| Title: | Fast Adaptive Spectral Clustering for Single and Multi-View Data |
| Version: | 1.1 |
| Author: | Christopher R John, David Watson |
| Maintainer: | Christopher R John <chris.r.john86@gmail.com> |
| Description: | A self-tuning spectral clustering method for single or multi-view data. 'Spectrum' uses a new type of adaptive density aware kernel that strengthens connections in the graph based on common nearest neighbours. It uses a tensor product graph data integration and diffusion procedure to integrate different data sources and reduce noise. 'Spectrum' uses either the eigengap or multimodality gap heuristics to determine the number of clusters. The method is sufficiently flexible so that a wide range of Gaussian and non-Gaussian structures can be clustered with automatic selection of K. |
| Depends: | R (≥ 3.5.0) |
| License: | AGPL-3 |
| Encoding: | UTF-8 |
| LazyData: | true |
| Imports: | ggplot2, ClusterR, Rfast, diptest |
| Suggests: | knitr |
| VignetteBuilder: | knitr |
| RoxygenNote: | 6.1.1 |
| NeedsCompilation: | no |
| Packaged: | 2020-02-10 08:16:36 UTC; christopher |
| Repository: | CRAN |
| Date/Publication: | 2020-02-10 08:40:02 UTC |
CNN_kernel: fast adaptive density-aware kernel
Description
CNN_kernel: fast adaptive density-aware kernel
Usage
CNN_kernel(mat, NN = 3, NN2 = 7)
Arguments
mat |
Matrix: matrix should have samples as columns and rows as features |
NN |
Numerical value: the number of nearest neighbours to use when calculating local sigma |
NN2 |
Numerical value: the number of nearest neighbours to use when calculating common nearest neighbours |
Value
A kernel matrix
Examples
CNN_kern <- CNN_kernel(blobs[,1:50])
Spectrum: Fast Adaptive Spectral Clustering for Single and Multi-view Data
Description
Spectrum is a self-tuning spectral clustering method for single or multi-view data. Spectrum uses a new type of adaptive density-aware kernel that strengthens connections between points that share common nearest neighbours in the graph. For integrating multi-view data and reducing noise a tensor product graph data integration and diffusion procedure is used. Spectrum analyses eigenvector variance or distribution to determine the number of clusters. Spectrum is well suited for a wide range of data, including both Gaussian and non-Gaussian structures.
Usage
Spectrum(data, method = 1, silent = FALSE, showres = TRUE,
diffusion = TRUE, kerneltype = c("density", "stsc"), maxk = 10,
NN = 3, NN2 = 7, showpca = FALSE, frac = 2, thresh = 7,
fontsize = 18, dotsize = 3, tunekernel = FALSE,
clusteralg = "GMM", FASP = FALSE, FASPk = NULL, fixk = NULL,
krangemax = 10, runrange = FALSE, diffusion_iters = 4,
KNNs_p = 10, missing = FALSE)
Arguments
data |
Data frame or list of data frames: contains the data with points to cluster as columns and rows as features. For multi-view data a list of dataframes is to be supplied with the samples in the same order. |
method |
Numerical value: 1 = default eigengap method (Gaussian clusters), 2 = multimodality gap method (Gaussian/ non-Gaussian clusters), 3 = no automatic method (see fixk param) |
silent |
Logical flag: whether to turn off messages |
showres |
Logical flag: whether to show the results on the screen |
diffusion |
Logical flag: whether to perform graph diffusion to reduce noise (default=TRUE) |
kerneltype |
Character string: 'density' (default) = adaptive density aware kernel, 'stsc' = Zelnik-Manor self-tuning kernel |
maxk |
Numerical value: the maximum number of expected clusters (default=10). This is data dependent, do not set excessively high. |
NN |
Numerical value: kernel param, the number of nearest neighbours to use sigma parameters (default=3) |
NN2 |
Numerical value: kernel param, the number of nearest neighbours to use for the common nearest neigbours (default = 7) |
showpca |
Logical flag: whether to show pca when running on one view |
frac |
Numerical value: optk search param, fraction to find the last substantial drop (multimodality gap method param) |
thresh |
Numerical value: optk search param, how many points ahead to keep searching (multimodality gap method param) |
fontsize |
Numerical value: controls font size of the ggplot2 plots |
dotsize |
Numerical value: controls the dot size of the ggplot2 plots |
tunekernel |
Logical flag: whether to tune the kernel, only applies for method 2 (default=FALSE) |
clusteralg |
Character string: clustering algorithm for eigenvector matrix (GMM or km) |
FASP |
Logical flag: whether to use Fast Approximate Spectral Clustering (for v. high sample numbers) |
FASPk |
Numerical value: the number of centroids to compute when doing FASP |
fixk |
Numerical value: if we are just performing spectral clustering without automatic selection of K, set this parameter and method to 3 |
krangemax |
Numerical value: the maximum K value to iterate towards when running a range of K |
runrange |
Logical flag: whether to run a range of K or not (default=FALSE), puts Kth results into Kth element of list |
diffusion_iters |
Numerical value: number of diffusion iterations for the graph (default=4) |
KNNs_p |
Numerical value: number of KNNs when making KNN graph (default=10, suggested=10-20) |
missing |
Logical flag: whether to impute missing data in multi-view analysis (default=FALSE) |
Value
A list, containing: 1) cluster assignments, in the same order as input data columns 2) eigenvector analysis results (either eigenvalues or dip test statistics) 3) optimal K 4) final similarity matrix 5) eigenvectors and eigenvalues of graph Laplacian
Examples
res <- Spectrum(brain[[1]][,1:50])
8 blob like structures
Description
A simulated dataset of 8 Gaussian blobs. Simulated using the 'clusterlab' CRAN package.
