varPartConfInf: Linear mixed model confidence intervals

In variancePartition: Quantify and interpret divers of variation in multilevel gene expression experiments

Description

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.

Usage

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,
  ...
)

Arguments

exprObj	matrix of expression data (g genes x n samples), or ExpressionSet, or EList returned by voom() from the limma package
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.: ~ a + b + (1|c)
data	data.frame with columns corresponding to formula
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 voom(). Value is ignored unless exprObj is an EList from voom() or weightsMatrix is specified
weightsMatrix	matrix the same dimension as exprObj with observation-level weights from voom(). Used only if useWeights is TRUE
showWarnings	show warnings about model fit (default TRUE)
colinearityCutoff	cutoff used to determine if model is computationally singular
control	control settings for lmer()
nsim	number of bootstrap datasets
...	Additional arguments for lmer() or lm()

Details

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.

Value

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

Examples

# 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 )

variancePartition documentation built on Nov. 8, 2020, 5:18 p.m.