix to coordinate matrix. compareGap
uses the clusGap function available in cluster package to calculate
gap statistics. plotGapStat uses factoextra package to plot the
gap statistics and find the best k value.
R Core Team (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/
Kaufman, L., & Rousseeuw, P. J. (2005). Finding groups in data: An introduction to cluster analysis. Wiley.
Legendre17. (1960, August 1). Finding the coordinates of points from distance matrix. Mathematics Stack Exchange. Retrieved November 17, 2021, from https://math.stackexchange.com/questions/156161/finding-the-coordinates-of-points-from-distance-matrix.
Paradis E, Schliep K (2019). “ape 5.0: an environment for modern phylogenetics and evolutionary analyses in R.” Bioinformatics, 35, 526-528.
Maechler M, Rousseeuw P, Struyf A, Hubert M, Hornik K (2021). cluster: Cluster Analysis Basics and Extensions. R package version 2.1.2
Alboukadel Kassambara and Fabian Mundt (2020). factoextra: Extract and Visualize the Results of Multivariate Data Analyses. R package version 1.0.7. https://CRAN.R-project.org/package=factoextra
Scrucca L, Fop M, Murphy TB, Raftery AE (2016). “mclust 5: clustering, classification and density estimation using Gaussian finite mixture models.” The R Journal, 8(1), 289–317. https://doi.org/10.32614/RJ-2016-021.
This package was developed as part of an assessment for 2021 BCB410H: Applied Bioinformatics, University of Toronto, Toronto, CANADA.