Linear mixed model confidence intervals
Fit linear mixed model to estimate contribution of multiple sources of variation while simultaneously correcting for all other variables. Then perform parametric bootstrap sampling to get a 95% confidence intervals for each variable for each gene.
varPartConfInf( exprObj, formula, data, REML = FALSE, useWeights = TRUE, weightsMatrix = NULL, showWarnings = TRUE, colinearityCutoff = 0.999, control = lme4::lmerControl(calc.derivs = FALSE, check.rankX = "stop.deficient"), nsim = 1000, ... )
exprObj |
matrix of expression data (g genes x n samples), or |
formula |
specifies variables for the linear (mixed) model. Must only specify covariates, since the rows of exprObj are automatically used a a response. e.g.: |
data |
|
REML |
use restricted maximum likelihood to fit linear mixed model. default is FALSE. Strongly discourage against changing this option, but here for compatibility. |
useWeights |
if TRUE, analysis uses heteroskedastic error estimates from |
weightsMatrix |
matrix the same dimension as exprObj with observation-level weights from |
showWarnings |
show warnings about model fit (default TRUE) |
colinearityCutoff |
cutoff used to determine if model is computationally singular |
control |
control settings for |
nsim |
number of bootstrap datasets |
... |
Additional arguments for |
A linear mixed model is fit for each gene, and bootMer() is used to generate parametric bootstrap confidence intervals. use.u=TRUE is used so that the
\hat{u}
values from the random effects are used as estimated and are not re-sampled. This gives confidence intervals as if additional data were generated from these same current samples. Conversely, use.u=FALSE assumes that this dataset is a sample from a larger population. Thus it simulates
\hat{u}
based on the estimated variance parameter. This approach gives confidence intervals as if additional data were collected from the larger population from which this dataset is sampled. Overall, use.u=TRUE gives smaller confidence intervals that are appropriate in this case.
list() of where each entry is the result for a gene. Each entry is a matrix of the 95% confidence interval of the variance fraction for each variable
# load library # library(variancePartition) # Intialize parallel backend with 4 cores library(BiocParallel) register(SnowParam(4)) # load simulated data: # geneExpr: matrix of gene expression values # info: information/metadata about each sample data(varPartData) # Specify variables to consider # Age is continuous so we model it as a fixed effect # Individual and Tissue are both categorical, so we model them as random effects form <- ~ Age + (1|Individual) + (1|Tissue) # Compute bootstrap confidence intervals for each variable for each gene resCI <- varPartConfInf( geneExpr[1:5,], form, info, nsim=100 )
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