Type:	Package
Title:	Fast Integrative Clustering and Feature Selection for High Dimensional Data
Version:	0.1.4
Author:	Abdalkarim Alnajjar [aut, cre, cph], Zihang Lu [aut]
Maintainer:	Abdalkarim Alnajjar <abdalkarim.alnajjar@queensu.ca>
Description:	A variational Bayesian approach for fast integrative clustering and feature selection, facilitating the analysis of multi-view, mixed type, high-dimensional datasets with applications in fields like cancer research, genomics, and more.
License:	MIT + file LICENSE
URL:	https://github.com/AbdalkarimA/iClusterVB
BugReports:	https://github.com/AbdalkarimA/iClusterVB/issues
Depends:	R (≥ 4.0.0)
Imports:	cluster, clustMixType, cowplot, ggplot2, graphics, grDevices, mclust, MCMCpack, mvtnorm, pheatmap, poLCA, Rcpp (≥ 1.0.12), stats, utils, VarSelLCM
Suggests:	knitr, rmarkdown, survival, survminer
LinkingTo:	Rcpp, RcppArmadillo
Encoding:	UTF-8
LazyData:	true
LazyDataCompression:	xz
RoxygenNote:	7.3.2
VignetteBuilder:	knitr
NeedsCompilation:	yes
Packaged:	2024-12-07 22:15:58 UTC; Abdalkarim
Repository:	CRAN
Date/Publication:	2024-12-09 19:50:06 UTC

Generates a heat map based on an iClusterVB object

Description

Generates a heat map based on an iClusterVB object

Usage

chmap(fit, rho = 0.5, cols = NULL, title = NULL, ...)

Arguments

Value

Returns a heat map for each data view.

Examples

# Setting up the data
dat1 <- list(
  gauss_1 = sim_data$continuous1_data[c(1:20, 61:80, 121:140, 181:200), 1:75],
  gauss_2 = sim_data$continuous2_data[c(1:20, 61:80, 121:140, 181:200), 1:75],
  poisson_1 = sim_data$count_data[c(1:20, 61:80, 121:140, 181:200), 1:75])


dist <- c(
  "gaussian", "gaussian",
  "poisson")

fit_iClusterVB <- iClusterVB(
  mydata = dat1,
  dist = dist,
  K = 4,
  initial_method = "VarSelLCM",
  VS_method = 1,
  max_iter = 25
)


# We can set the colors, turn off scaling and set titles

chmap(fit_iClusterVB,
  cols = c("red", "blue", "green", "purple"),
  title = c("Gene Expression", "DNA Methylation", "Copy Number"),
  scale = "none"
)

Fast Integrative Clustering for High-Dimensional Multi-View Data Using Variational Bayesian Inference

Description

iClusterVB offers a novel, fast, and integrative approach to clustering high-dimensional, mixed-type, and multi-view data. By employing variational Bayesian inference, iClusterVB facilitates effective feature selection and identification of disease subtypes, enhancing clinical decision-making.

Usage

iClusterVB(
  mydata,
  dist,
  K = 10,
  initial_method = "VarSelLCM",
  VS_method = 0,
  initial_cluster = NULL,
  initial_vs_prob = NULL,
  initial_fit = NULL,
  initial_omega = NULL,
  input_hyper_parameters = NULL,
  max_iter = 200,
  early_stop = 1,
  per = 10,
  convergence_threshold = 1e-04
)

Arguments

Value

The iClusterVB function creates an object (list) of class iClusterVB. Relevant outputs include:

• Of particular interest is rho, a list of the posterior inclusion probabilities for the features in each of the data views. This is the probability of including a certain predictor in the model, given the observations. This is only available if VS_method = 1.

