nec: NormExp Background Correction and Normalization Using Control...
In richierocks/limma2: Linear Models for Microarray Data

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

Perform normexp background correction using negative control probes and quantile normalization using negative and positive control probes.

nec(x, status=NULL, negctrl="negative", regular="regular", offset=16,
    robust=FALSE, detection.p="Detection")
neqc(x, status=NULL, negctrl="negative", regular="regular", offset=16,
    robust=FALSE, detection.p="Detection", ...)

`x`	object of class `EListRaw` or `matrix` containing raw intensities for regular and control probes from a series of microarrays.
`status`	character vector giving probe types. Defaults to `x$genes$Status` if `x` is an `EListRaw` object.
`negctrl`	character string identifier for negative control probes.
`regular`	character string identifier for regular probes, i.e., all probes other than control probes.
`offset`	numeric value added to the intensities after background correction.
`robust`	logical. Should robust estimators be used for the background mean and standard deviation?
`detection.p`	a character string giving the name of the component which contains detection p value information in `x` or a numeric matrix giving detection p values, `Detection` by default
`...`	any other arguments are passed to `normalizeBetweenArrays.`

neqc performs background correction followed by quantile normalization, using negative control probes for background correction and both negative and positive controls for normalization. nec is similar but performs background correction only.

When control data are available, these function call normexp.fit.control to estimate the parameters required by normal+exponential(normexp) convolution model with the help of negative control probes, followed by normexp.signal to perform the background correction. If x contains background intensities x$Eb, then these are first subtracted from the foreground intensities, prior to normexp background correction. After background correction, an offset is added to the data.

When control data are not available, these functions call normexp.fit.detection.p to estimate the normexp parameters. normexp.fit.detection.p infers negative control probe intensities from regular probes by taking advantage of their detection p value information.

For more descriptions to parameters x, status, negctrl, regular and detection.p, please refer to functions normexp.fit.control, normexp.fit.detection.p and read.ilmn.

Both nec and neqc perform the above steps. neqc continues on to quantile normalize the background-corrected intensities, including control probes. After normalization, the intensities are log2 transformed and the control probes are removed.

nec produces a EListRaw-class or matrix object of the same dimensions as x containing background-corrected intensities, on the raw scale. neqc produces a EList-class or matrix object containing normalized log2 intensities, with rows corresponding to control probes removed.

Wei Shi and Gordon Smyth

Shi W, Oshlack A and Smyth GK (2010). Optimizing the noise versus bias trade-off for Illumina Whole Genome Expression BeadChips. Nucleic Acids Research 38, e204. http://nar.oxfordjournals.org/content/38/22/e204

An overview of background correction functions is given in 04.Background.

An overview of LIMMA functions for normalization is given in 05.Normalization.

normexp.fit.control estimates the parameters in the normal+exponential convolution model using the negative control probes.

normexp.fit.detection.p estimates the parameters in the normal+exponential convolution model using negative control probe intensities inferred from regular probes by using their detection p values information.

normexp.fit estimates parameters in the normal+exponential convolution model using a saddle-point approximation or other methods.

neqc performs normexp background correction and quantile normalization aided by control probes.

## Not run: 
# neqc normalization for data which include control probes
x <- read.ilmn(files="sample probe profile.txt", ctrlfiles="control probe profile.txt")
y <- neqc(x)
fit <- lmFit(y,design)

# Same thing but in separate steps:
x.b <- nec(x)
y <- normalizeBetweenArrays(x.b,method="quantile")
y <- y[y$genes$Status=="regular",]

# neqc normalization for data which do not include control probes
xr <- read.ilmn(files="sample probe profile.txt")
yr <- neqc(xr)

## End(Not run)