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

View source: R/equalizeLibSizes.R

Adjusts counts so that the effective library sizes are equal, preserving fold-changes between groups and preserving biological variability within each group.

1 2 3 4 5 | ```
## S3 method for class 'DGEList'
equalizeLibSizes(y, dispersion=NULL, ...)
## Default S3 method:
equalizeLibSizes(y, group=NULL, dispersion=NULL,
lib.size=NULL, ...)
``` |

`y` |
matrix of counts or a |

`dispersion` |
numeric scalar or vector of dispersion parameters.
By default, is extracted from |

`group` |
vector or factor giving the experimental group/condition for each library. |

`lib.size` |
numeric vector giving the total count (sequence depth) for each library. |

`...` |
other arguments that are not currently used. |

Thus function implements the quantile-quantile normalization method of Robinson and Smyth (2008).
It computes normalized counts, or pseudo-counts, used by `exactTest`

and `estimateCommonDisp`

.

The output pseudo-counts are the counts that would have theoretically arisen had the effective library sizes been equal for all samples.
The pseudo-counts are computed in such as way as to preserve fold-change differences beween the groups defined by `y$samples$group`

as well as biological variability within each group.
Consequently, the results will depend on how the groups are defined.

Note that the column sums of the `pseudo.counts`

matrix will not generally be equal, because the effective library sizes are not necessarily the same as actual library sizes and because the normalized pseudo counts are not equal to expected counts.

`equalizeLibSizes.DGEList`

returns a `DGEList`

object with the following new components:

`pseudo.counts` |
numeric matrix of normalized pseudo-counts |

`pseudo.lib.size` |
normalized library size |

`equalizeLibSizes.default`

returns a list with components pseudo.counts and pseudo.lib.size.

This function is intended mainly for internal edgeR use. It is not normally called directly by users.

Mark Robinson, Davis McCarthy, Gordon Smyth

Robinson MD and Smyth GK (2008).
Small-sample estimation of negative binomial dispersion, with applications to SAGE data.
*Biostatistics*, 9, 321-332.
http://biostatistics.oxfordjournals.org/content/9/2/321

1 2 3 4 5 6 7 8 9 10 |

```
Loading required package: limma
Sample1 Sample2
Min. : 2.000 Min. : 8.00
1st Qu.: 8.000 1st Qu.:17.00
Median :10.000 Median :20.00
Mean : 9.775 Mean :20.03
3rd Qu.:12.000 3rd Qu.:23.00
Max. :19.000 Max. :35.00
Sample1 Sample2
Min. : 3.614 Min. : 5.194
1st Qu.:11.417 1st Qu.:11.867
Median :14.134 Median :13.876
Mean :14.018 Mean :13.974
3rd Qu.:16.963 3rd Qu.:16.053
Max. :26.748 Max. :24.674
```

edgeR documentation built on Sept. 24, 2018, 6 p.m.

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