selex.run {SELEX} | R Documentation

## Run a standard SELEX analysis

### Description

A function used to, in one shot,

- Determine kmax on the `crossValidationSample` with the minimum count determined by `minCount`

- Build a Markov model on the `trainingSample` and test it on the `crossValidationSample` with kmax length K-mers used to determine model fit, and constructed using `mmMethod`

- Calculate information gain for `infoRange` K-mer lengths on the `infoGainSample`, using the Markov model order with the highest R^2 to predict previous round values.

### Usage

    selex.run(trainingSample, crossValidationSample, minCount=100, infoGainSample,
      infoRange=NULL, mmMethod="DIVISION", mmWithLeftFlank=FALSE)

### Arguments

`trainingSample` | A sample handle to the training dataset.
`crossValidationSample` | A sample handle to the cross-validation dataset.
`minCount` | The minimum count to be used.
`infoGainSample` | A sample handle to the dataset on which to perform the information gain analysis.
`infoRange` | The range of K-mer lengths for which the information gain should be calculated. If `NULL`, the range is automatically set to start from the optimal Markov model order + 1 to the length of the variable region. This is the same as `k` in `selex.infogain`.
`mmMethod` | A character string indicating the algorithm used to evaluate the Markov model conditional probabilities. Can be either `"DIVISION"` (default) or `"TRANSITION"`.
`mmWithLeftFlank` | Predict expected counts by considering the sequences in the left flank of the variable region.

### Details

Please see the individual functions or ‘References’ for more details.

### Value

Not applicable

### References

Slattery, M., Riley, T.R., Liu, P., Abe, N., Gomez-Alcala, P., Dror, I., Zhou, T., Rohs, R., Honig, B., Bussemaker, H.J.,and Mann, R.S. (2011) Cofactor binding evokes latent differences in DNA binding specificity between Hox proteins. Cell 147:1270–1282.

Riley, T.R., Slattery, M., Abe, N., Rastogi, C., Liu, D., Mann, R.S., and Bussemaker, H.J. (2014) SELEX-seq: a method for characterizing the complete repertoire of binding site preferences for transcription factor complexes. Methods Mol. Biol. 1196:255–278.

### See Also

`selex.counts`, `selex.countSummary`, `selex.infogain`, `selex.infogainSummary`, `selex.mm`, `selex.mmSummary`

### Examples

    #Initialize the SELEX package
    #options(java.parameters="-Xmx1500M")
    #library(SELEX)

    # Configure the current session
    workDir = file.path(".", "SELEX_workspace")
    selex.config(workingDir=workDir,verbose=FALSE, maxThreadNumber= 4)

    # Extract sample data from package, including XML database
    sampleFiles = selex.exampledata(workDir)

    # Load all sample files using XML database
    selex.loadAnnotation(sampleFiles[3])

    # Create sample handles
    r0 = selex.sample(seqName="R0.libraries", sampleName="R0.barcodeGC", round=0)
    r2 = selex.sample(seqName='R2.libraries', sampleName='ExdHox.R2', round=2)

    # Split the r0 sample into testing and training datasets
    r0.split = selex.split(sample=r0)

    # Run entire analysis
    selex.run(trainingSample=r0.split$train, crossValidationSample=r0.split$test,
      infoGainSample=r2)

    # Display results
    selex.mmSummary()[,c(1,2,3,4,5,6)]
    selex.infogainSummary()[,c(1,2,3,4,5)]

[Package SELEX version 1.18.0 Index]