detectionPValue: Detection P-Values from Negative Controls

In limma: Linear Models for Microarray Data

Description

Compute the proportion of negative controls greater than each observed expression value. Particularly useful for Illumina BeadChips.

Usage

## S3 method for class 'EListRaw'
detectionPValues(x, status = NULL, ...)
## Default S3 method:
detectionPValues(x, status, negctrl = "negative", ...)

Arguments

x	object of class EListRaw or a numeric 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.
...	other arguments are not currently used.

Details

The rows of x for which status == negctrl are assumed to correspond to negative control probes.

For each column of x, the detection p-values are defined as (N.eq/2 + N.gt) / N.neg, where N.gt is the number of negative controls with expression greater than the observed value, N.eq is the number of negative controls with expression equal to the observed value, and N.neg is the total number of negative controls.

When used on Illumina BeadChip data, this function produces essentially the same detection p-values as returned by Illumina's GenomeStudio software.

Value

numeric matrix of same dimensions as x containing detection p-values.

Author(s)

Gordon Smyth

References

Shi, W, de Graaf, C, Kinkel, S, Achtman, A, Baldwin, T, Schofield, L, Scott, H, Hilton, D, Smyth, GK (2010). Estimating the proportion of microarray probes expressed in an RNA sample. Nucleic Acids Research 38(7), 2168-2176. https://www.ncbi.nlm.nih.gov/pubmed/20056656

See Also

An overview of LIMMA functions to read expression data is given in 03.ReadingData.

read.idat reads Illumina BeadChip expression data from binary IDAT files.

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

Examples

## Not run: 
# Read Illumina binary IDAT files
x <- read.idat(idat, bgx)
x$genes$DectionPValue <- detectionPValues(x)
y <- neqc(x)

## End(Not run)

limma documentation built on Nov. 8, 2020, 8:28 p.m.