Description Usage Arguments Value Examples
Threshold the possible binding sites based on score, or False Discovery Rate (FDR). To threshold on FDR, you must have computed an FDR/Score map using calc.fdr, and chosen an FDR threshold, for which makeFdrPlot() is helpful.
1 2 | output.sites(seqsScores, scoreThreshold = NULL, fdrScoreMap = NULL,
fdrThreshold = NULL)
|
seqsScores |
score.ms output representing scores for candidate binding sites |
scoreThreshold |
A numeric value giving the lower score boundary significance threshold. Sequences with scores higher than this boundary will be selected. (Not required if thresholding by FDR.) |
fdrScoreMap |
calc.fdr output giving mapping between score/FDR (only required if thresholding by FDR). |
fdrThreshold |
A numeric value between 0 and 1 giving upper FDR boundary- any site with a lower FDR score will be output. (only required if thresholding by FDR) |
Features object containing thresholded Transcription Factor Binding Sites, their locations, scores, strand, etc. If thresholding by score, this is equivalent to
seqsScores[seqsScores$score > scoreThreshold,]
.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 | require("rtfbs")
exampleArchive <- system.file("extdata", "NRSF.zip", package="rtfbs")
seqFile <- "input.fas"
unzip(exampleArchive, seqFile)
# Read in FASTA file "input.fas" from the examples into an
# MS (multiple sequences) object
ms <- read.ms(seqFile);
pwmFile <- "pwm.meme"
unzip(exampleArchive, pwmFile)
# Read in Position Weight Matrix (PWM) from MEME file from
# the examples into a Matrix object
pwm <- read.pwm(pwmFile)
# Build a 3rd order Markov Model to represent the sequences
# in the MS object "ms". The Model will be a list of
# matrices corrisponding in size to the order of the
# Markov Model
mm <- build.mm(ms, 3);
# Match the PWM against the sequences provided to find
# possible transcription factor binding sites. A
# Features object is returned, containing the location
# of each possible binding site and an associated score.
# Sites with a negative score are not returned unless
# we set threshold=-Inf as a parameter.
cs <- score.ms(ms, pwm, mm)
# Generate a sequence 1000 bases long using the supplied
# Markov Model and random numbers
v <- simulate.ms(mm, 10000)
# Match the PWM against the sequences provided to find
# possible transcription factor binding sites. A
# Features object is returned, containing the location
# of each possible binding site and an associated score.
# Sites with a negative score are not returned unless
# we set threshold=-Inf as a parameter. Any identified
# binding sites from simulated data are false positives
# and used to calculate False Discovery Rate
xs <- score.ms(v, pwm, mm)
# Calculate the False Discovery Rate for each possible
# binding site in the Features object CS. Return
# a mapping between each binding site score and the
# associated FDR.
fdr <- calc.fdr(ms, cs, v, xs)
# Output identified transcription factor binding sites
# below a 0.5 FDR threshold
output.sites(cs, NULL, fdr, 0.5)
# OR
# Output identified transcription factor binding sites
# above a 5.2 score threshold
output.sites(cs, 5.2)
unlink("pwm.meme")
unlink("input.fas")
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