Note

If any of the data views are "gaussian", please include them first, both in the input data mydata and correspondingly in the distribution vector dist. For example, dist <- c("gaussian","gaussian", "poisson", "multinomial"), and not dist <- c("poisson", "gaussian","gaussian", "multinomial") or dist <- c("gaussian", "poisson", "gaussian", "multinomial")

Examples

# sim_data comes with the iClusterVB package.
dat1 <- list(
  gauss_1 = sim_data$continuous1_data[c(1:20, 61:80, 121:140, 181:200), 1:75],
  gauss_2 = sim_data$continuous2_data[c(1:20, 61:80, 121:140, 181:200), 1:75],
  poisson_1 = sim_data$count_data[c(1:20, 61:80, 121:140, 181:200), 1:75])

dist <- c(
  "gaussian", "gaussian",
  "poisson")

# Note: `max_iter` is a time-intensive step.
# For the purpose of testing the code, use a small value (e.g. 10).
# For more accurate results, use a larger value (e.g. 200).

fit_iClusterVB <- iClusterVB(
  mydata = dat1,
  dist = dist,
  K = 4,
  initial_method = "VarSelLCM",
  VS_method = 1,
  max_iter = 10
)

# We can obtain a summary using the summary() function
summary(fit_iClusterVB)

LAML (Acute Myeloid Leukemia) Data

Description

This is a subset of the LAML (Acute Myeloid Leukemia) data (TCGA, 2013). The Acute Myeloid Leukemia (laml_tcga) datasets were download using the cBioPortal for Cancer Genomics tool (Cerami et al., 2012; Gao et al., 2013). The 170 samples with gene expression data and mutation data were included. Only a subset of the genes was selected, as desribed below. To access the data containing all the genes, please visit: https://github.com/AbdalkarimA/iClusterVB

Usage

data(laml)

Value

Within the data file, there is:

References

Cancer Genome Atlas Research Network, Ley, T. J., Miller, C., Ding, L., Raphael, B. J., Mungall, A. J., Robertson, A., Hoadley, K., Triche, T. J., Jr, Laird, P. W., Baty, J. D., Fulton, L. L., Fulton, R., Heath, S. E., Kalicki-Veizer, J., Kandoth, C., Klco, J. M., Koboldt, D. C., Kanchi, K. L., Kulkarni, S., … Eley, G. (2013). Genomic and epigenomic landscapes of adult de novo acute myeloid leukemia. The New England journal of medicine, 368(22), 2059–2074. https://doi.org/10.1056/NEJMoa1301689

Cerami, E., Gao, J., Dogrusoz, U., Gross, B. E., Sumer, S. O., Aksoy, B. A., Jacobsen, A., Byrne, C. J., Heuer, M. L., Larsson, E., Antipin, Y., Reva, B., Goldberg, A. P., Sander, C., & Schultz, N. (2012). The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer discovery, 2(5), 401–404. https://doi.org/10.1158/2159-8290.CD-12-0095

Gao, J., Aksoy, B. A., Dogrusoz, U., Dresdner, G., Gross, B., Sumer, S. O., Sun, Y., Jacobsen, A., Sinha, R., Larsson, E., Cerami, E., Sander, C., & Schultz, N. (2013). Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Science signaling, 6(269), pl1. https://doi.org/10.1126/scisignal.2004088

Zainul Abidin, F. N., & Westhead, D. R. (2017). Flexible model-based clustering of mixed binary and continuous data: application to genetic regulation and cancer. Nucleic acids research, 45(7), e53. https://doi.org/10.1093/nar/gkw1270

Generates a probability inclusion plot based on an iClusterVB object

Description

Generates a probability inclusion plot based on an iClusterVB object

Usage

piplot(
  fit,
  plot_grid = TRUE,
  ylab = "Probability of Inclusion",
  title = NULL,
  ...
)

Arguments

Value

Returns a probability inclusion plot or plots.

Examples

# Setting up the data
dat1 <- list(
  gauss_1 = sim_data$continuous1_data[c(1:20, 61:80, 121:140, 181:200), 1:75],
  gauss_2 = sim_data$continuous2_data[c(1:20, 61:80, 121:140, 181:200), 1:75],
  poisson_1 = sim_data$count_data[c(1:20, 61:80, 121:140, 181:200), 1:75])


dist <- c(
  "gaussian", "gaussian",
  "poisson")

fit_iClusterVB <- iClusterVB(
  mydata = dat1,
  dist = dist,
  K = 4,
  initial_cluster= c(rep(1, 20), rep(2, 20), rep(3, 20), rep(4, 20)),
  VS_method = 1,
  max_iter = 20
)

piplot(fit_iClusterVB, plot_grid = FALSE)

Generic plot method for 'iClusterVB' objects

Description

Generic plot method for 'iClusterVB' objects

Usage

## S3 method for class 'iClusterVB'
plot(x, ...)

Arguments

Value

Returns an evidence lower bound (ELBO) plot and a barplot of cluster percentages.

