score_transcripts {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 `calculate_motif_enrichment` to obtain binding site enrichment scores.

### Usage

    score_transcripts(
      sequences,
      motifs = NULL,
      max_hits = 5,
      threshold_method = c("p_value", "relative"),
      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

### See Also

Other matrix functions: `calculate_motif_enrichment()`, `run_matrix_spma()`, `run_matrix_tsma()`, `score_transcripts_single_motif()`

### 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 <- get_motif_by_rbp("ELAVL1")
    scores <- score_transcripts(foreground_set, motifs = motifs, cache = FALSE)
    ## Not run:
    # all Transite motifs, cached (writes scores to disk)
    scores <- score_transcripts(foreground_set)

    # all Transite motifs, uncached
    scores <- score_transcripts(foreground_set, cache = FALSE)

    foreground_df <- transite:::ge$foreground1_df
    foreground_set <- foreground_df$seq
    names(foreground_set) <- paste0(foreground_df$refseq, "|",
       foreground_df$seq_type)
    scores <- score_transcripts(foreground_set)

    ## End(Not run)

[Package transite version 1.10.0 Index]