norm1b {nudge} | R Documentation

## Function for normalizing the mean and variance (or just the variance) of single replicate log ratios

### Description

This performs a robust loess normalization of the variance of the log ratios in a single replicate experiment by regressing the absolute (mean normalized) log ratios on the log intensities and using the fitted values to scale the (mean normalized) log ratio for each gene.

### Usage

    norm1b(logratio, logintensity, span1 = 0.6, span2 = 0.2, mean.norm=TRUE)

### Arguments

`logratio` | A vector or single-column matrix of log (base 2) ratios of gene expressions in two samples, if mean.norm is FALSE the log ratios should be already mean normalized.
`logintensity` | A vector or single-column matrix of log (base 2) total intensities (defined as the product) of gene expressions in the two samples.
`span1` | Proportion of data used to fit the loess regression of the log ratios on the log intensities for the mean normalization.
`span2` | Proportion of data used to fit the loess regression of the absolute (mean normalized) log ratios on the log intensities for the variance normalization.
`mean.norm` | A logical value indicating whether or not a mean normalization should be performed prior to the variance normalization.

### Value

A vector or single-column matrix of mean and variance normalized log (base 2) ratios of gene expressions in two samples.

### Author(s)

N. Dean and A. E. Raftery

### References

N. Dean and A. E. Raftery (2005). Normal uniform mixture differential gene expression detection for cDNA microarrays. BMC Bioinformatics. 6, 173-186.

http://www.biomedcentral.com/1471-2105/6/173

### See Also

`norm1a`,`norm1c`,`norm1d`,`norm2c`,`norm2d`

### Examples

    data(like)
    lR<-log(like[,1],2)-log(like[,2],2)
    lI<-log(like[,1],2)+log(like[,2],2)

    lRnorm<-norm1b(lR,lI,mean.norm=TRUE)

[Package nudge version 1.8.0 Index]