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")