View source: R/normalization.R

estimate.norm.factors | R Documentation |

`estimate.norm.factors`

estiamtes normalization
factors to account for apparent reduction or increase in
relative frequencies of non-differentially expressing genes
as a result of compensating the increased or decreased
relative frequencies of truly differentially expressing
genes.

estimate.norm.factors(counts, lib.sizes = colSums(counts), method = "AH2010")

`counts` |
a matrix of RNA-Seq read counts with rows corresponding to gene features and columns corresponding to independent biological samples. |

`lib.sizes` |
a vector of observed library sizes, usually and by default estimated by column totals. |

`method` |
a character string specifying the method for normalization, currenlty, can be NULL or "AH2010". If method=NULL, the normalization factors will have values of 1 (i.e., no normalization is applied); if method="AH2010" (default), the normalization method proposed by Anders and Huber (2010) will be used. |

We take gene expression to be indicated by relative frequency of RNA-Seq reads mapped to a gene, relative to library sizes (column sums of the count matrix). Since the relative frequencies sum to 1 in each library (one column of the count matrix), the increased relative frequencies of truly over expressed genes in each column must be accompanied by decreased relative frequencies of other genes, even when those others do not truly differentially express. If not accounted for, this may give a false impression of biological relevance (see, e.g., Robinson and Oshlack (2010), for some examples.) A simple fix is to compute the relative frequencies relative to effective library sizesâ€”library sizes multiplied by normalization factors.

a vector of normalization factors.

Anders, S. and W. Huber (2010): "Differential expression analysis for sequence count data," Genome Biol., 11, R106.

Robinson, M. D. and A. Oshlack (2010): "A scaling normalization method for differential expression analysis of RNA-seq data," Genome Biol., 11, R25.

## Load Arabidopsis data data(arab) ## Estimate normalization factors using the method of Anders and Huber (2010) norm.factors = estimate.norm.factors(arab); print(norm.factors);

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