plotCoverage | R Documentation |
Generate plotly / ggplot RNA-seq genome and coverage plots from command line. Note that these are legacy functions. More expansive functionality is available using getCoverageData / getPlotObject / plotView functions.
plotCoverage(
se,
Event,
Gene,
seqname,
start,
end,
coordinates,
strand = c("*", "+", "-"),
zoom_factor = 0.2,
bases_flanking = 100,
tracks,
track_names = tracks,
condition,
ribbon_mode = c("sd", "ci", "sem", "none"),
selected_transcripts = "",
reverseGenomeCoords = FALSE,
plotJunctions = FALSE,
junctionThreshold = 0.01,
plot_key_isoforms = FALSE,
condense_tracks = FALSE,
stack_tracks = FALSE,
t_test = FALSE,
norm_event,
usePlotly = FALSE
)
plotGenome(
se,
reference_path,
Event,
Gene,
seqname,
start,
end,
coordinates,
zoom_factor = 0.2,
bases_flanking = 100,
reverseGenomeCoords = FALSE,
condense_tracks = FALSE,
selected_transcripts = "",
plot_key_isoforms = FALSE,
usePlotly = FALSE
)
se |
A NxtSE object, created by makeSE.
COV files must be linked to the NxtSE object. To do this, see the example
in makeSE. Required by |
Event |
The |
Gene |
Whether to use the range for the given |
seqname , start , end |
The chromosome (string) and genomic |
coordinates |
A string specifying genomic coordinates can be given
instead of |
strand |
Whether to show coverage of both strands "*" (default), or from the "+" or "-" strand only. |
zoom_factor |
Zoom out from event. Each level of zoom zooms out by a
factor of 3. E.g. for a query region of chr1:10000-11000, if a
|
bases_flanking |
(Default = |
tracks |
The names of individual samples,
or the names of the different conditions to be plotted. For the latter, set
|
track_names |
The names of the tracks to be displayed. If omitted, the
track_names will default to the input in |
condition |
To display normalised coverage per condition, set this to
the condition category. If omitted, |
ribbon_mode |
(default |
selected_transcripts |
(Optional) A vector containing transcript ID or transcript names of transcripts to be displayed on the gene annotation track. Useful to remove minor isoforms that are not relevant to the samples being displayed. |
reverseGenomeCoords |
(default |
plotJunctions |
(default |
junctionThreshold |
(default |
plot_key_isoforms |
(default |
condense_tracks |
(default |
stack_tracks |
(default |
t_test |
(default |
norm_event |
Whether to normalise by an event different to that given
in "Event". The difference between this and Event is that the genomic
coordinates can be centered around a different |
usePlotly |
If |
reference_path |
The path of the reference generated by
Build-Reference-methods. Required by |
In RNA sequencing, alignments to spliced transcripts will "skip" over genomic regions of introns. This can be illustrated in a plot using a horizontal genomic axis, with the vertical axis representing the number of alignments covering each nucleotide. As a result, the coverage "hills" represent the expression of exons, and "valleys" to introns.
Different alternatively-spliced isoforms thus produce different coverage patterns. The change in the coverage across an alternate exon relative to its constitutively-included flanking exons, for example, represents its alternative inclusion or skipping. Similarly, elevation of intron valleys represent increased intron retention.
With multiple replicates per sample, coverage is dependent on library size and gene expression. To compare alternative splicing ratios, normalisation of the coverage of the alternate exon (or alternatively retained intron) relative to their constitutive flanking exons, is required. There is no established method for this normalisation, and can be confounded in situations where flanking elements are themselves alternatively spliced.
SpliceWiz performs this coverage normalisation using the same method as its
estimate of spliced / intronic transcript abundance using the SpliceOver
method (see details section in collateData). This normalisation can be
applied to correct for library size and gene expression differences between
samples of the same experimental condition. After normalisation, mean and
variance of coverage can be computed as ratios relative to total transcript
abundance. This method can visualise alternatively included genomic regions
including casette exons, alternate splice site usage, and intron retention.
plotCoverage
generates plots showing depth of alignments to
the genomic axis. Plots can be generated for individual samples or samples
grouped by experimental conditions. In the latter, mean and 95% confidence
intervals are shown.
plotGenome
generates genome transcript tracks only. Protein-coding
regions are denoted by thick rectangles, whereas non-protein coding
transcripts or untranslated regions are denoted with thin rectangles.
Introns are denoted as lines.
For plotCoverage
and plotGenome
:
If usePlotly = FALSE
returns a patchwork-assembled static plot
If usePlotly = TRUE
returns a covPlotly object,
which generates a plotly interactive plot when shown using show()
plotCoverage()
: Legacy function - works by internally calling
getCoverageData(), getPlotObject(), then plotView()
plotGenome()
: Legacy function - works by internally calling
getGenomeData(), followed by plotAnnoTrack()
se <- SpliceWiz_example_NxtSE(novelSplicing = TRUE)
# Assign annotation of the experimental conditions
colData(se)$treatment <- rep(c("A", "B"), each = 3)
# Verify that the COV files are linked to the NxtSE object:
covfile(se)
# Plot the genome track only, with specified gene:
plotGenome(se, Gene = "SRSF3")
# View the genome track, specifying a genomic region via coordinates:
plotGenome(se, coordinates = "chrZ:10000-20000")
# Return a list of ggplot and plotly objects, also plotting junction counts
plotCoverage(
se = se,
Event = "SE:SRSF3-203-exon4;SRSF3-202-int3",
tracks = colnames(se)[1:4], plotJunctions = TRUE
)
# Plot the same, but as a plotly interactive plot
if(interactive()) {
p <- plotCoverage(
se = se,
Event = "SE:SRSF3-203-exon4;SRSF3-202-int3",
tracks = colnames(se)[1:4], plotJunctions = TRUE,
usePlotly = TRUE
)
show(p)
}
# Plot by condition "treatment", including provisional PSIs
plotCoverage(
se = se,
Event = "SE:SRSF3-203-exon4;SRSF3-202-int3",
tracks = c("A", "B"), condition = "treatment", plotJunctions = TRUE
)
# As above, but stack all traces into the same track
# - NB: plotJunctions is disabled when `stack_tracks = TRUE`
plotCoverage(
se = se,
Event = "SE:SRSF3-203-exon4;SRSF3-202-int3",
tracks = c("A", "B"), condition = "treatment", stack_tracks = TRUE
)
# Plot the above, but unstancked, and with t-test track
# - NB: plotJunctions is disabled when `stack_tracks = TRUE`
plotCoverage(
se = se,
Event = "SE:SRSF3-203-exon4;SRSF3-202-int3",
tracks = c("A", "B"), condition = "treatment", t_test = TRUE
)
# Select only transcripts involved in the selected alternative splicing event
plotCoverage(
se = se,
Event = "SE:SRSF3-203-exon4;SRSF3-202-int3",
tracks = colnames(se)[1:4],
plot_key_isoforms = TRUE
)
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