predictTestSet {cleanUpdTSeq} | R Documentation

## predict authenticity of putative pA sites

### Description

classify putative pA sites into true and false bins.

### Usage

    predictTestSet(
      Ndata.NaiveBayes = NULL,
      Pdata.NaiveBayes = NULL,
      testSet.NaiveBayes,
      classifier = NULL,
      outputFile = "test-predNaiveBayes.tsv",
      assignmentCutoff = 0.5,
      return_sequences = FALSE
    )

### Arguments

`Ndata.NaiveBayes` | A data.frame, containing features for the negative training data, which is built using the function `buildFeatureVector`. It is described further in`data.NaiveBayes`.
`Pdata.NaiveBayes` | A data.frame, containing features for the positive training data, which is built using the function `buildFeatureVector`. It is described further in`data.NaiveBayes`.
`testSet.NaiveBayes` | An object of `featureVector` for test data built for Naive Bayes analysis using the function `buildFeatureVector`.
`classifier` | An object of class PASclassifier.
`outputFile` | A character(1) vector, file name for outputting prediction results. The prediction output is written to the file, tab separated.
`assignmentCutoff` | A numeric(1) vector, specifying the cutoff for classifying a putative pA site into a true or false pA class. It should be any number between 0 and 1. For example, assignmentCutoff = 0.5 will assign an putative pA site with prob_true_pA > 0.5 to the True class (1), and any putative pA site with prob_true_pA < = 0.5 as False (0).
`return_sequences` | A logical(1) vector, indicating whether upstream and downstream sequences should be included in the output

### Value

A data.frame including all info as described below. The upstream and downstream sequence used in assessing the putative pA site might be included when return_sequences = TRUE.

`peak_name` | the name of the putative pA site (originally from the 4th field in the bed file).
`prob_fake_pA` | the probability that the putative pA site is false
`prob_true_pA` | the probability that the putative pA site is true
`pred_class` | the predicted class of the putative pA site, based on the assignment cutoff. 0 = Falsee/oligo(dT) internally primed, 1 = True
`upstream_seq` | the upstream sequence of the putative pA site used in the analysis
`downstream_seq` | the downstream sequence of the putative pA site used in the analysis.

### Author(s)

Sarah Sheppard, Haibo Liu, Jianhong Ou, Nathan Lawson, Lihua J. Zhu

### References

Sheppard S, Lawson ND, Zhu LJ. Accurate identification of polyadenylation sites from 3' end deep sequencing using a naive Bayes classifier. Bioinformatics. 2013;29(20):2564-2571.

### Examples

    library(BSgenome.Drerio.UCSC.danRer7)
    testFile <- system.file("extdata", "test.bed",
                            package = "cleanUpdTSeq")
    ## convert the test set to GRanges without upstream and downstream sequence
    ## information
    peaks <- BED6WithSeq2GRangesSeq(file = testFile,
                                   skip = 1L, withSeq = TRUE)
    ## build the feature vector for the test set without sequence information
    testSet.NaiveBayes = buildFeatureVector(peaks,
                                            genome = Drerio,
                                            upstream = 40L,
                                            downstream = 30L,
                                            wordSize = 6L,
                                            alphabet = c("ACGT"),
                                            sampleType = "unknown",
                                            replaceNAdistance = 30,
                                            method = "NaiveBayes",
                                            fetchSeq = TRUE,
                                            return_sequences = TRUE)
    data(data.NaiveBayes)
    ## sample the test data for code testing, DO NOT do this for real data
    samp <- c(1:22, sample(23:4118, 50), 4119, 4120)
    Ndata.NaiveBayes <- data.NaiveBayes$Negative[, samp]
    Pdata.NaiveBayes <- data.NaiveBayes$Positive[, samp]
    testSet.NaiveBayes@data <- testSet.NaiveBayes@data[, samp[-1]-1]

    test_out <- predictTestSet(Ndata.NaiveBayes,
                               Pdata.NaiveBayes,
                               testSet.NaiveBayes,
    	                          outputFile = tempfile(),
                               assignmentCutoff = 0.5)

[Package cleanUpdTSeq version 1.30.0 Index]