Usage
blobs
Format
A data frame with 10 rows and 800 variables
A brain cancer dataset
Description
A dataset containing The Cancer Genome Atlas expression data. From this publication https://tcga-data.nci.nih.gov/docs/publications/lgggbm_2016/. The first data frame is a 5133X150 RNA-seq data matrix, the second is a 262X150 miRNA-seq data matrix, the third is 45X150 protein array data matrix. The data was all pre-normalised then subject to log transform.
Usage
brain
Format
A list of data frames
Source
https://gdac.broadinstitute.org/
Three concentric circles
Description
Simulated data using the 'clusterSim' CRAN package.
Usage
circles
Format
A data frame with 2 rows and 540 variables
cluster_similarity: cluster a similarity matrix using the Ng method
Description
This function performs clustering of a similarity matrix following the method of Ng or of Melia. We recommend using the Ng method with GMM to cluster the eigenvectors instead of k-means.
Usage
cluster_similarity(A2, k = k, clusteralg = "GMM", specalg = "Ng")
Arguments
A2 |
Data frame or matrix: a similarity matrix |
k |
Numerical value: the number of clusters |
clusteralg |
Character value: GMM or km clustering algorithm (suggested=GMM) |
specalg |
Character value: Ng or Melia variant of spectral clustering (default=Ng) |
Value
A numeric vector of cluster assignments
References
Ng, Andrew Y., Michael I. Jordan, and Yair Weiss. "On spectral clustering: Analysis and an algorithm." Advances in neural information processing systems. 2002.
Meila, Marina, et al. "Spectral Clustering: a Tutorial for the 2010’s." Handbook of Cluster Analysis. CRC Press, 2016. 1-23.
Examples
ng_similarity <- cluster_similarity(missl[[1]],k=8)
estimate_k: estimate K using the eigengap or multimodality gap heuristics
Description
This function will try to estimate K given a similarity matrix. Generally the maximum eigengap is preferred, but on some data examining the distribution of the eigenvectors as in the multimodality gap heuristic may be beneficial.
Usage
estimate_k(A2, maxk = 10, showplots = TRUE)
Arguments
A2 |
Data frame or matrix: a similarity matrix |
maxk |
Numerical value: maximum number of K to be considered |
showplots |
Character value: whether to show the plot on the screen |
Value
A data frame containing the eigenvalues and dip-test statistics of the eigenvectors of the graph Laplacian
Examples
k_test <- estimate_k(missl[[1]])
harmonise_ids: works on a list of similarity matrices to add entries of NA where there are missing observations between views
Description
Simply adds a column and row of NA with the missing ID for data imputation. The similarity matrix requires row and column IDs present for this to work.
Usage
harmonise_ids(l)
Arguments
l |
A list of similarity matrices: those to be harmonised. |
Value
A list of harmonised similarity matrices.
Examples
h_test <- harmonise_ids(missl)
integrate_similarity_matrices: integrate similarity matrices using a tensor product graph linear combination and diffusion technique
Description
Given a list of similarity matrices this function will integrate them running the Shu algorithm, also can reduce noise if the input is a list consisting of a single matrix.
