run_kmer_spma: _k_-mer-based Spectrum Motif Analysis

In transite: RNA-binding protein 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,
  midpoint = 0,
  x_value_limits = NULL,
  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
midpoint	for enrichment values the midpoint should be 1, for log enrichment values 0 (defaults to 0)
x_value_limits	sets limits of the x-value color scale (used to harmonize color scales of different spectrum plots), see limits argument of continuous_scale (defaults to NULL, i.e., the data-dependent default scale range)
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)

transite documentation built on Nov. 8, 2020, 5:27 p.m.