fitVarPartModel {variancePartition} | R Documentation

## 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, weightsMatrix = NULL, showWarnings = TRUE,
      fxn = identity, control = lme4::lmerControl(calc.derivs = FALSE,
      check.rankX = "stop.deficient"), ...)

    ## S4 method for signature 'matrix'
    fitVarPartModel(exprObj, formula, data, REML = FALSE,
      useWeights = TRUE, weightsMatrix = NULL, showWarnings = TRUE,
      fxn = identity, control = lme4::lmerControl(calc.derivs = FALSE,
      check.rankX = "stop.deficient"), ...)

    ## S4 method for signature 'data.frame'
    fitVarPartModel(exprObj, formula, data,
      REML = FALSE, useWeights = TRUE, weightsMatrix = NULL,
      showWarnings = TRUE, fxn = identity,
      control = lme4::lmerControl(calc.derivs = FALSE, check.rankX =
      "stop.deficient"), ...)

    ## S4 method for signature 'EList'
    fitVarPartModel(exprObj, formula, data, REML = FALSE,
      useWeights = TRUE, weightsMatrix = NULL, showWarnings = TRUE,
      fxn = identity, control = lme4::lmerControl(calc.derivs = FALSE,
      check.rankX = "stop.deficient"), ...)

    ## S4 method for signature 'ExpressionSet'
    fitVarPartModel(exprObj, formula, data,
      REML = FALSE, useWeights = TRUE, weightsMatrix = NULL,
      showWarnings = TRUE, fxn = identity,
      control = lme4::lmerControl(calc.derivs = FALSE, check.rankX =
      "stop.deficient"), ...)

### 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
`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)
`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()
`...` | 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 to 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 foreach/dopar to run loops 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().

### Value

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

### Examples

    # load library
    # library(variancePartition)

    # optional step to run analysis in parallel on multicore machines
    # Here, we used 4 threads
    library(doParallel)
    cl <- makeCluster(4)
    registerDoParallel(cl)
    # or by using the doSNOW package

    # 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 <- pData(sample.ExpressionSet)
    results2 <- fitVarPartModel( sample.ExpressionSet, form, info2 )

    # stop cluster
    stopCluster(cl)

[Package variancePartition version 1.12.3 Index]