Usage
integrate_similarity_matrices(kernellist, KNNs_p = 10,
diffusion_iters = 4, method = "TPG")
Arguments
kernellist |
A list of similarity matrices: those to be integrated |
KNNs_p |
Numerical value: number of nearest neighbours for KNN graph (default=10, suggested=10-20) |
diffusion_iters |
Numerical value: number of iterations for graph diffusion (default=4, suggested=2-6) |
method |
Character: either TPG (see reference below) or mean (default=TPG) |
Value
An integrated similarity matrix
References
Shu, Le, and Longin Jan Latecki. "Integration of single-view graphs with diffusion of tensor product graphs for multi-view spectral clustering." Asian Conference on Machine Learning. 2016.
Examples
i_test <- integrate_similarity_matrices(misslfilled,method='mean')
kernel_pca: A kernel pca function
Description
kernel_pca: A kernel pca function
Usage
kernel_pca(datam, labels = FALSE, axistextsize = 18,
legendtextsize = 18, dotsize = 3, similarity = TRUE)
Arguments
datam |
Dataframe or matrix: a data frame with samples as columns, rows as features, or a kernel matrix |
labels |
Factor: to label the plot with colours |
axistextsize |
Numerical value: axis text size |
legendtextsize |
Numerical value: legend text size |
dotsize |
Numerical value: dot size |
similarity |
Logical flag: whether the input is a similarity matrix or not |
Value
A kernel PCA plot
Examples
ex_kernel_pca <- kernel_pca(blobs[,1:50], similarity=FALSE)
mean_imputation: mean imputation function for multi-view spectral clustering with missing data
Description
Works on a list of similarity matrices to impute missing values using the mean from the other views.
Usage
mean_imputation(l)
Arguments
l |
A list of data frames: all those to be included in the imputation. |
Value
A list of completed data frames.
Examples
m_test <- mean_imputation(misslfilled)
A list of the blob data as similarity matrices with a missing entry in one
Description
Two copies of a simulated dataset of 8 Gaussian blobs in a list converted to a similarity matrix, but one has a missing observation.
Usage
missl
Format
A list of two data frames
A list of the blob data as similarity matrices with a missing entry in one filled with NAs
Description
Two copies of a simulated dataset of 8 Gaussian blobs in a list converted to a similarity matrix, but one has a missing observation filled with NAs.
Usage
misslfilled
Format
A list of two data frames
ng_kernel: Kernel from the Ng spectral clustering algorithm
Description
This is the kernel from the Ng spectral clustering algorithm. It takes a global sigma which requires tuning for new datasets in most cases. It is possible to use the sigma_finder function to find a sigma for a dataset. Sigma is assumed to be squared already.
Usage
ng_kernel(data, sigma = 0.1)
Arguments
data |
Data frame or matrix: with points as columns, features as rows |
sigma |
Numerical value: a global sigma that controls the drop off in affinity |
Value
A similarity matrix of the input data
References
Ng, Andrew Y., Michael I. Jordan, and Yair Weiss. "On spectral clustering: Analysis and an algorithm." Advances in neural information processing systems. 2002.
Examples
ng_similarity <- ng_kernel(brain[[1]])
pca: A pca function
Description
pca: A pca function
Usage
pca(mydata, labels = FALSE, dotsize = 3, axistextsize = 18,
legendtextsize = 18)
Arguments
mydata |
Data frame or matrix: matrix or data frame with samples as columns, features as rows |
labels |
Factor: to label the plot with colours |
dotsize |
Numerical value: dot size |
axistextsize |
Numerical value: axis text size |
legendtextsize |
Numerical value: legend text size |
Value
A pca plot object
Examples
ex_pca <- pca(blobs[,1:50])
rbfkernel_b: fast self-tuning kernel
Description
rbfkernel_b: fast self-tuning kernel
Usage
rbfkernel_b(mat, K = 3, sigma = 1)
Arguments
mat |
Matrix: matrix should have samples as columns and rows as features |
K |
Numerical value: the number of nearest neighbours to use when calculating local sigma |
sigma |
Numerical value: a global sigma, usually left to 1 which has no effect |
Value
A kernel matrix
Examples
stsc_kern <- rbfkernel_b(blobs[,1:50])
sigma_finder: heuristic to find sigma for the Ng kernel
Description
This is a heuristic to find the sigma for the kernel from the Ng spectral clustering algorithm. It returns a global sigma. It uses the mean K nearest neighbour distances of all samples to determine sigma.
Usage
sigma_finder(mat, NN = 3)
Arguments
mat |
Data frame or matrix: with points as columns, features as rows |
NN |
Numerical value: the number of nearest neighbours to use (default=3) |
Value
A global sigma
Examples
sig <- sigma_finder(blobs)
Two spirals wrapped around one another
Description
Simulated data using the 'mlbench' CRAN package.
Usage
spirals
Format
A data frame with 2 rows and 180 variables