Apart from merging data and performing calculations on it, the DoOR.functions
package provides several ways to visualize the data served by the DoOR.data
package. Please see the DoOR.function vignette for
instructions on how to install and load both functions and data. The plotting
functions can be identified by their common prefix dplot_
, most of them make
use of the ggplot2
package which allows to override most, if not all of the
theming options.
dplot_response_matrix()
](dplot_al_map()
dplot_tuningCurve()
dplot_response_profile()
dplot_compare_profiles()
dplot_across_ru()
dplot_across_osns()
First we need to load packages and data:
#load data library(DoOR.functions) library(DoOR.data) load_door_data(nointeraction = TRUE)
dplot_response_matrix()
{#responseMatrix}
dplot_response_matrix()
visualizes the DoOR consensus response matrix either
as a point matrix with the size of the points relating to the response strength,
or as a heatmap, relating color to response strength. The default plot that is
used depends on the data we enter: positive values only (like from
door_response_matrix
or door_response_matrix_non_normalized
) which are
scaled [0,1]
will by default be plotted as a point-matrix. If we provide data
that contains negative values (e.g. data with the spontaneous firing rate set to
0 via reset_sfr(door_response_matrix, "SFR")
) it will be shown as color-coded
heatmap.
dplot_response_matrix(door_response_matrix[2:50,], tag = "Name", base_size = 8)
If the data contains negative values, a colored response matrix will be plotted:
dplot_response_matrix(reset_sfr(door_response_matrix, "SFR")[2:50,], tag = "Name", base_size = 8)
dplot_al_map()
{#ALmap}With dplot_al_map()
we can visualize the ensemble response a given odorant
elicits across DoOR responding units (receptors, sensory neurons, glomeruli, ...
) as a hypothetical antennal lobe activation pattern.
dplot_al_map("QSJXEFYPDANLFS-UHFFFAOYSA-N", base_size = 8)
If we do not know the InChIKey of the substance we are interested in, we can use
the trans_id()
function for conversion. If we are interested in the expressed
receptor rather than the glomerulus names:
dplot_al_map(trans_id("benzaldehyde", from = "Name"), tag = "receptor", main = "SMILES", base_size = 8)
If we prefer the plain activation pattern without annotations at all:
dplot_al_map(trans_id("628-63-7", from = "CAS"), tag = "", main = "", legend = FALSE, scalebar = FALSE)
dplot_tuningCurve()
{#tuningCurve}The set of odorants a given responding unit is responsive to can be described as
its tuning curve. With dplot_tuningCurve()
we can easily display such a tuning
curve together with its kurtosis. Kurtosis is a measure of the shape of the
tuning curve, i.e. whether a responding unit is narrowly tuned to a few odorants
(high kurtosis), or whether it responds to many odorants (low kurtosis). The
gaussian distribution has a kurtosis of 0.
If we only specify the receptor/response unit name, the data is taken from
door_response_matrix
and dplot_tuningCurve()
uses reset_sfr()
to reset
spontaneous firing rate (or any other specified odorant) to zero before
plotting.
dplot_tuningCurve(receptor = "Or22a", base_size = 8)
To prevent resetting by SFR:
dplot_tuningCurve(receptor = "Or22a", zero = "", base_size = 8)
We can also plot any other vector of responses, once response.vector
is
specified, the value of receptor
is only used for the plot title and not for
data lookup anymore:
dplot_tuningCurve(receptor = "receptor X", response.vector = c(1:100), base_size = 8)
dplot_tuningCurve()
can as well be used to visualize the ensemble of
responding units that is activated by a given odorant. Therefore, we specify an
odorant name instead of a response unit name:
dplot_tuningCurve(odorant = "PGMYKACGEOXYJE-UHFFFAOYSA-N", base_size = 8)
We can specify the chemical identifier to plot via odor.main
:
dplot_tuningCurve(odorant = "PGMYKACGEOXYJE-UHFFFAOYSA-N", odor.main = "SMILES", base_size = 8) dplot_tuningCurve(odorant = "CURLTUGMZLYLDI-UHFFFAOYSA-N", odor.main = "InChI", base_size = 8)
And finally we can control the color of the bars:
dplot_tuningCurve(odorant = trans_id("carbon dioxide", from = "Name"), fill.odorant = "#FF0000", base_size = 8)
As mentioned, all of these plots are generated with the ggplot2 package which allows to override theming:
library(ggplot2) dplot_tuningCurve(odorant = trans_id("carbon dioxide", from = "Name"), base_size = 8) + theme(panel.background = element_rect(fill = "grey", color = "magenta"))
dplot_response_profile()
{#responseProile}dplot_response_profile
creates a horizontal bar plot of the response profile
of a given receptor. It displays the same data as dplot_tuningCurve
but
focusses on the odorant identity.
