est_lucid: Fit LUCID model to conduct integrated clustering

In USCbiostats/LUCIDus: Latent Unknown Clustering Integrating Multi-View Data

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
)

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.

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

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

## Not run: 
# 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")


## End(Not run)

USCbiostats/LUCIDus documentation built on Dec. 4, 2022, 3:24 p.m.