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Binned Motif Enrichment Analysis with monaLisa

Source: R/motif_enrichment_monaLisa.R
calcBinnedMotifEnrR.Rd

This function performs a motif enrichment analysis on bins of sequences. For each bin, the sequences in all other bins are used as background.

Usage

calcBinnedMotifEnrR(
  seqs,
  bins = NULL,
  pwmL = NULL,
  background = c("otherBins", "allBins", "zeroBin", "genome"),
  test = c("fisher", "binomial"),
  maxFracN = 0.7,
  maxKmerSize = 3L,
  min.score = 10,
  matchMethod = "matchPWM",
  GCbreaks = c(0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 0.6, 0.7, 0.8),
  pseudocount.log2enr = 8,
  p.adjust.method = "BH",
  genome = NULL,
  genome.regions = NULL,
  genome.oversample = 2,
  BPPARAM = SerialParam(),
  verbose = FALSE,
  ...
)

Arguments

seqs

DNAStringSet object with sequences to test

bins

factor of the same length and order as seqs, indicating the bin for each sequence. Typically the return value of bin. For background = "genome", bins can be omitted.

pwmL

PWMatrixList with motifs for which to calculate enrichments.

background

A character scalar specifying the background sequences to use. One of "otherBins" (default), "allBins", "zeroBin" or "genome" (see "Details").

test

A character scalar specifying the type of enrichment test to perform. One of "fisher" (default) or "binomial". The enrichment test is one-sided (enriched in foreground).

maxFracN

A numeric scalar with the maximal fraction of N bases allowed in a sequence (defaults to 0.7). Sequences with higher fractions are excluded from the analysis.

maxKmerSize

the maximum k-mer size to consider, when adjusting background sequence weights for k-mer composition compared to the foreground sequences. The default value (3) will correct for mono-, di- and tri-mer composition.

min.score

the minimal score for motif hits, used in findMotifHits.

matchMethod

the method used to scan for motif hits, passed to the method parameter in findMotifHits.

GCbreaks

The breaks between GC bins. The default value is based on the hard-coded bins used in Homer.

pseudocount.log2enr

A numerical scalar with the pseudocount to add to foreground and background counts when calculating log2 motif enrichments

p.adjust.method

A character scalar selecting the p value adjustment method (used in p.adjust).

genome

A BSgenome or DNAStringSet object with the genome sequence. Only used for background = "genome" for extracting background sequences.

genome.regions

An optional GRanges object defining the intervals in genome from which background sequences are sampled for background = "genome". If NULL, background sequences are sampled randomly from genome.

genome.oversample

A numeric scalar of at least 1.0 defining how many background sequences will be sampled per foreground sequence for background = "genome". Larger values will take longer but improve the sequence composition similarity between foreground and background (see "Details").

BPPARAM

An optional BiocParallelParam instance determining the parallel back-end to be used during evaluation.

verbose

A logical scalar. If TRUE, print progress messages.

...

Additional arguments for findMotifHits.

Value

A SummarizedExperiment object with motifs in rows and bins in columns, containing seven assays:

negLog10P

: -log10 P values

negLog10Padj

: -log10 adjusted P values

pearsonResid

: motif enrichments as Pearson residuals

expForegroundWgtWithHits

: expected number of foreground sequences with motif hits

log2enr

: motif enrichments as log2 ratios

sumForegroundWgtWithHits

: Sum of foreground sequence weights in a bin that have motif hits

sumBackgroundWgtWithHits

: Sum of background sequence weights in a bin that have motif hits

The rowData of the object contains annotations (name, PFMs, PWMs and GC fraction) for the motifs, while the colData slot contains summary information about the bins.

Details

This function implements a binned motif enrichment analysis. In each enrichment analysis, the sequences in a specific bin are used as foreground sequences to test for motif enrichments comparing to background sequences (defined by background, see below). The logic follows the findMotifsGenome.pl tool from Homer version 4.11, with -size given -nomotif -mknown and additionally -h if using test = "fisher", and gives very similar results. As in the Homer tool, sequences are weighted to correct for GC and k-mer composition differences between fore- and background sets.

The background sequences are defined according to the value of the background argument:

otherBins

: sequences from all other bins (excluding the current bin)

allBins

: sequences from all bins (including the current bin)

zeroBin

: sequences from the "zero bin", defined by the maxAbsX argument of bin. If bins does not define a "zero bin", for example because it was created by bin(..., maxAbsX = NULL), selecting this background definition will abort with an error.

genome

: sequences randomly sampled from the genome (or the intervals defined in genome.regions if given). For each foreground sequence, genome.oversample background sequences of the same size are sampled (on average). From these, one per foreground sequence is selected trying to match the G+C composition. In order to make the sampling deterministic, a seed number needs to be provided to the RNGseed parameter in SerialParam or MulticoreParam when creating the BiocParallelParam instance in BPPARAM.

Motif hits are predicted using findMotifHits and multiple hits per sequence are counted as just one hit (ZOOPS mode). For each motif, the weights of sequences that have a hit are summed separately for foreground (sumForegroundWgtWithHits) and background (sumBackgroundWgtWithHits). The total foreground (totalWgtForeground) and background (totalWgtBackground) sum of sequence weights is also calculated. If a motif has zero sumForegroundWgtWithHits and sumBackgroundWgtWithHits, then any values (p-values and enrichment) that are calculated using these two numbers are set to NA.

Two statistical tests for the calculation of enrichment log p-value are available: test = "fisher" (default) to perform Fisher's exact tests, or test = "binomial" to perform binomial tests (default in Homer), using:

fisher

: fisher.test(x = tab, alternative = "greater"), where tab is the contingency table with the summed weights of sequences in foreground or background sets (rows), and with or without a hit for a particular motif (columns).

binomial

: pbinom(q = sumForegroundWgtWithHits - 1, size = totalWgtForeground, prob = sumBackgroundWgtWithHits / totalWgtBackground, lower.tail = FALSE, log.p = TRUE)

Examples

seqs <- Biostrings::DNAStringSet(c("GTCAGTCGATC", "CAGTCTAGCTG",
                                   "CGATCGTCAGT", "AGCTGCAGTCT"))
bins <- factor(rep(1:2, each = 2))
m <- rbind(A = c(2, 0, 0),
           C = c(1, 1, 0),
           G = c(0, 2, 0),
           T = c(0, 0, 3))
pwms <- TFBSTools::PWMatrixList(
    TFBSTools::PWMatrix(ID = "m1", profileMatrix = m),
    TFBSTools::PWMatrix(ID = "m2", profileMatrix = m[, 3:1])
)
calcBinnedMotifEnrR(seqs = seqs, bins = bins, pwmL = pwms,
                    min.score = 3)
#> class: SummarizedExperiment 
#> dim: 2 2 
#> metadata(5): bins bins.binmode bins.breaks bins.bin0 param
#> assays(7): negLog10P negLog10Padj ... sumForegroundWgtWithHits
#>   sumBackgroundWgtWithHits
#> rownames(2): m1 m2
#> rowData names(5): motif.id motif.name motif.pfm motif.pwm
#>   motif.percentGC
#> colnames(2): 1 2
#> colData names(6): bin.names bin.lower ... totalWgtForeground
#>   totalWgtBackground