Bioinformatics Toolbox™

gcrmabackadj - Perform GC Robust Multi-array Average (GCRMA) background adjustment on Affymetrix microarray probe-level data using sequence information

Syntax

PMMatrix_Adj = gcrmabackadj(PMMatrix, MMMatrix, AffinPM, AffinMM)
[PMMatrix_Adj, nsbStruct] = gcrmabackadj(PMMatrix, MMMatrix, AffinPM, AffinMM)

... = gcrmabackadj( ...'OpticalCorr', OpticalCorrValue, ...)
... = gcrmabackadj( ...'CorrConst', CorrConstValue, ...)
... = gcrmabackadj( ...'Method', MethodValue, ...)
... = gcrmabackadj( ...'TuningParam', TuningParamValue, ...)
... = gcrmabackadj( ...'AddVariance', AddVarianceValue, ...)
... = gcrmabackadj( ...'Showplot', ShowplotValue, ...)
... = gcrmabackadj( ...'Verbose', VerboseValue, ...)

Arguments

Return Values

Description

PMMatrix_Adj = gcrmabackadj(PMMatrix, MMMatrix, AffinPM, AffinMM) performs GCRMA background adjustment (including optical background correction and nonspecific binding correction) on Affymetrix microarray probe-level data, using probe sequence information and returns PMMatrix_Adj, a matrix of background adjusted PM (perfect match) intensity values.

Note   If AffinPM and AffinMM data are not available, you can still use the gcrmabackadj function by entering empty column vectors for both of these inputs in the syntax.

[PMMatrix_Adj, nsbStruct] = gcrmabackadj(PMMatrix, MMMatrix, AffinPM, AffinMM) returns nsbStruct, a structure containing nonspecific binding background parameters, estimated from the intensities and affinities of probes on an Affymetrix GeneChip array. nsbStruct includes the following fields:

• sigma

• mu_pm

• mu_mm

... = gcrmabackadj( ...'PropertyName', PropertyValue, ...) calls gcrmabackadj with optional properties that use property name/property value pairs. You can specify one or more properties in any order. Each PropertyName must be enclosed in single quotation marks and is case insensitive. These property name/property value pairs are as follows:

... = gcrmabackadj( ...'OpticalCorr', OpticalCorrValue, ...) controls the use of optical background correction on the PM and MM probe intensity values in PMMatrix and MMMatrix. Choices are true (default) or false.

... = gcrmabackadj( ...'CorrConst', CorrConstValue, ...) specifies the correlation constant, rho, for log background intensity for each PM/MM probe pair. Choices are any value ≥ 0 and ≤ 1. Default is 0.7.

... = gcrmabackadj( ...'Method', MethodValue, ...) specifies the method to estimate the signal. Choices are MLE, a faster, ad hoc Maximum Likelihood Estimate method, or EB, a slower, more formal, empirical Bayes method. Default is MLE.

... = gcrmabackadj( ...'TuningParam', TuningParamValue, ...) specifies the tuning parameter used by the estimate method. This tuning parameter sets the lower bound of signal values with positive probability. Choices are a positive value. Default is 5 (MLE) or 0.5 (EB).

Tip   For information on determining a setting for this parameter, see Wu et al., 2004.

... = gcrmabackadj( ...'AddVariance', AddVarianceValue, ...) controls whether the signal variance is added to the weight function for smoothing low signal edge. Choices are true or false (default).

... = gcrmabackadj( ...'Showplot', ShowplotValue, ...) controls the display of a plot showing the log₂ of probe intensity values from a specified column (chip) in MMMatrix, versus probe affinities in AffinMM. Choices are true, false, or I, an integer specifying a column in MMMatrix. If set to true, the first column in MMMatrix is plotted. Default is:

• false — When return values are specified.

• true — When return values are not specified.

... = gcrmabackadj( ...'Verbose', VerboseValue, ...) controls the display of a progress report showing the number of each chip as it is completed. Choices are true (default) or false.

Examples

1. Load the MAT-file, included with the Bioinformatics Toolbox software, that contains Affymetrix data from a prostate cancer study. The variables in the MAT-file include seqMatrix, a matrix containing sequence information for PM probes, pmMatrix and mmMatrix, matrices containing PM and MM probe intensity values, and probeIndices, a column vector containing probe indexing information.

   load prostatecancerrawdata
   

2. Compute the Affymetrix PM and MM probe affinities from their sequences and MM probe intensities.

   [apm, amm] = affyprobeaffinities(seqMatrix, mmMatrix(:,1),...
                'ProbeIndices', probeIndices);
   

3. Perform GCRMA background adjustment on the Affymetrix microarray probe-level data, creating a matrix of background adjusted PM intensity values. Also, display a plot showing the log₂ of probe intensity values from column 3 (chip 3) in mmMatrix, versus probe affinities in amm.

   pms_adj = gcrmabackadj(pmMatrix, mmMatrix, apm, amm, 'showplot', 3);

   

4. Perform GCRMA background adjustment again, using the slower, more formal, empirical Bayes method.

   pms_adj2 = gcrmabackadj(pmMatrix, mmMatrix, apm, amm, 'method', 'EB');

The prostatecancerrawdata.mat file used in this example contains data from Best et al., 2005.

References

[1] Wu, Z., Irizarry, R.A., Gentleman, R., Murillo, F.M., and Spencer, F. (2004). A Model Based Background Adjustment for Oligonucleotide Expression Arrays. Journal of the American Statistical Association 99(468), 909–917.

[2] Wu, Z., and Irizarry, R.A. (2005). Stochastic Models Inspired by Hybridization Theory for Short Oligonucleotide Arrays. Proceedings of RECOMB 2004. J Comput Biol. 12(6), 882–93.

[3] Wu, Z., and Irizarry, R.A. (2005). A Statistical Framework for the Analysis of Microarray Probe-Level Data. Johns Hopkins University, Biostatistics Working Papers 73.

[4] Wu, Z., and Irizarry, R.A. (2003). A Model Based Background Adjustment for Oligonucleotide Expression Arrays. RSS Workshop on Gene Expression, Wye, England, http://biosun01.biostat.jhsph.edu/%7Eririzarr/Talks/gctalk.pdf.

[5] Abd Rabbo, N.A., and Barakat, H.M. (1979). Estimation Problems in Bivariate Lognormal Distribution. Indian J. Pure Appl. Math 10(7), 815–825.

[6] Best, C.J.M., Gillespie, J.W., Yi, Y., Chandramouli, G.V.R., Perlmutter, M.A., Gathright, Y., Erickson, H.S., Georgevich, L., Tangrea, M.A., Duray, P.H., Gonzalez, S., Velasco, A., Linehan, W.M., Matusik, R.J., Price, D.K., Figg, W.D., Emmert-Buck, M.R., and Chuaqui, R.F. (2005). Molecular alterations in primary prostate cancer after androgen ablation therapy. Clinical Cancer Research 11, 6823–6834.

See Also

Bioinformatics Toolbox functions: affygcrma, affyprobeseqread, affyread, celintensityread, probelibraryinfo

gcrma

genbankread