selex.mm {SELEX} | R Documentation

## Build or retrieve a Markov model

### Description

A function used to compute and store a Markov model built by training and cross-validating on specified files. It returns a Markov model handle to conveniently reference the calculated model in other SELEX functions.

### Usage

    selex.mm(sample, order=NA, crossValidationSample=NULL, Kmax= NULL,
      seqfilter=NULL, mmMethod="DIVISION", mmWithLeftFlank=FALSE)

### Arguments

`sample` | A sample handle to the training dataset.
`order` | The order of the Markov model to be built. If `NA`, builds Markov models of all orders from `0` to `Kmax-1`, selecting the one with highest R^2.
`crossValidationSample` | A sample handle to the cross-validation dataset. If `NULL`, no R^2 value will be provided.
`Kmax` | The K-mer length to determine model fit on. If `NA`, automatically finds kmax with a minimum count set to 100. See `selex.kmax` and ‘Details’.
`seqfilter` | A sequence filter object to include/exclude sequences that are read in from the FASTQ file.
`mmMethod` | A character string indicating the algorithm used to evaluate the Markov model conditional probabilities. Can be either `"DIVISION"` (default) or `"TRANSITION"`. See ‘Details’.
`mmWithLeftFlank` | Predict expected counts by considering the sequences in the left flank of the variable region. See ‘Details’.

### Details

`selex.mm` builds an N-th order Markov model by training the model on the `sample` dataset and tests its accuracy by attempting to predict the K-mer counts of the cross-validation dataset. This K-mer length is determined either by setting `Kmax` or by an internal call to `selex.kmax`. If a cross-validation dataset does not exist, `selex.split` can be used to partition a dataset into testing and training datasets.
The Markov model uses conditional probabilities to predict the probability of observing a given K-mer. For example, let us consider the following 6-mer sequence:
AGGCTA
If a fourth-order Markov model were to predict the prior observation probability of the above sequence, the following would have to be evaluated:

p(AGGCTA) = p(AGG) p(C|AGG) p(T|GGC) p(A|GCT)

These conditional probabilities can be evaluated using one of two algorithms. The `TRANSITION` algorithm relies on K-mer counts of length equal to the order of the Markov model. For the example above,

p(C|AGG) = count(AGGC) / [count(AGGA) + count(AGGC) + count(AGGG) + count(AGGT)]

The `DIVISION` algorithm relies on K-mer frequencies for lengths equal to Markov model order and order-1. For the example above,

p(C|AGG) = P(AGGC) / P(AGG) = [count(AGGC) / (total 4-mer counts)] / [count(AGG) / (total 3-mer counts)]

The the flanking sequences in the left flank of the variable region can be taken into consideration when predicting prior observation probabilities by setting `mmWithLeftFlank` to `TRUE`. If the left barcode of the sequence in the example above is TGG, P(AGG) in the prior probability becomes

p(AGG) = p(A|TGG) p(G|GGA) p(G|GAG).

After computation, the Markov model is stored in the working directory. When a new `seqfilter` object is provided, the Markov model is reconstructed. See `selex.seqfilter` for more details. See ‘References’ for more details regarding the model construction process.
`selex.mmSummary` can be used to view the R^2 values for all orders that have been computed for the Markov model. If `crossValidationSample` is `NULL`, the resulting Markov model will not be displayed by `selex.mmSummary`.

### Value

`selex.mm` returns a Markov model handle.

### 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.infogain`, `selex.kmax`, `selex.mmSummary`, `selex.run`, `selex.split`

### Examples

    mm = selex.mm(sample=r0.split$train, order=NA, crossValidationSample=r0.split$test)

    # View Markov model R^2 values
    selex.mmSummary()

[Package SELEX version 1.26.0 Index]