Description Usage Arguments Value Author(s) References Examples
est_lucid
estimates an integrated cluster assignment of genetic effects using complete biomarker data with/without disease outcomes. Options to produce sparse solutions for cluster-specific parameter estimates under a circumstance of analyzing high-dimensional data are also provided. An IntClust
object will be produced.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 |
G |
Genetic features, a matrix |
CoG |
Covariates to be included in the G->X path |
Z |
Biomarker data, a matrix, can be incomplete and have missing values |
Y |
Disease outcome, a vector |
CoY |
Covariates to be included in the X->Y path |
useY |
Using Y or not, default is TRUE |
family |
"binary" or "normal" for Y |
K |
Pre-specified # of latent clusters, default is 2 |
Pred |
Flag to compute posterior probability of latent cluster with fitted model, default is TRUE |
initial |
A list of initial model parameters will be returned for integrative clustering |
itr_tol |
A list of tolerance settings will be returned for integrative clustering |
tunepar |
A list of tuning parameters and settings will be returned for integrative clustering |
est_lucid
returns an object of list containing parameters estimates, predicted probability of latent clusters, and other features:
beta |
Estimates of genetic effects, matrix |
mu |
Estimates of cluster-specific biomarker means, matrix |
sigma |
Estimates of cluster-specific biomarker covariance matrix, list |
gamma |
Estimates of cluster-specific disease risk, vector |
pcluster |
Probability of cluster, when G is null |
pred |
Predicted probability of belonging to each latent cluster |
Cheng Peng, Zhao Yang, David V. Conti
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.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | # Integrative clustering without feature selection
set.seed(10)
IntClusFit <- est_lucid(G=G1,Z=Z1,Y=Y1,K=2,family="binary",Pred=TRUE)
## Not run:
# Re-run the model with covariates in the G->X path
IntClusCoFit1 <- est_lucid(G=G1,CoG=CoG,Z=Z1,Y=Y1,K=2,family="binary",Pred=TRUE)
# Re-run the model with covariates in the X->Y path
IntClusCoFit2 <- est_lucid(G=G1,Z=Z1,Y=Y1,CoY=CoY,K=2,family="binary",Pred=TRUE)
# Re-run the model with covariates in both G->X and X->Y paths
IntClusCoFit3 <- est_lucid(G=G1,CoG=CoG,Z=Z1,Y=Y1,CoY=CoY,K=2,family="binary",Pred=TRUE)
# Model fit with incomplete biomarker data and covariates in both G->X & X->Y paths
IntClusCoFit3_Incomp <- est_lucid(G=G1,CoG=CoG,Z=Z1_Incomp,Y=Y1,CoY=CoY,K=2,family="binary")
## End(Not run)
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