runKmerSPMA {transite} | R Documentation

## k-mer-based Spectrum Motif Analysis

### Description

SPMA helps to illuminate the relationship between RBP binding evidence and the transcript sorting criterion, e.g., fold change between treatment and control samples.

### Usage

    runKmerSPMA(background.set, motifs = NULL, k = 6, n.bins = 40,
      max.model.degree = 1, max.cs.permutations = 1e+07,
      min.cs.permutations = 5000, fg.permutations = 5000,
      p.adjust.method = "BH", p.combining.method = "fisher", n.cores = 1)

### Arguments

`background.set` | character vector of ranked sequences, either DNA (only containing upper case characters A, C, G, T) or RNA (A, C, G, U). The sequences in `background.set` must be ranked (i.e., sorted). Commonly used sorting criteria are measures of differential expression, such as fold change or signal-to-noise ratio (e.g., between treatment and control samples in gene expression profiling experiments).
`motifs` | a list of motifs that is used to score the specified sequences. If `is.null(motifs)` then all Transite motifs are used.
`k` | length of k-mer, either `6` for hexamers or `7` for heptamers
`n.bins` | specifies the number of bins in which the sequences will be divided, valid values are between 7 and 100
`max.model.degree` | maximum degree of polynomial
`max.cs.permutations` | maximum number of permutations performed in Monte Carlo test for consistency score
`min.cs.permutations` | minimum number of permutations performed in Monte Carlo test for consistency score
`fg.permutations` | numer of foreground permutations
`p.adjust.method` | see `p.adjust`
`p.combining.method` | one of the following: Fisher (1932) (`"fisher"`), Stouffer (1949), Liptak (1958) (`"SL"`), Mudholkar and George (1979) (`"MG"`), and Tippett (1931) (`"tippett"`) (see `pCombine`)
`n.cores` | number of computing cores to use

### Details

In order to investigate how motif targets are distributed across a spectrum of transcripts (e.g., all transcripts of a platform, ordered by fold change), Spectrum Motif Analysis visualizes the gradient of RBP binding evidence across all transcripts.

The k-mer-based approach differs from the matrix-based approach by how the sequences are scored. Here, sequences are broken into k-mers, i.e., oligonucleotide sequences of k bases. And only statistically significantly enriched or depleted k-mers are then used to calculate a score for each RNA-binding protein, which quantifies its target overrepresentation.

### Value

A list with the following components:

`foreground.scores` | the result of `runKmerTSMA` for the binned data
`spectrum.info.df` | a data frame with the SPMA results
`spectrum.plots` | a list of spectrum plots, as generated by `scoreSpectrum`
`classifier.scores` | a list of classifier scores, as returned by `spectrumClassifier`

### See Also

Other SPMA functions: `runMatrixSPMA`, `scoreSpectrum`, `spectrumClassifier`, `subdivideData`

Other k-mer functions: `calculateKmerEnrichment`, `checkKmers`, `computeKmerEnrichment`, `drawVolcanoPlot`, `empiricalEnrichmentMeanCDF`, `generateKmers`, `generatePermutedEnrichments`, `homopolymerCorrection`, `permTestGeometricMean`, `runKmerTSMA`

### Examples

    # example data set
    background.df <- transite:::ge$background
    # sort sequences by signal-to-noise ratio
    background.df <- dplyr::arrange(background.df, value)
    # character vector of named and ranked (by signal-to-noise ratio) sequences
    background.set <- gsub("T", "U", background.df$seq)
    names(background.set) <- paste0(background.df$refseq, "|",
      background.df$seq.type)

    results <- runKmerSPMA(background.set,
                           motifs = getMotifById("M178_0.6"),
                           n.bins = 20,
                           fg.permutations = 10)

    ## Not run:
    results <- runKmerSPMA(background.set)
    ## End(Not run)

[Package transite version 1.2.1 Index]