| Title: | Presence-Absence Calls from Negative Strand Matching Probesets |
|---|---|
| Description: | A function to make gene presence/absence calls based on distance from negative strand matching probesets (NSMP) which are derived from Affymetrix annotation. PANP is applied after gene expression values are created, and therefore can be used after any preprocessing method such as MAS5 or GCRMA, or PM-only methods like RMA. NSMP sets have been established for the HGU133A and HGU133-Plus-2.0 chipsets to date. |
| Authors: | Peter Warren |
| Maintainer: | Peter Warren <[email protected]> |
| License: | GPL (>= 2) |
| Version: | 1.75.0 |
| Built: | 2024-10-01 05:08:21 UTC |
| Source: | https://github.com/bioc/panp |
An ExpressionSet object resulting from gcrma processing of three HG-U133A chips from the 28-chip affyCOMP II dataset, derived from the AffyMetrix Latin Squares dataset. The gcrma.ExpressionSet can be used as example input for panp's pa.calls() function, and it is used for this purpose in the panp vignette.
data(gcrma.ExpressionSet)data(gcrma.ExpressionSet)
An ExpressionSet object comprising three gcrma-processed HG-U133A chips
The 3 HG-U133A chips are the first three of affyCOMP II Latin Squares set
This list of NSMP probeset names is used by pa.calls() in calculating the NSMP expression distribution. It is specific to the chip type; the correct version is automatically loaded for the detected chip type, if supported. The user does not load or use this data directly.
data(NSMPnames.hgu133a)data(NSMPnames.hgu133a)
A vector containing probeset names
October 2004 AffyMetrix chip annotation files, filtered, with outliers removed
This list of NSMP probeset names is used by pa.calls() in calculating the NSMP expression distribution. It is specific to the chip type; the correct version is automatically loaded for the detected chip type, if supported. The user does not load or use this data directly.
data(NSMPnames.hgu133plus2)data(NSMPnames.hgu133plus2)
A vector containing probeset names
October 2004 AffyMetrix chip annotation files, filtered, with outliers removed
Function to make gene presence/absence calls based on distance from empirical distribution of chip-specific negative strand matching probesets (NSMP).
pa.calls(object, looseCutoff = 0.02, tightCutoff = 0.01, verbose = FALSE)pa.calls(object, looseCutoff = 0.02, tightCutoff = 0.01, verbose = FALSE)
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. |
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:
P-values <= tightCutoff
P-values > looseCutoff
P-values between tightCutoff and looseCutoff
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. |
NSMP sets have been established for the HGU133A and HGU133-Plus-2.0 chipsets to date. Hence only these two are currently supported by PANP.
Peter Warren
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
## 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## 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