ebayes {limma} | R Documentation |

Given a series of related parameter estimates and standard errors, compute moderated t-statistics and log-odds of differential expression by empirical Bayes shrinkage of the standard errors towards a common value.

ebayes(fit,proportion=0.01,stdev.coef.lim=c(0.1,4)) eBayes(fit,proportion=0.01,stdev.coef.lim=c(0.1,4))

`fit` |
a list object produced by `lm.series` , `gls.series` , `mrlm` or `lmFit` containing components `coefficients` , `stdev.unscaled` , `sigma` and `df.residual` |

`proportion` |
numeric value between 0 and 1, assumed proportion of genes which are differentially expressed |

`stdev.coef.lim` |
numeric vector of length 2, assumed lower and upper limits for the standard deviation of log2 fold changes for differentially expressed genes |

These functions is used to rank genes in order of evidence for differential expression.
It uses an empirical Bayes method to shrink the gene-wise sample variances towards a common values and, in so doing, augmenting the degrees of freedom for the individual variances.
The function accepts as input output from the functions `lmFit`

, `lm.series`

, `mrlm`

or `gls.series`

.
The estimates `s2.prior`

and `df.prior`

are computed by `fitFDist`

.
`s2.post`

is the weighted average of `s2.prior`

and `sigma^2`

with weights proportional to `df.prior`

and `df.residual`

respectively.
The `lods`

is sometimes known as the B-statistic.

`eBayes`

doesn't compute ordinary (unmoderated) t-statistics by default, but these can be easily extracted from
the linear model output, see the example below.

`ebayes`

is the earlier and leaner function.
`eBayes`

is intended to have a more object orientated flavor as it produces objects containing all the necessary components for downstream analysis.

`ebayes`

produces an ordinary list with the following components.
`eBayes`

adds the following components to `fit`

to produce an augmented object, usually of class `MArrayLM`

.

`t` |
numeric vector or matrix of penalized t-statistics |

`p.value` |
numeric vector of p-values corresponding to the t-statistics |

`s2.prior` |
estimated prior value for `sigma^2` |

`df.prior` |
degrees of freedom associated with `s2.prior` |

`s2.post` |
vector giving the posterior values for `sigma^2` |

`lods` |
numeric vector or matrix giving the log-odds of differential expression |

`var.prior` |
estimated prior value for the variance of the log2-fold-change for differentially expressed gene |

`F` |
numeric vector of F-statistics for testing all contrasts simultaneously equal to zero |

`F.p.value` |
numeric vector giving p-values corresponding to `F` |

Gordon Smyth

Lönnstedt, I. and Speed, T. P. (2002). Replicated microarray data. *Statistica Sinica* **12**, 31-46.

Smyth, G. K. (2004). Linear models and empirical Bayes methods for assessing differential expression in microarray experiments.
*Statistical Applications in Genetics and Molecular Biology*, **3**, No. 1, Article 3. http://www.bepress.com/sagmb/vol3/iss1/art3

`squeezeVar`

, `fitFDist`

, `tmixture.matrix`

.

An overview of linear model functions in limma is given by 06.LinearModels.

# See also lmFit examples # Simulate gene expression data, # 6 microarrays and 100 genes with one gene differentially expressed set.seed(2004); invisible(runif(100)) M <- matrix(rnorm(100*6,sd=0.3),100,6) M[1,] <- M[1,] + 1 fit <- lmFit(M) # Ordinary t-statistic par(mfrow=c(1,2)) ordinary.t <- fit$coef / fit$stdev.unscaled / fit$sigma qqt(ordinary.t,df=fit$df.residual,main="Ordinary t") abline(0,1) # Moderated t-statistic eb <- eBayes(fit) qqt(eb$t,df=eb$df.prior+eb$df.residual,main="Moderated t") abline(0,1) # Points off the line may be differentially expressed par(mfrow=c(1,1))

[Package *limma* version 2.4.7 Index]