DaMiR.normalization {DaMiRseq} | R Documentation

## Filter non Expressed and 'Hypervariant' features and Data Normalization

### Description

Features will be firstly filtered based on their expression value and/or by their variability across samples; features will be then normalized.

### Usage

    DaMiR.normalization(data, minCounts = 10, fSample = 0.5, hyper = c("yes",
      "no"), th.cv = 3, type = c("vst", "rlog"), nFitType = c("parametric",
      "local", "mean"))

### Arguments

`data` | A `SummarizedExperiment` object
`minCounts` | Minimum reads counts; default is 10
`fSample` | Fraction of samples with `minCounts` counts; default is 0.5
`hyper` | Flag to enable gene filtering by Coefficient of Variation (CV); default is "yes"
`th.cv` | Threshold of minimum CV to consider a feature 'Hypervariant' accross samples; default is 3
`type` | Type of normalization to be applied: `varianceStabilizingTransformation` (`vst`) or `rlog` are allowed; default is "`vst`"
`nFitType` | Type of method to estimate the dispersion by vst or rlog. Default is "parametric". See details in estimateDispersions

### Details

Before normalization step, this function allows the user to filter features by:

- Expression - Features will be filtered out whether their reads count do not reach a `minCounts` in at least `fSample` of samples;

- CV - The CV of each feature is individually calculated for each sample class. Featurers with both class CV greater than `th.cv` will be discarded. Computing a class restricted CV may prevent the removal of hypervariant features that may be specifically associated with a certain class. This could be important, for example, for immune genes whose expression under definite conditions may unveil peculiar class-gene association.

Finally, expressed features will be normalized by `varianceStabilizingTransformation` (default) or `rlog`, both implemented in `DESeq2` package. We suggest to use `varianceStabilizingTransformation` to speed up the normalization process because `rlog` is very time-consuming despite the two methods produce quite similar results.

### Value

A `SummarizedExperiment` object which contains a normalized expression matrix (log2 scale) and the data frame with 'class' and (optionally) variables.

### Author(s)

Mattia Chiesa, Luca Piacentini

### References

Michael I Love, Wolfgang Huber and Simon Anders (2014): Moderated estimation of fold change and dispersion for RNA-Seq data with DESeq2. Genome Biology

### See Also

`varianceStabilizingTransformation, rlog`

### Examples

    # use example data:
    data(SE)
    # perform normalization on a subset of data:
    SE_sub<-SE[1:1000, c(1:3, 21:23)]
    data_norm <- DaMiR.normalization(SE_sub, minCounts=10, fSample=0.8,
    hyper="yes", th.cv = 2.5)

[Package DaMiRseq version 1.4.2 Index]