voom {limma} | R Documentation |

Transform count data to log2-counts per million (logCPM), estimate the mean-variance relationship and use this to compute appropriate observation-level weights. The data are then ready for linear modelling.

voom(counts, design = NULL, lib.size = NULL, normalize.method = "none", span = 0.5, plot = FALSE, save.plot = FALSE, ...)

`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 |

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

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

`save.plot` |
logical, should the coordinates and line of the plot be saved in the output? |

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

This function is intended to process RNA-Seq or ChIP-Seq data prior to linear modelling in limma.

`voom`

is an acronym for mean-variance modelling at the observational level.
The key concern is to estimate the mean-variance relationship in the data, then use this to compute appropriate weights for each observation.
Count data almost show non-trivial mean-variance relationships.
Raw counts show increasing variance with increasing count size, while log-counts typically show a decreasing mean-variance trend.
This function estimates the mean-variance trend for log-counts, then assigns a weight to each observation based on its predicted variance.
The weights are then used in the linear modelling process to adjust for heteroscedasticity.

`voom`

performs the following specific calculations.
First, the counts are converted to logCPM values, adding 0.5 to all the counts to avoid taking the logarithm of zero.
The matrix of logCPM values is then optionally normalized.
The `lmFit`

function is used to fit row-wise linear models.
The `lowess`

function is then used to fit a trend to the square-root-standard-deviations as a function of average logCPM.
The trend line is then used to predict the variance of each logCPM value as a function of its fitted value, and the inverse variances become the estimated precision weights.

An `EList`

object with the following components:

`E` |
numeric matrix of normalized expression values on the log2 scale |

`weights` |
numeric matrix of inverse variance weights |

`design` |
design matrix |

`lib.size` |
numeric vector of total normalized library sizes |

`genes` |
dataframe of gene annotation extracted from |

`voom.xy` |
if |

`voom.line` |
if |

Charity Law and Gordon Smyth

Law, CW, Chen, Y, Shi, W, Smyth, GK (2014).
Voom: precision weights unlock linear model analysis tools for RNA-seq read counts.
*Genome Biology* 15, R29.
http://genomebiology.com/2014/15/2/R29

`voomWithQualityWeights`

.
`vooma`

is similar to `voom`

but for microarrays instead of RNA-seq.

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

[Package *limma* version 3.34.5 Index]