Description Usage Arguments Details Value Author(s) References See Also

View source: R/voomWithQualityWeights.R

Combine voom observational-level weights with sample-specific quality weights in a designed experiment.

1 2 3 |

`counts` |
a numeric |

`design` |
design matrix with rows corresponding to samples and columns to coefficients to be estimated. Defaults to the unit vector meaning that samples are treated as replicates. |

`lib.size` |
numeric vector containing total library sizes for each sample.
If |

`normalize.method` |
normalization method to be applied to the logCPM values.
Choices are as for the |

`plot` |
logical, should a plot of the mean-variance trend and sample-specific weights be displayed? |

`span` |
width of the lowess smoothing window as a proportion. |

`var.design` |
design matrix for the variance model. Defaults to the sample-specific model whereby each sample has a distinct quality weight. |

`var.group` |
vector or factor indicating groups to have different quality weights.
This is another way to specify |

`method` |
character string specifying the method used to estimate the quality weights.
Choices are |

`maxiter` |
maximum number of iterations allowed for quality weight estimation when |

`tol` |
convergence tolerance for quality weight estimation when |

`trace` |
logical.
If |

`col` |
colours to use in the barplot of sample-specific weights if |

`...` |
other arguments are passed to |

This function is an alternative to `voom`

and, like `voom`

, is intended to process RNA-seq data prior to linear modeling in limma.
It combines observational-level weights from `voom`

with sample-specific weights estimated using the `arrayWeights`

function.
The method is described by Liu et al (2015).

An `EList`

object similar to that from `voom`

,
with an extra column `sample.weights`

containing the vector of sample quality factors added to the `targets`

data.frame.
The `weights`

component combines the sample weights and the usual voom precision weights.

Matthew Ritchie, Cynthia Liu, Gordon Smyth

Liu, R., Holik, A. Z., Su, S., Jansz, N., Chen, K., Leong, H. S., Blewitt, M. E., Asselin-Labat, M.-L., Smyth, G. K., Ritchie, M. E. (2015).
Why weight? Combining voom with estimates of sample quality improves power in RNA-seq analyses.
*Nucleic Acids Research* 43, e97.
http://nar.oxfordjournals.org/content/43/15/e97

See also `voomLmFit`

in the edgeR package.

A summary of limma functions for RNA-seq analysis is given in 11.RNAseq.

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