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

* ExpressionMatrix* = gcrma(

`PMMatrix`

`MMMatrix`

`ProbeIndices`

`AffinPM`

`AffinMM`

`ExpressionMatrix`

`PMMatrix`

`MMMatrix`

`ProbeIndices`

`SequenceMatrix`

`ExpressionMatrix`

`ChipIndexValue`

`ExpressionMatrix`

`OpticalCorrValue`

`ExpressionMatrix`

`CorrConstValue`

`ExpressionMatrix`

`MethodValue`

`ExpressionMatrix`

`TuningParamValue`

`ExpressionMatrix`

`GSBCorrValue`

`ExpressionMatrix`

`NormalizeValue`

`ExpressionMatrix`

`VerboseValue`

`PMMatrix` | Matrix of intensity values where each row corresponds
to a perfect match (PM) probe and each column corresponds to an Affymetrix ## TipYou can use the |

`MMMatrix` | Matrix of intensity values where each row corresponds to a mismatch (MM) probe and each column corresponds to an Affymetrix CEL file. (Each CEL file is generated from a separate chip. All chips should be of the same type.) ## TipYou can use the |

`ProbeIndices` | Column vector containing probe indices. Probes within
a probe set are numbered 0 through ## TipYou can use the |

`AffinPM` | Column vector of PM probe affinities.## TipYou can use the |

`AffinMM` | Column vector of MM probe affinities.## TipYou can use the |

`SequenceMatrix` | An `0` — None`1` — A`2` — C`3` — G`4` — T
## TipYou can use the |

`ChipIndexValue` | Positive integer specifying a column index in ,
which specifies a chip. This chip intensity data is used to compute
probe affinities. Default is `MMMatrix` `1` . |

`OpticalCorrValue` | Controls the use of optical background correction on the PM
and MM intensity values in and `PMMatrix` .
Choices are `MMMatrix` `true` (default) or `false` . |

`CorrConstValue` | Value that 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` . |

`MethodValue` | Character vector that 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` . |

`TuningParamValue` | Value that 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).## TipFor information on determining a setting for this parameter, see Wu et al., 2004. |

`GSBCorrValue` | Specifies whether to perform gene-specific binding (GSB) correction
using probe affinity data. Choices are `true` (default)
or `false` . If there is no probe affinity information,
this property is ignored. |

`NormalizeValue` | Controls whether quantile normalization is performed on background
adjusted data. Choices are `true` (default) or `false` . |

`VerboseValue` | Controls the display of a progress report showing the
number of each chip as it is completed. Choices are |

`ExpressionMatrix` | Matrix of log_{2} 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. |

performs
GCRMA background adjustment, quantile normalization, and median-polish
summarization on Affymetrix microarray probe-level data using
probe affinity data. * ExpressionMatrix* = gcrma(

`PMMatrix`

`MMMatrix`

`ProbeIndices`

`AffinPM`

`AffinMM`

`ExpressionMatrix`

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

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* = gcrma(

`PMMatrix`

`MMMatrix`

`ProbeIndices`

`SequenceMatrix`

`ExpressionMatrix`

If * AffinPM* and

`AffinMM`

`SequenceMatrix`

`gcrma`

function
by entering an empty matrix for these inputs in the syntax.

calls * ExpressionMatrix* = gcrma(
...'

`PropertyName`

`PropertyValue`

`gcrma`

with optional properties
that use property name/property value pairs. You can specify one or
more properties in any order. Each `PropertyName`

```
```

computes probe affinities from MM probe intensity
data from the chip with the specified column index in * ExpressionMatrix* =
gcrma(..., 'ChipIndex',

`ChipIndexValue`

`MMMatrix`

`ChipIndexValue`

`1`

.
If `AffinPM`

`AffinMM`

controls
the use of optical background correction on the PM and MM intensity
values in * ExpressionMatrix* = gcrma(...,
'OpticalCorr',

`OpticalCorrValue`

`PMMatrix`

`MMMatrix`

`true`

(default) or `false`

.

specifies
the correlation constant, rho, for background intensity for each PM/MM
probe pair. Choices are any value * ExpressionMatrix* = gcrma(...,
'CorrConst',

`CorrConstValue`

`≥ 0`

and ```
≤
1
```

. Default is `0.7`

.

specifies
the method to estimate the signal. Choices are * ExpressionMatrix* = gcrma(...,
'Method',

`MethodValue`

`MLE`

,
a faster, ad hoc Maximum Likelihood Estimate method, or `EB`

,
a slower, more formal, empirical Bayes method. Default is `MLE`

.

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 * ExpressionMatrix* = gcrma(...,
'TuningParam',

`TuningParamValue`

`5`

(MLE) or `0.5`

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

specifies
whether to perform gene specific binding (GSB) correction using probe
affinity data. Choices are * ExpressionMatrix* = gcrma(...,
'GSBCorr',

`GSBCorrValue`

`true`

(default) or `false`

.
If there is no probe affinity information, this property is ignored.

controls
whether quantile normalization is performed on background adjusted
data. Choices are * ExpressionMatrix* = gcrma(...,
'Normalize',

`NormalizeValue`

`true`

(default) or `false`

.

controls
the display of a progress report showing the number of each chip as
it is completed. Choices are * ExpressionMatrix* = gcrma(...,
'Verbose',

`VerboseValue`

`true`

(default) or `false`

.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`

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

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

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.

[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.

`affygcrma`

| `affyprobeseqread`

| `affyread`

| `affyrma`

| `celintensityread`

| `gcrmabackadj`

| `quantilenorm`

| `rmabackadj`

| `rmasummary`

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