aggregateMatches {scanMiR} | R Documentation |
Aggregates miRNA binding sites with log_kd values to predict transcript repression. See the vignette for more detail.
aggregateMatches( m, a = 0.007726, b = 0.5735, c = 0.181, p3 = 0.04403, coef_utr = -0.28019, coef_orf = -0.08622, p3.range = c(3L, 8L), keepSiteInfo = TRUE, toInt = FALSE, BP = NULL )
m |
A GRanges or data.frame of matches as returned by 'findSeedMatches'. |
a |
The relative concentration of unbound AGO-miRNA complexes. |
b |
Factor specifying the additional repression by a single bound AGO. |
c |
Penalty for sites that are found within the ORF region. |
p3 |
Factor specifying additional repression due to 3p alignment. |
coef_utr |
Factor specifying additional repression due to UTR length. |
coef_orf |
Factor specifying additional repression due to ORF length. |
p3.range |
Range used for 3p alignment. |
keepSiteInfo |
Logical; whether to return information about site types (default = TRUE). Ignored if 'm' does not contain 'log_kd' values |
toInt |
Logical; whether to convert repression scores to integers (default = FALSE). |
BP |
Pass 'BiocParallel::MulticoreParam(ncores, progressbar=TRUE)' to
enable multithreading. Note that in addition, 'aggregateMatches' uses the
data.table package, which is often set to use multi-threading by
default (which would be multiplied by threads determined by 'BP'). See
|
a data.frame containing aggregated repression values and/or information about the numbers and types of matches
# we create mock RNA sequences and seeds: seqs <- getRandomSeq(n=10) # load sample KdModel data(SampleKdModel) # find matches matches <- findSeedMatches(seqs, SampleKdModel) # aggregate matches aggregateMatches(matches)