fitVarPartModel-method: Fit linear (mixed) model

In GabrielHoffman/variancePartition: Quantify and interpret drivers of variation in multilevel gene expression experiments

Fit linear (mixed) model

Description

Fit linear (mixed) model to estimate contribution of multiple sources of variation while simultaneously correcting for all other variables.

Usage

fitVarPartModel(
  exprObj,
  formula,
  data,
  REML = FALSE,
  useWeights = TRUE,
  fxn = identity,
  control = vpcontrol,
  hideErrorsInBackend = FALSE,
  showWarnings = TRUE,
  BPPARAM = SerialParam(),
  ...
)

## S4 method for signature 'matrix'
fitVarPartModel(
  exprObj,
  formula,
  data,
  REML = FALSE,
  useWeights = TRUE,
  fxn = identity,
  control = vpcontrol,
  hideErrorsInBackend = FALSE,
  showWarnings = TRUE,
  BPPARAM = SerialParam(),
  ...
)

## S4 method for signature 'data.frame'
fitVarPartModel(
  exprObj,
  formula,
  data,
  REML = FALSE,
  useWeights = TRUE,
  fxn = identity,
  control = vpcontrol,
  hideErrorsInBackend = FALSE,
  showWarnings = TRUE,
  BPPARAM = SerialParam(),
  ...
)

## S4 method for signature 'EList'
fitVarPartModel(
  exprObj,
  formula,
  data,
  REML = FALSE,
  useWeights = TRUE,
  fxn = identity,
  control = vpcontrol,
  hideErrorsInBackend = FALSE,
  showWarnings = TRUE,
  BPPARAM = SerialParam(),
  ...
)

## S4 method for signature 'ExpressionSet'
fitVarPartModel(
  exprObj,
  formula,
  data,
  REML = FALSE,
  useWeights = TRUE,
  fxn = identity,
  control = vpcontrol,
  hideErrorsInBackend = FALSE,
  showWarnings = TRUE,
  BPPARAM = SerialParam(),
  ...
)

## S4 method for signature 'sparseMatrix'
fitVarPartModel(
  exprObj,
  formula,
  data,
  REML = FALSE,
  useWeights = TRUE,
  fxn = identity,
  control = vpcontrol,
  hideErrorsInBackend = FALSE,
  showWarnings = TRUE,
  BPPARAM = SerialParam(),
  ...
)

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 as 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. See Details.
useWeights	if TRUE, analysis uses heteroskedastic error estimates from voom(). Value is ignored unless exprObj is an EList() from voom() or weightsMatrix is specified
fxn	apply function to model fit for each gene. Defaults to identify function so it returns the model fit itself
control	control settings for lmer()
hideErrorsInBackend	default FALSE. If TRUE, hide errors in attr(.,"errors") and attr(.,"error.initial")
showWarnings	default TRUE. Indicate model failures
BPPARAM	parameters for parallel evaluation
...	Additional arguments for lmer() or lm()

Details

A linear (mixed) model is fit for each gene in exprObj, using formula to specify variables in the regression. If categorical variables are modeled as random effects (as is recommended), then a linear mixed model us used. For example if formula is ~ a + b + (1|c), then the model is

fit <- lmer( exprObj[j,] ~ a + b + (1|c), data=data)

If there are no random effects, so formula is ~ a + b + c, a 'standard' linear model is used:

fit <- lm( exprObj[j,] ~ a + b + c, data=data)

In both cases, useWeights=TRUE causes weightsMatrix[j,] to be included as weights in the regression model.

Note: Fitting the model for 20,000 genes can be computationally intensive. To accelerate computation, models can be fit in parallel using BiocParallel to run in parallel. Parallel processing must be enabled before calling this function. See below.

The regression model is fit for each gene separately. Samples with missing values in either gene expression or metadata are omitted by the underlying call to lm/lmer.

Since this function returns a list of each model fit, using this function is slower and uses more memory than fitExtractVarPartModel().

REML=FALSE uses maximum likelihood to estimate variance fractions. This approach produced unbiased estimates, while REML=TRUE can show substantial bias. See Vignette "3) Theory and practice of random effects and REML"

Value

list() of where each entry is a model fit produced by lmer() or lm()

Examples

# load library
# library(variancePartition)

library(BiocParallel)

# 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)

# Step 1: fit linear mixed model on gene expression
# If categorical variables are specified, a linear mixed model is used
# If all variables are modeled as continuous, a linear model is used
# each entry in results is a regression model fit on a single gene
# Step 2: extract variance fractions from each model fit
# for each gene, returns fraction of variation attributable to each variable
# Interpretation: the variance explained by each variable
# after correction for all other variables
varPart <- fitExtractVarPartModel(geneExpr, form, info)

# violin plot of contribution of each variable to total variance
# also sort columns
plotVarPart(sortCols(varPart))

# Advanced:
# Fit model and extract variance in two separate steps
# Step 1: fit model for each gene, store model fit for each gene in a list
results <- fitVarPartModel(geneExpr, form, info)

# Step 2: extract variance fractions
varPart <- extractVarPart(results)

# Note: fitVarPartModel also accepts ExpressionSet
data(sample.ExpressionSet, package = "Biobase")

# ExpressionSet example
form <- ~ (1 | sex) + (1 | type) + score
info2 <- Biobase::pData(sample.ExpressionSet)
results2 <- fitVarPartModel(sample.ExpressionSet, form, info2)

GabrielHoffman/variancePartition documentation built on April 20, 2024, 7:29 p.m.