Examples

# Setting up the data
dat1 <- list(
  gauss_1 = sim_data$continuous1_data[c(1:20, 61:80, 121:140, 181:200), 1:75],
  gauss_2 = sim_data$continuous2_data[c(1:20, 61:80, 121:140, 181:200), 1:75],
  poisson_1 = sim_data$count_data[c(1:20, 61:80, 121:140, 181:200), 1:75])


dist <- c(
  "gaussian", "gaussian",
  "poisson")

fit_iClusterVB <- iClusterVB(
  mydata = dat1,
  dist = dist,
  K = 4,
  initial_method = "VarSelLCM",
  VS_method = 1,
  max_iter = 25
)

plot(fit_iClusterVB)

Simulated Dataset

Description

The dataset consists of N = 240 individuals and R = 4 data views with different data types. Two of the data views are continuous, one is count, and one is binary. The true number of clusters was set to K = 4, and the cluster proportions were set at \pi_1 = 0.25, \pi_2 = 0.25, \pi_3 = 0.25, \pi_4 = 0.25, such that we have balanced cluster proportions. Each of the data views had p_r = 500 features, r = 1, \dots, 4, but only 50, or 10%, were relevant features that contributed to the clustering, and the rest were noise features that did not contribute to the clustering. In total, there were p = \sum_{r=1}^4 = 2000 features.

For data view 1 (continuous), relevant features were generated from the following normal distributions: \text{N}(10, 1) for Cluster 1, \text{N}(5, 1) for Cluster 2, \text{N}(-5, 1) for Cluster 3, and \text{N}(-10, 1) for Cluster 4, while noise features were generated from \text{N}(0, 1). For data view 2 (continuous), relevant features were generated from the following normal distributions: \text{N}(-10, 1) for Cluster 1, \text{N}(-5, 1) for Cluster 2, \text{N}(5, 1) for Cluster 3, and \text{N}(10, 1) for Cluster 4, while noise features were generated from \text{N}(0, 1). For data view 3 (binary), relevant features were generated from the following Bernoulli distributions: \text{Bernoulli}(0.05) for Cluster 1, \text{Bernoulli}(0.2) for Cluster 2, \text{Bernoulli}(0.4) for Cluster 3, and \text{Bernoulli}(0.6) for Cluster 4, while noise features were generated from \text{Bernoulli}(0.1). For data view 4 (count), relevant features were generated from the following Poisson distributions: \text{Poisson}(50) for Cluster 1, \text{Poisson}(35) for Cluster 2, \text{Poisson}(20) for Cluster 3, and \text{Poisson}(10) for Cluster 4, while noise features were generated from \text{Poisson}(2).

Usage

data(sim_data)

Format

A list containing four datasets, and other elements of interest.

Generic summary method for 'iClusterVB' objects

Description

Generic summary method for 'iClusterVB' objects

Usage

## S3 method for class 'iClusterVB'
summary(object, rho = 0.5, ...)

Arguments

Value

Returns a summary list for an 'agnes' object.

Examples

# Setting up the data
dat1 <- list(
  gauss_1 = sim_data$continuous1_data[c(1:20, 61:80, 121:140, 181:200), 1:75],
  gauss_2 = sim_data$continuous2_data[c(1:20, 61:80, 121:140, 181:200), 1:75],
  poisson_1 = sim_data$count_data[c(1:20, 61:80, 121:140, 181:200), 1:75])

dist <- c(
  "gaussian", "gaussian",
  "poisson")

fit_iClusterVB <- iClusterVB(
  mydata = dat1,
  dist = dist,
  K = 4,
  initial_method = "VarSelLCM",
  VS_method = 1,
  max_iter = 25
)

## S3 method for class 'iClusterVB'
summary(fit_iClusterVB, rho = 0.75)