Description Usage Arguments Details Note Examples

Gene-specific normalization factors for each sample can be provided as a matrix,
which will preempt `sizeFactors`

. In some experiments, counts for each
sample have varying dependence on covariates, e.g. on GC-content for sequencing
data run on different days, and in this case it makes sense to provide
gene-specific factors for each sample rather than a single size factor.

1 2 3 4 5 6 7 8 9 10 11 12 | ```
normalizationFactors(object, ...)
normalizationFactors(object, ...) <- value
## S4 method for signature 'DESeqDataSet'
normalizationFactors(object)
## S4 replacement method for signature 'DESeqDataSet,matrix'
normalizationFactors(object)<-value
## S4 method for signature 'DESeqDataSet'
normalizationFactors(object)
``` |

`object` |
a |

`...` |
additional arguments |

`value` |
the matrix of normalization factors |

Normalization factors alter the model of `DESeq`

in the following way, for
counts *K_ij* and normalization factors *NF_ij* for gene i and sample j:

* K_ij ~ NB(mu_ij, alpha_i) *

* mu_ij = NF_ij q_ij *

Normalization factors are on the scale of the counts (similar to `sizeFactors`

)
and unlike offsets, which are typically on the scale of the predictors (in this case, log counts).
Normalization factors should include library size normalization. They should have
row-wise geometric mean near 1, as is the case with size factors, such that the mean of normalized
counts is close to the mean of unnormalized counts.

1 2 3 4 5 6 7 | ```
dds <- makeExampleDESeqDataSet()
normFactors <- matrix(runif(nrow(dds)*ncol(dds),0.5,1.5),
ncol=ncol(dds),nrow=nrow(dds))
normFactors <- normFactors / rowMeans(normFactors)
normalizationFactors(dds) <- normFactors
dds <- estimateDispersions(dds)
dds <- nbinomWaldTest(dds)
``` |

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