LUCIDus (version 2.2.1)

est_lucid: Fit LUCID model to conduct integrated clustering

Description

The Latent Unknown Clustering with Integrated Data (LUCID) performs integrative clustering using multi-view data. LUCID model is estimated via EM algorithm for model-based clustering. It also features variable selection, integrated imputation, bootstrap inference and visualization via Sankey diagram.

Usage

est_lucid(
  G,
  Z,
  Y,
  CoG = NULL,
  CoY = NULL,
  K = 2,
  family = c("normal", "binary"),
  useY = TRUE,
  tol = 0.001,
  max_itr = 1000,
  max_tot.itr = 10000,
  Rho_G = 0,
  Rho_Z_Mu = 0,
  Rho_Z_Cov = 0,
  modelName = NULL,
  seed = 123,
  init_impute = c("mclust", "lod"),
  init_par = c("mclust", "random"),
  verbose = FALSE
)

Value

A list which contains the several features of LUCID, including:

pars

Estimates of parameters of LUCID, including beta (effect of exposure), mu (cluster-specific mean for omics data), sigma (cluster-specific variance-covariance matrix for omics data) and gamma (effect estimate of association between latent cluster and outcome)

K

Number of latent cluster

modelName

Geometric model to estiamte variance-covariance matrix for omics data

likelihood

The log likelihood of the LUCID model

post.p

Posterior inclusion probability (PIP) for assigning observation i to latent cluster j

Z

If missing values are observed, this is the complet dataset for omics data with missing values imputed by LUCID

Arguments

G

Exposures, a numeric vector, matrix, or data frame. Categorical variable should be transformed into dummy variables. If a matrix or data frame, rows represent observations and columns correspond to variables.

Z

Omics data, a numeric matrix or data frame. Rows correspond to observations and columns correspond to variables.

Y

Outcome, a numeric vector. Categorical variable is not allowed. Binary outcome should be coded as 0 and 1.

CoG

Optional, covariates to be adjusted for estimating the latent cluster. A numeric vector, matrix or data frame. Categorical variable should be transformed into dummy variables.

CoY

Optional, covariates to be adjusted for estimating the association between latent cluster and the outcome. A numeric vector, matrix or data frame. Categorical variable should be transformed into dummy variables.

K

Number of latent clusters. An integer greater or equal to 2. User can use lucid to determine the optimal number of latent clusters.

family

Distribution of outcome. For continuous outcome, use "normal"; for binary outcome, use "binary". Default is "normal".

useY

Flag to include information of outcome when estimating the latent cluster. Default is TRUE.

tol

Tolerance for convergence of EM algorithm. Default is 1e-3.

max_itr

Max number of iterations for EM algorithm.

max_tot.itr

Max number of total iterations for est_lucid function. est_lucid may conduct EM algorithm for multiple times if the algorithm fails to converge.

Rho_G

A scalar. This parameter is the LASSO penalty to regularize exposures. If user wants to tune the penalty, use the wrapper function lucid

Rho_Z_Mu

A scalar. This parameter is the LASSO penalty to regularize cluster-specific means for omics data (Z). If user wants to tune the penalty, use the wrapper function lucid

Rho_Z_Cov

A scalar. This parameter is the graphical LASSO penalty to estimate sparse cluster-specific variance-covariance matrices for omics data (Z). If user wants to tune the penalty, use the wrapper function lucid

modelName

The variance-covariance structure for omics data. See mclust::mclustModelNames for details.

seed

An integer to initialize the EM algorithm or imputing missing values. Default is 123.

init_impute

Method to initialize the imputation of missing values in LUCID. "mclust" will use mclust:imputeData to implement EM Algorithm for Unrestricted General Location Model to impute the missing values in omics data; lod will initialize the imputation via relacing missing values by LOD / sqrt(2). LOD is determined by the minimum of each variable in omics data.

init_par

Method to initialize the EM algorithm. "mclust" will use mclust model to initialize parameters; "random" initialize parameters from uniform distribution.

verbose

A flag indicates whether detailed information for each iteration of EM algorithm is printed in console. Default is FALSE.

References

Cheng Peng, Jun Wang, Isaac Asante, Stan Louie, Ran Jin, Lida Chatzi, Graham Casey, Duncan C Thomas, David V Conti, A Latent Unknown Clustering Integrating Multi-Omics Data (LUCID) with Phenotypic Traits, Bioinformatics, btz667, https://doi.org/10.1093/bioinformatics/btz667.

Examples

if (FALSE) {
# use simulated data
G <- sim_data$G
Z <- sim_data$Z
Y_normal <- sim_data$Y_normal
Y_binary <- sim_data$Y_binary
cov <- sim_data$Covariate

# fit LUCID model with continuous outcome
fit1 <- est_lucid(G = G, Z = Z, Y = Y_normal, family = "normal", K = 2, 
seed = 1008)

# fit LUCID model with block-wise missing pattern in omics data
Z_miss_1 <- Z
Z_miss_1[sample(1:nrow(Z), 0.3 * nrow(Z)), ] <- NA
fit2 <- est_lucid(G = G, Z = Z_miss_1, Y = Y_normal, family = "normal", K = 2)

# fit LUCID model with sporadic missing pattern in omics data
Z_miss_2 <- Z
index <- arrayInd(sample(length(Z_miss_2), 0.3 * length(Z_miss_2)), dim(Z_miss_2))
Z_miss_2[index] <- NA
# initialize imputation by imputing 
fit3 <- est_lucid(G = G, Z = Z_miss_2, Y = Y_normal, family = "normal", 
K = 2, seed = 1008, init_impute = "lod") 
LOD
# initialize imputation by mclust
fit4 <- est_lucid(G = G, Z = Z_miss_2, Y = Y, family = "normal", K = 2, 
seed = 123, init_impute = "mclust") 

# fit LUCID model with binary outcome
fit5 <- est_lucid(G = G, Z = Z, Y = Y_binary, family = "binary", K = 2,
seed = 1008)

# fit LUCID model with covariates
fit6 <- est_lucid(G = G, Z = Z, Y = Y_binary, CoY = cov, family = "binary", 
K = 2, seed = 1008)

# use LUCID model to conduct integrated variable selection
# select exposure
fit6 <- est_lucid(G = G, Z = Z, Y = Y_normal, CoY = NULL, family = "normal", 
K = 2, seed = 1008, Rho_G = 0.1)
# select omics data
fit7 <- est_lucid(G = G, Z = Z, Y = Y_normal, CoY = NULL, family = "normal",
K = 2, seed = 1008, Rho_Z_Mu = 90, Rho_Z_Cov = 0.1, init_par = "random")

}