| Title: | A Random-Forest-Based Approach for Imputing Clustered Incomplete Data |
| Version: | 1.0 |
| Date: | 2016-04-05 |
| Author: | Jiebiao Wang and Lin S. Chen |
| Maintainer: | Jiebiao Wang <randel.wang@gmail.com> |
| Description: | It offers random-forest-based functions to impute clustered incomplete data. The package is tailored for but not limited to imputing multitissue expression data, in which a gene's expression is measured on the collected tissues of an individual but missing on the uncollected tissues. |
| License: | GPL-2 | GPL-3 [expanded from: GPL] |
| Depends: | doParallel, randomForest, lme4, foreach |
| URL: | https://github.com/randel/MixRF |
| BugReports: | https://github.com/randel/MixRF/issues |
| RoxygenNote: | 5.0.1 |
| NeedsCompilation: | no |
| Packaged: | 2016-04-06 02:05:06 UTC; JWang |
| Repository: | CRAN |
| Date/Publication: | 2016-04-06 09:43:04 |
A random-forest-based algorithm for imputing clustered incomplete data
Description
This package offers random-forest-based functions to impute clustered incomplete data. The package is tailored for but not limited to imputing multitissue expression data, in which a gene's expression is measured on the collected tissues of an individual but missing on the uncollected tissues.
Details
| Package: | MixRF |
| Type: | Package |
| Version: | 1.0 |
| Date: | 2016-04-05 |
| License: | GPL |
| LazyLoad: | yes |
Author(s)
Jiebiao Wang and Lin S. Chen
Maintainer: Jiebiao Wang <randel.wang@gmail.com>
References
Wang, J., Gamazon, E.R., Pierce, B.L., Stranger, B.E., Im, H.K., Gibbons, R.D., Cox, N.J., Nicolae, D.L. and Chen, L.S. (2016) Imputing gene expression in uncollected tissues within and beyond GTEx. http://dx.doi.org/10.1016/j.ajhg.2016.02.020
See Also
Mixed Random Forest
Description
The function to fit a random forest with random effects.
Usage
MixRF(Y, X, random, data, initialRandomEffects = 0, ErrorTolerance = 0.001,
MaxIterations = 1000)
Arguments
Y |
The outcome variable. |
X |
A data frame or matrix contains the predictors. |
random |
A string in lme4 format indicates the random effect model. |
data |
The data set as a data frame. |
initialRandomEffects |
The initial values for random effects. |
ErrorTolerance |
The tolerance for log-likelihood. |
MaxIterations |
The maximum iteration times. |
Value
A list contains the random forest ($forest), mixed model ($MixedModel), and random effects ($RandomEffects). See the example below for the usage.
Examples
data(sleepstudy)
tmp = MixRF(Y = sleepstudy$Reaction, X = as.data.frame(sleepstudy$Days),
random = "(Days|Subject)", data = sleepstudy, initialRandomEffects = 0,
ErrorTolerance = 0.01, MaxIterations = 100)
# tmp$forest
# tmp$MixedModel
# tmp$RandomEffects
Impute a large number of genes using the MixRF algorithm with parallel computing
Description
This function impute the expression of a large number of genes using the MixRF algorithm with parallel computing.
Usage
MixRF.impute(Ydat, eqtl.lis, snp.dat, cov = NULL, iPC = TRUE,
idx.selected.gene.iPC = NULL, parallel.size = 1, correlation = FALSE,
nCV = 3)
Arguments
Ydat |
An array of expression data of dimension sample-by-gene-by-tissue, nxpxT, where n is sample size. p is the number of genes, and T is the number of tissues. Ydat[,1,] is a matrix of the first gene expression in T tissues for n individuals, nxT. Ydat[,,1] is a nxp matrix of the expression data of p genes in the first tissue. |
eqtl.lis |
A list of eQTL names of length p. Each element in the list contains the name of the eQTLs for the corresponding gene. The order of the list should correspond to the order of genes in Ydat. The code and example to calculate eQTLs can be found at https://github.com/randel/MixRF/blob/master/R/eqtl.r. |
snp.dat |
A matrix of genotype. Each row is a sample and each column corresponds to one SNP. The column names should match eqtl.lis. |
cov |
A matrix of covariates. Each row is a sample and each column corresponds to one covariate. For example, age, gender. |
iPC |
An option. When it is TRUE, the imputed PCs (iPCs) for each tissue type will be constructed based on the combined observed and imputed data on the selected genes. The iPCs will be adjusted as covariates in the imputation. |
idx.selected.gene.iPC |
The option is used only when iPC=TRUE. When it is, one may select a subset of genes and impute those first to construct iPCs. |
parallel.size |
A numerical value specifying the number of CPUs/cores/processors available for parallel computing. |
correlation |
The option to calculate the imputation correlation using cross-validation or not. The default is FALSE. |
nCV |
The option is used only when correlation=TRUE. The number of folds for cross-validation. The default is 3 folds. |
Value
An nxpxT array of imputed and observed expression data. The observed values in Ydat are still kept and the missing values in Ydat are imputed. When the user chooses to calculate the imputation correlation using cross-validation (correlation=TRUE), the estimated imputation correlation (cor) will also be returned in a list together with the imputed data (Yimp).
Examples
## Not run:
data(sim)
idx.selected.gene.iPC = which(sapply(sim$eqtl.lis, length) >= 1)
Yimp = MixRF.impute(sim$Ydat, sim$eqtl.lis, sim$snp.dat, sim$cov, iPC = TRUE,
idx.selected.gene.iPC, parallel.size = 4)
## End(Not run)
Simulated data list
Description
This simulated data list is for demonstration.
Value
Ydat |
An array of expression data of dimension sample-by-gene-by-tissue, nxpxT, where n is sample size. p is the number of genes, and T is the number of tissues. Ydat[,1,] is a matrix of the first gene expression in T tissues for n individuals, nxT. Ydat[,,1] is a nxp matrix of the expression data of p genes in the first tissue. |
eqtl.lis |
A list of eQTL names of length p. Each of the element in the list contains the name of the eQTLs for the corresponding gene. The order of the list should correspond to the order of genes in Ydat. |
snp.dat |
A matrix of genotype. Each row is a sample and each column corresponds to one SNP. The column names should match eqtl.lis. |
cov |
A matrix of covariates. Each row is a sample and each column corresponds to one covariate. For example, age, gender. |