runKmerSPMA {transite} | R Documentation |
SPMA helps to illuminate the relationship between RBP binding evidence and the transcript sorting criterion, e.g., fold change between treatment and control samples.
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)
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 |
motifs |
a list of motifs that is used to score the specified sequences.
If |
k |
length of k-mer, either |
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.combining.method |
one of the following: Fisher (1932)
( |
n.cores |
number of computing cores to use |
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.
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
|
Other SPMA functions: runMatrixSPMA
,
scoreSpectrum
,
spectrumClassifier
,
subdivideData
Other k-mer functions: calculateKmerEnrichment
,
checkKmers
,
computeKmerEnrichment
,
drawVolcanoPlot
,
empiricalEnrichmentMeanCDF
,
generateKmers
,
generatePermutedEnrichments
,
homopolymerCorrection
,
permTestGeometricMean
,
runKmerTSMA
# 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)