monaLisa
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.
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,
...
)
DNAStringSet
object with sequences to
test
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.
PWMatrixList with motifs for which to calculate enrichments.
A character
scalar specifying the background
sequences to use. One of "otherBins"
(default), "allBins"
,
"zeroBin"
or "genome"
(see "Details").
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).
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.
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.
the minimal score for motif hits, used in
findMotifHits
.
the method used to scan for motif hits, passed to the
method
parameter in findMotifHits
.
The breaks between GC bins. The default value is based on the hard-coded bins used in Homer.
A numerical scalar with the pseudocount to add to foreground and background counts when calculating log2 motif enrichments
A character scalar selecting the p value adjustment
method (used in p.adjust
).
A BSgenome
or DNAStringSet
object with the
genome sequence. Only used for background = "genome"
for extracting
background sequences.
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
.
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"
).
An optional BiocParallelParam
instance determining the parallel back-end to be used during evaluation.
A logical scalar. If TRUE
, print progress messages.
Additional arguments for findMotifHits
.
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.
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)
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