Bioinformatics Toolbox™

gcrma

Perform GC Robust Multi-array Average (GCRMA) background adjustment, quantile normalization, and median-polish summarization on Affymetrix^® microarray probe-level data

Syntax

ExpressionMatrix = gcrma(PMMatrix, MMMatrix, ProbeIndices, AffinPM, AffinMM)
ExpressionMatrix = gcrma(PMMatrix, MMMatrix, ProbeIndices, SequenceMatrix)

ExpressionMatrix = gcrma(..., 'ChipIndex', ChipIndexValue, ...)
ExpressionMatrix = gcrma(..., 'OpticalCorr', OpticalCorrValue, ...)
ExpressionMatrix = gcrma(..., 'CorrConst', CorrConstValue, ...)
ExpressionMatrix = gcrma(..., 'Method', MethodValue, ...)
ExpressionMatrix = gcrma(..., 'TuningParam', TuningParamValue, ...)
ExpressionMatrix = gcrma(..., 'GSBCorr', GSBCorrValue, ...)
ExpressionMatrix = gcrma(..., 'Normalize', NormalizeValue, ...)
ExpressionMatrix = gcrma(..., 'Verbose', VerboseValue, ...)

Arguments

Return Values

Description

ExpressionMatrix = gcrma(PMMatrix, MMMatrix, ProbeIndices, AffinPM, AffinMM) performs GCRMA background adjustment, quantile normalization, and median-polish summarization on Affymetrix microarray probe-level data using probe affinity data. ExpressionMatrix is a matrix of log₂ expression values where each row corresponds to a gene (probe set) and each column corresponds to an Affymetrix CEL file, which represents a single chip.

Note   There is no column in ExpressionMatrix that contains probe set or gene information.

ExpressionMatrix = gcrma(PMMatrix, MMMatrix, ProbeIndices, SequenceMatrix) performs GCRMA background adjustment, quantile normalization, and Robust Multi-array Average (RMA) summarization on Affymetrix microarray probe-level data using probe sequence data to compute probe affinity data. ExpressionMatrix is a matrix of log₂ expression values where each row corresponds to a gene (probe set) and each column corresponds to an Affymetrix CEL file, which represents a single chip.

Note   If AffinPM and AffinMM affinity data and SequenceMatrix sequence data are not available, you can still use the gcrma function by entering an empty matrix for these inputs in the syntax.

ExpressionMatrix = gcrma( ...'PropertyName', PropertyValue, ...) calls gcrma 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 quotes and is case insensitive. These property name/property value pairs are as follows:

ExpressionMatrix = gcrma(..., 'ChipIndex', ChipIndexValue, ...) computes probe affinities from MM probe intensity data from the chip with the specified column index in MMMatrix, assuming no affinity data is provided. Default ChipIndexValue is 1. If AffinPM and AffinMM affinity data are provided, this property is ignored.

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

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

ExpressionMatrix = gcrma(..., '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.

ExpressionMatrix = gcrma(..., '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.

ExpressionMatrix = gcrma(..., 'GSBCorr', GSBCorrValue, ...) controls whether gene specific binding (GSB) correction is performed on the nonspecific binding (NSB) data. Choices are true (default) or false.

ExpressionMatrix = gcrma(..., 'Normalize', NormalizeValue, ...) controls whether quantile normalization is performed on background adjusted data. Choices are true (default) or false.

ExpressionMatrix = gcrma(..., '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, quantile normalization, and Robust Multi-array Average (RMA) summarization on the Affymetrix microarray probe-level data and create a matrix of expression values.

   expdata = gcrma(pmMatrix, mmMatrix, probeIndices, seqMatrix);

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] Speed, T. (2006). Background models and GCRMA. Lecture 10, Statistics 246, University of California Berkeley. http://www.stat.berkeley.edu/users/terry/Classes/s246.2006/Week10/Week10L1.pdf.

[5] 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: affyprobeseqread, affyread, celintensityread, gcrmabackadj, quantilenorm, rmabackadj, rmasummary

galread

gcrmabackadj