dream {variancePartition} | R Documentation

## Differential expression with linear mixed model

### Description

Fit linear mixed model for differential expression and preform hypothesis test on fixed effects as specified in the contrast matrix L

### Usage

    dream(
      exprObj,
      formula,
      data,
      L,
      ddf = c("Satterthwaite", "Kenward-Roger"),
      useWeights = TRUE,
      weightsMatrix = NULL,
      control = lme4::lmerControl(calc.derivs = FALSE, check.rankX = "stop.deficient"),
      suppressWarnings = FALSE,
      quiet = FALSE,
      BPPARAM = bpparam(),
      computeResiduals = FALSE,
      REML = TRUE,
      ...
    )

### 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)` Formulas with only fixed effects also work, and lmFit() followed by contrasts.fit() are run.
`data` | data.frame with columns corresponding to formula
`L` | contrast matrix specifying a linear combination of fixed effects to test
`ddf` | Specifiy "Satterthwaite" or "Kenward-Roger" method to estimate effective degress of freedom for hypothesis testing in the linear mixed model. Note that Kenward-Roger is more accurate, but is *much* slower. Satterthwaite is a good enough approximation for most datasets.
`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
`control` | control settings for `lmer()`
`suppressWarnings` | if TRUE, do not stop because of warnings or errors in model fit
`quiet` | suppress message, default FALSE
`BPPARAM` | parameters for parallel evaluation
`computeResiduals` | if TRUE, compute residuals and extract with `residuals(fit)`. Setting to false saves memory
`REML` | use restricted maximum likelihood to fit linear mixed model. default is TRUE. See Details.
`...` | 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)`

`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 code 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 lmer.

Hypothesis tests and degrees of freedom are producted by `lmerTest` and `pbkrtest` pacakges

While `REML=TRUE` is required by `lmerTest` when ddf='Kenward-Roger', ddf='Satterthwaite' can be used with `REML` as `TRUE` or `FALSE`. Since the Kenward-Roger method gave the best power with an accurate control of false positive rate in our simulations, and since the Satterthwaite method with REML=TRUE gives p-values that are slightly closer to the Kenward-Roger p-values, `REML=TRUE` is the default. See Vignette "3) Theory and practice of random effects and REML"

### Value

MArrayLM2 object (just like MArrayLM from limma), and the directly estimated p-value (without eBayes)

### Examples

    # load library
    # library(variancePartition)
    library(BiocParallel)
    register(SerialParam())

    # load simulated data:
    # geneExpr: matrix of gene expression values
    # info: information/metadata about each sample
    data(varPartData)

    form <- ~ Batch + (1|Individual) + (1|Tissue)

    # Fit linear mixed model for each gene
    # run on just 10 genes for time
    fit = dream( geneExpr[1:10,], form, info)

    # view top genes
    topTable( fit )

    # get contrast matrix testing if the coefficient for Batch2 is
    # different from coefficient for Batch3
    # The variable of interest must be a fixed effect
    L = getContrast( geneExpr, form, info, c("Batch2", "Batch3"))

    # plot contrasts
    plotContrasts( L )

    # Fit linear mixed model for each gene
    # run on just 10 genes for time
    # Note that that dream() is not compatible with eBayes()
    fit2 = dream( geneExpr[1:10,], form, info, L)

    # view top genes
    topTable( fit2 )

    # Parallel processing using multiple cores with reduced memory usage
    param = SnowParam(4, "SOCK", progressbar=TRUE)
    fit3 = dream( geneExpr[1:10,], form, info, L, BPPARAM = param)

    # Fit fixed effect model for each gene
    # Use lmFit in the backend
    # Need to run eBayes afterward
    form <- ~ Batch
    fit4 = dream( geneExpr[1:10,], form, info)
    fit4 = eBayes( fit4 )

    # view top genes
    topTable( fit4 )

    # Compute residuals using dream
    fit5 = dream( geneExpr[1:10,], form, info, L, BPPARAM = param, computeResiduals=TRUE)

    # Return residuals
    residuals(fit5)

[Package variancePartition version 1.18.3 Index]