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# affyprobeaffinities

Compute Affymetrix probe affinities from their sequences and MM probe intensities

## Syntax

```[AffinPM, AffinMM] = affyprobeaffinities(SequenceMatrix, MMIntensity) [AffinPM, AffinMM, BaseProf] = affyprobeaffinities(SequenceMatrix, MMIntensity) [AffinPM, AffinMM, BaseProf, Stats] = affyprobeaffinities(SequenceMatrix, MMIntensity) ... = affyprobeaffinities(SequenceMatrix, MMIntensity, ...'ProbeIndices', ProbeIndicesValue, ...) ... = affyprobeaffinities(SequenceMatrix, MMIntensity, ...'Showplot', ShowplotValue, ...) ```

## Input Arguments

`SequenceMatrix`

An N-by-25 matrix of sequence information for the perfect match (PM) probes on an Affymetrix® GeneChip® array, where N is the number of probes on the array. Each row corresponds to a probe, and each column corresponds to one of the 25 sequence positions. Nucleotides in the sequences are represented by one of the following integers:

• `0` — None

• `1` — A

• `2` — C

• `3` — G

• `4` — T

### Tip

You can use the `affyprobeseqread` function to generate this matrix. If you have this sequence information in letter representation, you can convert it to integer representation using the `nt2int` function.

`MMIntensity`

Column vector containing mismatch (MM) probe intensities from a CEL file, generated from a single Affymetrix GeneChip array. Each row corresponds to a probe.

### Tip

You can extract this column vector from the `MMIntensities` matrix returned by the `celintensityread` function.

`ProbeIndicesValue`

Column vector containing probe indexing information. Probes within a probe set are numbered 0 through N - 1, where N is the number of probes in the probe set.

### Tip

You can use the `affyprobeseqread` function to generate this column vector.

`ShowplotValue`

Controls the display of a plot showing the affinity values of each of the four bases (A, C, G, and T) for each of the 25 sequence positions, for all probes on the Affymetrix GeneChip array. Choices are `true` or `false` (default).

## Output Arguments

 `AffinPM` Column vector of PM probe affinities, computed from their probe sequences and MM probe intensities. `AffinMM` Column vector of MM probe affinities, computed from their probe sequences and MM probe intensities. `BaseProf` 4-by-4 matrix containing the four parameters for a polynomial of degree 3, for each base, A, C, G, and T. Each row corresponds to a base, and each column corresponds to a parameter. These values are estimated from the probe sequences and intensities, and represent all probes on an Affymetrix GeneChip array. `Stats` Row vector containing four statistics in the following order:R-square statisticF statisticp-valueError variance

## Description

```[AffinPM, AffinMM] = affyprobeaffinities(SequenceMatrix, MMIntensity)``` returns a column vector of PM probe affinities and a column vector of MM probe affinities, computed from their probe sequences and MM probe intensities. Each row in `AffinPM` and `AffinMM` corresponds to a probe. NaN is returned for probes with no sequence information. Each probe affinity is the sum of position-dependent base affinities. For a given base type, the positional effect is modeled as a polynomial of degree 3.

```[AffinPM, AffinMM, BaseProf] = affyprobeaffinities(SequenceMatrix, MMIntensity)``` also estimates affinity coefficients using multiple linear regression. It returns `BaseProf`, a 4-by-4 matrix containing the four parameters for a polynomial of degree 3, for each base, A, C, G, and T. Each row corresponds to a base, and each column corresponds to a parameter. These values are estimated from the probe sequences and intensities, and represent all probes on an Affymetrix GeneChip array.

```[AffinPM, AffinMM, BaseProf, Stats] = affyprobeaffinities(SequenceMatrix, MMIntensity)``` also returns `Stats`, a row vector containing four statistics in the following order:

• R-square statistic

• F statistic

• p-value

• Error variance

```... = affyprobeaffinities(SequenceMatrix, MMIntensity, ...'PropertyName', PropertyValue, ...)``` calls `affyprobeaffinities` 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:

``` ... = affyprobeaffinities(SequenceMatrix, MMIntensity, ...'ProbeIndices', ProbeIndicesValue, ...)``` uses probe indices to normalize the probe intensities with the median of their probe set intensities.

### Tip

Use of the `ProbeIndices` property is recommended only if your `MMIntensity` data are not from a nonspecific binding experiment.

```... = affyprobeaffinities(SequenceMatrix, MMIntensity, ...'Showplot', ShowplotValue, ...)``` controls the display of a plot of the probe affinity base profile. Choices are `true` or `false` (default).

## Examples

collapse all

This example shows how to calculate Affymetrix PM and MM probe affinities from their sequences and MM probe intensities.

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, `mmMatrix` , a matrix containing 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, and also plot the affinity values of each of the four bases (A, C, G, and T) for each of the 25 sequence positions, for all probes on the Affymetrix GeneChip array.

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

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

## References

[1] Naef, F., and Magnasco, M.O. (2003). Solving the Riddle of the Bright Mismatches: Labeling and Effective Binding in Oligonucleotide Arrays. Physical Review E 68, 011906.

[2] 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.

[3] 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.