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
lucidto 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_lucidfunction.est_lucidmay 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::mclustModelNamesfor 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:imputeDatato implement EM Algorithm for Unrestricted General Location Model to impute the missing values in omics data;lodwill 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
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")
}