run_kmer_spma {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

    run_kmer_spma(
      sorted_transcript_sequences,
      sorted_transcript_values = NULL,
      transcript_values_label = "transcript value",
      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

`sorted_transcript_sequences` | 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 `sorted_transcript_sequences` 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).
`sorted_transcript_values` | vector of sorted transcript values, i.e., the fold change or signal-to-noise ratio or any other quantity that was used to sort the transcripts that were passed to `run_matrix_spma` or `run_kmer_spma` (default value is `NULL`). These values are displayed as a semi-transparent area over the enrichment value heatmaps of spectrum plots.
`transcript_values_label` | label of transcript sorting criterion (e.g., `"log fold change"`, default value is `"transcript value"`), only shown if `!is.null(sorted_transcript_values)`
`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 `p_combine`)
`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 `run_kmer_tsma` for the binned data
`spectrum_info_df` | a data frame with the SPMA results
`spectrum_plots` | a list of spectrum plots, as generated by `score_spectrum`
`classifier_scores` | a list of classifier scores, as returned by `classify_spectrum`

### See Also

Other SPMA functions: `classify_spectrum()`, `run_matrix_spma()`, `score_spectrum()`, `subdivide_data()`

Other k-mer functions: `calculate_kmer_enrichment()`, `check_kmers()`, `compute_kmer_enrichment()`, `count_homopolymer_corrected_kmers()`, `draw_volcano_plot()`, `estimate_significance_core()`, `estimate_significance()`, `generate_kmers()`, `generate_permuted_enrichments()`, `run_kmer_tsma()`

### Examples

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

    results <- run_kmer_spma(background_seqs,
                             sorted_transcript_values = background_df$value,
                             transcript_values_label = "signal-to-noise ratio",
                             motifs = get_motif_by_id("M178_0.6"),
                             n_bins = 20,
                             fg_permutations = 10)

    ## Not run:
    results <- run_kmer_spma(background_seqs,
                             sorted_transcript_values = background_df$value,
                             transcript_values_label = "signal-to-noise ratio")
    ## End(Not run)

[Package transite version 1.6.2 Index]