metaseqrPlot | R Documentation |
This is the main function for producing sructured quality control and informative graphs base on the results of the various steps of the metaseqR package. The graphs produced span a variety of issues like good sample reproducibility (Multi-Dimensional Scaling plot, biotype detection, heatmaps. diagplotMetaseqr, apart from implementing certain package-specific plots, is a wrapper around several diagnostic plots present in other RNA-Seq analysis packages such as EDASeq and NOISeq.
metaseqrPlot(object, sampleList, annotation = NULL,
contrastList = NULL, pList = NULL,
thresholds = list(p = 0.05, f = 1),
plotType = c("mds", "biodetection", "countsbio",
"saturation", "readnoise", "rnacomp", "correl",
"pairs", "boxplot", "gcbias", "lengthbias",
"meandiff", "meanvar", "deheatmap", "volcano",
"biodist", "filtered", "mastat", "deregulogram",
"statvenn", "foldvenn"),
isNorm = FALSE, output = "x11", path = NULL, ...)
object |
a matrix or a data frame containing count data derived before or after the normalization procedure, filtered or not by the metaseqR2's filters and/or p-value. |
sampleList |
the list containing condition names and the samples under each condition. |
annotation |
a data frame containing annotation elements for each row in object. See also Details. |
contrastList |
the vector of contrasts as defined in
the main help page of |
pList |
a list of p-values for each contrast as
obtained from any of the |
thresholds |
a list with the elements |
plotType |
one or more of the diagnostic plots supported in metaseqR2 package. See also Details. |
isNorm |
a logical indicating whether object contains raw or normalized data. It is not essential and it serves only plot annotation purposes. |
output |
one or more R plotting device to direct the plot result to. See Details. |
path |
the path to create output files. |
... |
further arguments to be passed to plot
devices, such as parameter from |
Regarding object
, the object can be fed to any of
the diagplotMetaseqr
plotting systems but not every
plot is meaningful. For example, it's meaningless to
create a "biodist"
plot for a count matrix before
normalization or statistical testing.
Regarding annotation
, usually, it is a subset of
the annotation obtained by getAnnotation
or
a subset of possibly embedded annotation with the input
counts table. This parameter is optional and required
only when diagplotType is any of "biodetection"
,
"countsbio"
, "saturation"
,
"rnacomp"
, "readnoise"
, "biodist"
,
"gcbias"
, "lengthbias"
or
"filtered"
.
Regarding contrastList
, this parameter is optional
and required only when diagplotType
is any of
"deheatmap"
, "volcano"
or "biodist"
.
It can also be a named structured list of contrasts as
returned by the internal function
metaseqR2:::makeContrastList
.
Regarding diagplotType
, many of these plots
require the presence of additional package, something
that is checked while running the main metaseqr2 function.
The supported plots are "mds"
, "biodetection"
,
"countsbio"
, "saturation"
, "rnacomp"
,
"boxplot"
, "gcbias"
, "lengthbias"
,
"meandiff"
, "meanvar"
, "deheatmap"
,
"volcano"
, "biodist"
, "filtered"
,
"readnoise"
, "venn"
, "correl"
,
"pairwise"
. For a brief description of these plots
please see the main metaseqr2
help page.
Regarding pList
, this parameter is optional
and required only when diagplotType
is any of
"deheatmap"
, "volcano"
or "biodist"
.
Regarding output
, supported mechanisms are:
"png"
, "jpg"
, "bmp"
, "pdf"
,
"ps"
or "json"
. The latter is currently
available for the creation of interactive volcano plots
only when reporting the output, through the highcharts
javascript library. The default plotting ("x11"
)
is not supported due to instability in certain devices.
A named list containing the file names of the produced
plots. Each list member is names according to the
selected plotting device and is also a named list, whose
names are the plot types. The final contents are the file
names in case the plots are written to a physical
location (not meaningful for "x11"
).
In order to make the best out of this function, you should generally provide the annotation argument as most and also the most informative plots depend on this. If you don't know what is inside your counts table or how many annotation elements you can provide by embedding it, it's always best to setup a local databse so as to use predefined annotations that work better with the functions of the whole package.
Panagiotis Moulos
dataMatrix <- metaseqR2:::exampleCountData(2000)
sampleList <- list(A=c("A1","A2"),B=c("B1","B2","B3"))
contrast <- "A_vs_B"
metaseqrPlot(dataMatrix,sampleList,plotType=c("mds","boxplot"))
normArgs <- getDefaults("normalization","deseq2")
object <- normalizeDeseq2(dataMatrix,sampleList,normArgs)
metaseqrPlot(object,sampleList,plotType="boxplot")
## More
#p <- statDeseq2(object,sampleList)
#metaseqrPlot(object,sampleList,contrastList=contrast,pList=p,
# plotType="volcano")
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.