panp (version 1.42.0)

pa.calls: Presence-Absence Calls from Negative Strand Matching Probesets

Description

Function to make gene presence/absence calls based on distance from empirical distribution of chip-specific negative strand matching probesets (NSMP).

Usage

pa.calls(object, looseCutoff = 0.02, tightCutoff = 0.01, verbose = FALSE)

Arguments

object

an ExpressionSet object (result of running expression-generating function, like expresso(), rma(), mas5(), etc.) Currently, this must be of chip type HGU133A or HGU133 Plus 2.0

looseCutoff

the larger P-value cutoff (see details)

tightCutoff

the smaller, more strict P-value cutoff

verbose

logical. If 'TRUE' detailed progress messages are reported.

Value

list

a new list containing two matrices: Pcalls and Pvals, as follows:

Pcalls

a matrix of Presence (P), Marginal (M), Absent (A) indicators

Pvals

a matrix of P-values. Each data point is the P-value for the expr at the same x, y coordinates.

Details

The function calculates a matrix of P-values for the expression values in the input ExpressionSet. P-values are calculated based on the empirical survivor function (1-CDF) of the set of negative probesets identified by Affymetrix as negative strand matching probesets (NSMP) with no cross hybridization. These probesets are therefore assumed to show nothing but background/machine noise plus some occasional non-specific binding. The P-value returned for any probeset expression value in ExpressionSet is the value of the NSMP survivor function for that expression level.

Presence/Absence calls are derived by applying the two cutoff values to the matrix of P-values for all genes in the ExpressionSet, as follows:

Present ('P')

P-values <= tightcutoff<="" dd="">

Absent ('A')

P-values > looseCutoff

Marginal ('M')

P-values between tightCutoff and looseCutoff

References

Warren, P., Bienkowska, J., Martini, P., Jackson, J., and Taylor, D., PANP - a New Method of Gene Detection on Oligonucleotide Expression Arrays (2007), in preparation

Examples

## Load example ExpressionSet 
data(gcrma.ExpressionSet)

## Generate Pvals and Pcalls matrices from ExpressionSet, using default cutoffs
PA <- pa.calls(gcrma.ExpressionSet)

## to access the Pcalls and Pvals:
myPcalls <- PA$Pcalls
myPvals <-  PA$Pvals