Per default the response strength is displayed as bar height as well as color code:
dplot_response_profile("Gr21a.Gr63a", tag = "Name", base_size = 8)
We can again omit to reset to SFR:
dplot_response_profile("Gr21a.Gr63a", tag = "Name", base_size = 8, zero ="")
And if we prefer monochrome data:
dplot_response_profile("Gr21a.Gr63a", tag = "CAS", base_size = 8, colored = FALSE)
dplot_compare_profiles()
{#compareProfiles}As the name indicates, with dplot_compare_profiles()
we can plot two response
profiles side by side for comparison. The syntax here differs from the previous
plots as we can use it also to compare the original data sets in DoOR.
This time, with x
and y
we have to specify a whole data.frame, by.x
and
by.y
take the corresponding column names that will be plotted. If x
is not
specified, both by.x
and by.y
will be taken from x
.
dplot_compare_profiles(x = Or22a, y = Or22a, by.x = "Pelz.2006.AntEC50", by.y = "Hallem.2004.EN", tag = "Name", base_size = 8)
We see that the measured Or22a sensory neuron responses in these two data sets are in good accordance.
Next we compare two DoOR consensus response profiles, measurements of the misexpressed receptor Or35a and recordings from the ac3B sensory neuron that naturally expresses Or35a:
dplot_compare_profiles( x = door_response_matrix, by.x = "Or35a", by.y = "ac3B", tag = "Name", base_size = 8 )
or with "SFR" set to 0:
dplot_compare_profiles( x = reset_sfr(door_response_matrix, "SFR"), by.x = "Or35a", by.y = "ac3B", tag = "Name", base_size = 8 )
We see that the response profiles are very similar but not identical, which is expected as the ac3B neuron expresses other receptors in addition to Or35a.
dplot_across_ru()
{#acrossReceptors}
With dplot_across_ru()
we can visualize the responses that one or several
odorants elicit across receptors / responding units.
odors <- trans_id(c("pentyl acetate", "carbon dioxide", "2,3-butanedione"), from = "Name") dplot_across_ru(odors, tag = "Name", base_size = 8)
dplot_across_osns()
{#across_OSNs}
dplot_across_osns()
is similar to dplot_across_ru()
but the responding units
are sorted according to the sensory neuron they belong to. This sorting is
controlled via door_mappings
from the DoOR.data
package. There are two types
of plot that dplot_across_osns()
can return. Type 2 directly resembles
dplot_across_ru()
:
dplot_across_osns(odors, base_size = 8, plot.type = 2)
In type 1 the data is split according to odorant X sensillum, the color is assigned to the corresponding neuron A-D or X-Z if the neuron's identity is unknown:
dplot_across_osns(odors, base_size = 8, plot.type = 1)
As this plot gets pretty messy, we have the option to restrict plotting to certain subsets of sensilla. We can for example only plot the responses of antennal basiconic (ab) sensilla:
dplot_across_osns(odors, base_size = 8, plot.type = 1, sub = "ab")
Or we plot coeloconic and trichoid sensilla:
dplot_across_osns(odors, base_size = 8, plot.type = 1, sub = c("ac", "at"))
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