Description Usage Arguments Details Value Note Author(s) References Examples
Uses the "Block"
, "Row"
and "Column"
information of an
EList
or EListRaw
object to resemble the original positions on the
array(s). The resulting plot is similar to the original scan image of the
considered array(s). Thus, this function is a visualization tool that can be
used to visualize protein microarrays for which the original scan image is not
available. Visual inspection of the spatial expression pattern can then identify
possible local tendencies and strong spatial biases. Moreover, the array can be
inspected at all stages of the preprocessing workflow in order to check the
impact of the particular methods that have been applied.
1 2 3 |
elist |
|
idx |
integer, vector of integers or the string |
data.type |
string indicating whether the foreground ( |
log |
logical indicating whether the input data is logarithmized. If TRUE the log2 scale is expected. If FALSE a log2-transformation will be performed (mandatory). |
normalized |
logical indicating whether |
aggregation |
string indicating whether the data stored in
|
colpal |
string indicating the color palette for the plot(s). The default
is |
graphics.device |
string indicating the file format for the plot(s) saved
in |
output.path |
string indicating the output path for the plots (optional). |
This function allows plotting of protein microarray data using the gplots
function heatmap.2()
for visual quality control. The data obtained
from an EList
or EListRaw
object is re-ordered and represented
in the same way the spots are ordered on the actual microarray. Consequently,
the resulting plot is similar to the original scan image of the considered
array. This allows for visual control and assessment of possible patterns in
spatial distribution.
Mandatory arguments are elist
, idx
, log
, normalized
and aggregation
. While elist
specifies the EList
or EListRaw
object to be used, idx
designates the array
column index in elist
to plot a single array from the EList
object. Alternatively, a vector (e.g., 1:5
) or the string "all"
can be designated to include multiple, respectively, all arrays that were
imported.
Furthermore, data.type
allows for plotting of "fg"
, foreground
data (i.e., elist$E
and elist$C
), which is the default or
"bg"
, background data (i.e., elist$Eb
and elist$Cb
).
The normalization approaches of PAA which comprise also data logarithmization
do not include control data. With normalized=TRUE
it is indicated that
the input data was normalized, so the control data will be logarithmized (log2)
before plotting as well. However, since the complete data (foreground and
background values of protein features and control spots) can be logarithmized
regardless of normalization the argument log
states whether the
designated data is already logarithmized (note: log2 scale is always expected).
The parameter aggregation
indicates whether the protein microarray
data has been aggregated by loadGPR()
and, if so, which method has been
used.
Moreover, the parameter colpal
defines the color palette that will
be used for the plot. Some exemplary values are "heat.colors"
(default),
"terrain.colors"
, "topo.colors"
, "greenred"
and
"bluered"
.
Finally, the output path optionally can be specified with the argument
output.path
to save the plot(s). Then, one or more tiff or png file(s)
containing the corresponding plot(s) are saved into the subfolder "array_plots".
No value is returned.
Please note the instructions of the PAA function loadGPR()
. Note that the
data has to be imported including controls to avoid annoying gaps in the plot
(for ProtoArrays this is done automatically and for other types of arrays the
arguments description
, description.features
and
description.discard
must be defined). Note that the data can be
imported without aggregation by loadGPR()
(when
aggregation="none"
) in order to inspect the array visually with
plotArray()
before duplicate aggregation.
Daniel Bemmerl and Michael Turewicz michael.turewicz@rub.de
The package gplots
by Gregory R. Warnes et al. can be downloaded from
CRAN (http://CRAN.R-project.org/package=gplots).
Gregory R. Warnes, Ben Bolker, Lodewijk Bonebakker, Robert Gentleman, Wolfgang Huber, Andy Liaw, Thomas Lumley, Martin Maechler, Arni Magnusson, Steffen Moeller, Marc Schwartz and Bill Venables (2015). gplots: Various R Programming Tools for Plotting Data. R package version 2.17.0. http://CRAN.R-project.org/package=gplots
1 2 3 4 |
Loading required package: Rcpp
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.