| plotMotifs {circRNAprofiler} | R Documentation |
The function plotMotifs() generates 2 bar charts showing the log2FC and the number of occurences of each motif found in the target sequences (e.g detected Vs randomly selected).
plotMotifs(mergedMotifsFTS, mergedMotifsBTS, log2FC = 1, nf1 = 1, nf2 = 1, df1Name = "foreground", df2Name = "background")
mergedMotifsFTS |
A data frame containing the number of occurences
of each motif found in foreground target sequences (e.g from detected
back-spliced junctions). It can be generated with the
|
mergedMotifsBTS |
A data frame containing the number of occurences
of each motif found in the background target sequences (e.g. from
random back-spliced junctions). It can be generated with the
|
log2FC |
An integer specifying the log2FC cut-off. Default value is 1. |
nf1 |
An integer specifying the normalization factor for the first data frame mergedMotifsFTS. The occurrences of each motif are divided by nf1. The normalized values are then used for fold-change calculation. Set this to the number of target sequences (e.g from detected back-spliced junctions) where the motifs were extracted from. Default value is 1. |
nf2 |
An integer specifying the normalization factor for the second data frame mergedMotifsBTS. The occurrences of each motif are divided by nf2. The normalized values are then used for fold-change calculation. Set this to the number of target sequences (e.g from random back-spliced junctions) where the motifs were extracted from. Default value is 1. NOTE: By setting nf1 and nf2 equals to 1 the number of target sequences (e.g detected Vs randomly selected) where the motifs were extrated from, is supposed to be the same. |
df1Name |
A string specifying the name of the first data frame. This will be displayed in the legend of the plot. Deafult value is "foreground". |
df2Name |
A string specifying the name of the first data frame. This will be displayed in the legend of the plot. Deafult value is "background". |
A ggplot object.
# Load data frame containing detected back-spliced junctions
data("mergedBSJunctions")
# Load short version of the gencode v19 annotation file
data("gtf")
# Annotate the first back-spliced junctions
annotatedFBSJs <- annotateBSJs(mergedBSJunctions[1, ], gtf)
# Get random back-spliced junctions
randomBSJunctions <- getRandomBSJunctions(gtf, n = 1, f = 10)
# Annotate random back-spliced junctions
annotatedBBSJs <- annotateBSJs(randomBSJunctions, gtf, isRandom = TRUE)
# Get genome
genome <- BSgenome::getBSgenome("BSgenome.Hsapiens.UCSC.hg19")
# Retrieve target sequences from detected back-spliced junctions
targetsFTS <- getSeqsFromGRs(
annotatedFBSJs,
genome,
lIntron = 200,
lExon = 10,
type = "ie"
)
# Retrieve target sequences from random back-spliced junctions
targetsBTS <- getSeqsFromGRs(
annotatedBBSJs,
genome,
lIntron = 200,
lExon = 10,
type = "ie"
)
# Get motifs
motifsFTS <- getMotifs(
targetsFTS,
width = 6,
species = "Hsapiens",
rbp = TRUE,
reverse = FALSE)
motifsBTS <- getMotifs(
targetsBTS,
width = 6,
species = "Hsapiens",
rbp = TRUE,
reverse = FALSE)
# Merge motifs
mergedMotifsFTS <- mergeMotifs(motifsFTS)
mergedMotifsBTS <- mergeMotifs(motifsBTS)
# Plot
p <- plotMotifs(
mergedMotifsFTS,
mergedMotifsBTS,
log2FC = 2,
nf1 = nrow(annotatedFBSJs),
nf2 = nrow(annotatedBBSJs),
df1Name = "foreground",
df2Name = "background")