R: Scores transcripts with position weight matrices

scoreTranscripts {transite}

R Documentation

Scores transcripts with position weight matrices

Description

This function is used to count the binding sites in a set of sequences for all or a subset of RNA-binding protein sequence motifs and returns the result in a data frame, which is subsequently used by calculateMotifEnrichment to obtain binding site enrichment scores.

Usage

scoreTranscripts(sequences, motifs = NULL, max.hits = 5,
  threshold.method = "p.value", threshold.value = 0.25^6,
  n.cores = 1, cache = paste0(tempdir(), "/sc/"))

Arguments

`sequences`	character vector of named sequences (only containing upper case characters A, C, G, T), where the names are RefSeq identifiers and sequence type qualifiers (`"3UTR"`, `"5UTR"`, `"mRNA"`), e.g. `"NM_010356\|3UTR"`
`motifs`	a list of motifs that is used to score the specified sequences. If `is.null(motifs)` then all Transite motifs are used.
`max.hits`	maximum number of putative binding sites per mRNA that are counted
`threshold.method`	either `"p.value"` (default) or `"relative"`. If `threshold.method` equals `"p.value"`, the default `threshold.value` is `0.25^6`, which is lowest p-value that can be achieved by hexamer motifs, the shortest supported motifs. If `threshold.method` equals `"relative"`, the default `threshold.value` is `0.9`, which is 90% of the maximum PWM score.
`threshold.value`	semantics of the `threshold.value` depend on `threshold.method` (default is 0.25^6)
`n.cores`	the number of cores that are used
`cache`	either logical or path to a directory where scores are cached. The scores of each motif are stored in a separate file that contains a hash table with RefSeq identifiers and sequence type qualifiers as keys and the number of putative binding sites as values. If `cache` is `FALSE`, scores will not be cached.

Value

A list with three entries:

(1) df: a data frame with the following columns:

`motif.id`	the motif identifier that is used in the original motif library
`motif.rbps`	the gene symbol of the RNA-binding protein(s)
`absolute.hits`	the absolute frequency of putative binding sites per motif in all transcripts
`relative.hits`	the relative, i.e., absolute divided by total, frequency of binding sites per motif in all transcripts
`total.sites`	the total number of potential binding sites
`one.hit`, `two.hits`, ...	number of transcripts with one, two, three, ... putative binding sites

(2) total.sites: a numeric vector with the total number of potential binding sites per transcript

(3) absolute.hits: a numeric vector with the absolute (not relative) number of putative binding sites per transcript

Examples

foreground.set <- c(
  "CAACAGCCUUAAUU", "CAGUCAAGACUCC", "CUUUGGGGAAU",
  "UCAUUUUAUUAAA", "AAUUGGUGUCUGGAUACUUCCCUGUACAU",
  "AUCAAAUUA", "AGAU", "GACACUUAAAGAUCCU",
  "UAGCAUUAACUUAAUG", "AUGGA", "GAAGAGUGCUCA",
  "AUAGAC", "AGUUC", "CCAGUAA"
)
# names are used as keys in the hash table (cached version only)
# ideally sequence identifiers (e.g., RefSeq ids) and region labels
# (e.g., 3UTR for 3'-UTR)
names(foreground.set) <- c(
  "NM_1_DUMMY|3UTR", "NM_2_DUMMY|3UTR", "NM_3_DUMMY|3UTR",
  "NM_4_DUMMY|3UTR", "NM_5_DUMMY|3UTR", "NM_6_DUMMY|3UTR",
  "NM_7_DUMMY|3UTR", "NM_8_DUMMY|3UTR", "NM_9_DUMMY|3UTR",
  "NM_10_DUMMY|3UTR", "NM_11_DUMMY|3UTR", "NM_12_DUMMY|3UTR",
  "NM_13_DUMMY|3UTR", "NM_14_DUMMY|3UTR"
)

# specific motifs, uncached
motifs <- getMotifByRBP("ELAVL1")
scores <- scoreTranscripts(foreground.set, motifs = motifs, cache = FALSE)
## Not run: 
# all Transite motifs, cached (writes scores to disk)
scores <- scoreTranscripts(foreground.set)

# all Transite motifs, uncached
scores <- scoreTranscripts(foreground.set, cache = FALSE)

foreground.df <- transite:::ge$foreground1
foreground.set <- foreground.df$seq
names(foreground.set) <- paste0(foreground.df$refseq, "|",
   foreground.df$seq.type)
scores <- scoreTranscripts(foreground.set)

## End(Not run)