R/testMotifPos.R
testMotifPos.Rd
Test for a Uniform Distribution across a set of best matches
A Position Weight Matrix, universalmotif object or list thereof.
Alternatively can be a single DataFrame or list of DataFrames as returned
by getPwmMatches with best_only = TRUE
An XStringSet. Not required if matches are supplied as x
Width of bins across the range to group data into
Use absolute positions around zero to find symmetrical enrichment
logical(1) Also find matches using the reverse complement of pwm
The minimum score to return a match
Choose how to resolve matches with tied scores
Alternative hypothesis for the binomial test
Column to sort results by
Passed to mclapply
Passed to matchPWM
A data.frame with columns start
, end
, centre
, width
, total_matches
,
matches_in_region
, expected
, enrichment
, prop_total
, p
and consensus_motif
The total matches represent the total number of matches within the set of sequences, whilst the number observed in the final region are also given, along with the proportion of the total this represents. Enrichment is simply the ratio of observed to expected based on the expectation of the null hypothesis
The consensus motif across all matches is returned as a Position Frequency Matrix (PFM) using consensusMatrix.
This function tests for an even positional spread of motif matches across a set of sequences, using the assumption (i.e. H~0~) that if there is no positional bias, matches will be evenly distributed across all positions within a set of sequences. Conversely, if there is positional bias, typically but not necessarily near the centre of a range, this function intends to detect this signal, as a rejection of the null hypothesis.
Input can be provided as the output from getPwmMatches setting
best_only = TRUE
if these matches have already been identified.
If choosing to provide this object to the argument matches
, nothing is required
for the arguments pwm
, stringset
, rc
, min_score
or break_ties
Otherwise, a Position Weight Matrix (PWM) and an XStringSet
are required,
along with the relevant arguments, with best matches identified within the
function.
The set of best matches are then grouped into bins along the range, with the
central bin containing zero, and tallied.
Setting abs
to TRUE
will set all positions from the centre as
absolute values, returning counts purely as bins with distances from zero,
marking this as an inclusive lower bound.
Motif alignments are assigned into bins based on the central position of the
match, as provided in the column from_centre
when calling
getPwmMatches.
The binom.test is performed on each bin using the alternative hypothesis, with the returned p-values across all bins combined using the Harmonic Mean p-value (HMP) (See p.hmp). All bins with raw p-values below the HMP are identified and the returned values for start, end, centre, width, matches in region, expected and enrichment are across this set of bins. The expectation is that where a positional bias is evident, this will be a narrow range containing a non-trivial proportion of the total matches.
It should also be noted that binom.test()
can return p-values of zero, as
beyond machine precision. In these instances, zero p-values are excluded from
calculation of the HMP. This will give a very slight conservative bias, and
assumes that for these extreme cases, neighbouring bins are highly likely to
also return extremely low p-values and no significance will be lost.
## Load the example PWM
data("ex_pwm")
esr1 <- ex_pwm$ESR1
## Load the example sequences
data("ar_er_seq")
## Get the best match and use this data
matches <- getPwmMatches(esr1, ar_er_seq, best_only = TRUE)
## Test for enrichment in any position
testMotifPos(matches)
#> start end centre width total_matches matches_in_region expected enrichment
#> 1 -165 185 10 350 45 38 27.90698 1.361667
#> prop_total p fdr consensus_motif
#> 1 0.8444444 0.9741004 0.9741004 30, 2, 1....
## Provide a list of PWMs, testing for distance from zero
testMotifPos(ex_pwm, ar_er_seq, abs = TRUE, binwidth = 10)
#> start end centre width total_matches matches_in_region expected
#> ESR1 10 40 25 30 45 15 6.99481865
#> ANDR 0 190 95 190 53 28 13.80208333
#> FOXA1 0 190 95 190 179 110 100.97435897
#> ZN281 60 190 125 130 36 29 18.65284974
#> ZN143 120 130 125 10 1 1 0.05263158
#> enrichment prop_total p fdr consensus_motif
#> ESR1 2.144444 0.3333333 0.2493238 0.7445537 30, 2, 1....
#> ANDR 2.028679 0.5283019 0.2978215 0.7445537 4, 3, 2,....
#> FOXA1 1.089385 0.6145251 0.8064541 0.9520175 6, 0, 0,....
#> ZN281 1.554722 0.8055556 0.8349283 0.9520175 5, 0, 25....
#> ZN143 19.000000 1.0000000 0.9520175 0.9520175 1, 0, 0,....