curvesplot: Plot of fitted curves
In DRomics: Dose Response for Omics
curvesplot R Documentation
Plot of fitted curves
Description
Provides a plot of all the fitted curves from a dataframe of the main workflow
results, possibly extended with
additional information (e.g. groups from functional annotation) used to color and/or split the curves.
Usage
curvesplot(extendedres, xmin, xmax, y0shift = TRUE, scaling = TRUE,
facetby, facetby2, free.y.scales = FALSE, ncol4faceting,
colorby, removelegend = FALSE,
npoints = 500, line.size = 0.5,
line.alpha = 0.8, dose_log_transfo = TRUE,
addBMD = TRUE, BMDtype = c("zSD", "xfold"),
point.size = 1, point.alpha = 0.8)
Arguments
extendedres
the dataframe of results provided by bmdcalc (res)
or a subset of this data frame (selected lines). This dataframe can be extended
with additional columns coming for example from the annotation of items, and some lines
can be replicated if their corresponding item has more than one annotation.
This extended dataframe
must at least contain the column giving the identification of each curve (id),
the column model naming the fitted model and the values of
the parameters (columns b, c, d, e, f) and
a column coding for the chosen BMD, by default BMD.zSD or BMD.xfold
BMDtype is to "xfold".
xmin
If not defined, a value just below the maximum BMD values is fixed if the x dose scale is in log, or 0 otherwise.
xmax
Maximal dose/concentration for definition of the x range (can be defined
as max(f$omicdata$dose) with f the output of drcfit()). If not defined, a value just above the maximum BMD values is taken.
y0shift
If TRUE (default choice) curves are all shifted to have the theoretical
signal at the control at 0.
scaling
If TRUE, default choice, curves are all shifted to have the theoretical
signal at the control at 0 y0 and scaled by dividing by
the maximal absolute signal change (up or down) from the signal at the control
maxychange.
facetby
optional argument naming the column of extendedres chosen
to split the plot in facets (no split if omitted).
facetby2
optional argument naming the column of extendedres chosen
as an additional argument to split the plot in facets using
ggplot2::facet_grid, with columns defined by facetby
and rows defined by facetby2 (no split if omitted).
free.y.scales
if TRUE the y scales are free in the different facets.
ncol4faceting
Number of columns for facetting (not used if facetby2
is also provided.
colorby
optional argument naming the column of extendedres chosen
to color the curves (no color if omitted).
removelegend
If TRUE the color legend is removed (useful if the number
of colors is great).
npoints
Number of points computed on each curve to plot it.
line.size
Width of the lines for plotting curves.
line.alpha
Transparency of the lines for plotting curves.
dose_log_transfo
If TRUE a log transformation of the dose is used in the plot.
This option needs a definition of a strictly positive value of xmin in input.
addBMD
If TRUE points are added on the curve at BMD-BMR values (requires to have
BMD and BMD values in the first argument extendedres).
BMDtype
The type of BMD to add, "zSD" (default choice) or "xfold".
point.size
Size of the BMD-BMR points added on the curves.
point.alpha
Transparency of the BMD-BMR points added on the curves.
Details
For each item of the extended dataframe, the name of the model
(column model) and the values of
the parameters (columns b, c, d, e, f)
are used to compute theoretical dose-response curves in the range
[xmin ; xmax].
Value
a ggplot object.
Author(s)
Marie-Laure Delignette-Muller
See Also
See plot.bmdboot.
Examples
# (1) A toy example on a very small subsample of a microarray data set)
#
datafilename <- system.file("extdata", "transcripto_very_small_sample.txt",
package = "DRomics")
o <- microarraydata(datafilename, check = TRUE, norm.method = "cyclicloess")
s_quad <- itemselect(o, select.method = "quadratic", FDR = 0.01)
f <- drcfit(s_quad, progressbar = TRUE)
r <- bmdcalc(f)
# (1.a)
# Default plot of all the curves with BMD values added as points on the curve
#
curvesplot(r$res, xmax = max(f$omicdata$dose))
# use of line size, point size, transparency
curvesplot(r$res, xmax = max(f$omicdata$dose),
line.alpha = 0.2, line.size = 1, point.alpha = 0.3, point.size = 1.8)
# the same plot with dose not in log scale
# fixing xmin and xmax
curvesplot(r$res, xmin = 0.1, xmax = max(f$omicdata$dose),
dose_log_transfo = FALSE, addBMD = TRUE)
# or not
curvesplot(r$res, dose_log_transfo = FALSE, addBMD = TRUE)
# plot of curves colored by models
curvesplot(r$res, xmax = max(f$omicdata$dose), colorby = "model")
# plot of curves facetted by item
curvesplot(r$res, xmax = max(f$omicdata$dose), facetby = "id")
# plot of curves facetted by trends
curvesplot(r$res, xmax = max(f$omicdata$dose), facetby = "trend")
# the same plot with free y scales
curvesplot(r$res, xmax = max(f$omicdata$dose), facetby = "trend",
free.y.scales = TRUE)
# (1.b)
# Plot of all the curves without shifting y0 values to 0
# and without scaling
curvesplot(r$res, xmax = max(f$omicdata$dose),
scaling = FALSE, y0shift = FALSE)
# (1.c)
# Plot of all the curves colored by model, with one facet per trend
#
curvesplot(r$res, xmax = max(f$omicdata$dose),
facetby = "trend", colorby = "model")
# changing the number of columns
curvesplot(r$res, xmax = max(f$omicdata$dose),
facetby = "trend", colorby = "model", ncol4faceting = 4)
# playing with size and transparency of lines
curvesplot(r$res, xmax = max(f$omicdata$dose),
facetby = "trend", colorby = "model",
line.size = 0.5, line.alpha = 0.8)
curvesplot(r$res, xmax = max(f$omicdata$dose),
facetby = "trend", colorby = "model",
line.size = 0.8, line.alpha = 0.2)
curvesplot(r$res, xmax = max(f$omicdata$dose),
facetby = "trend", line.size = 1, line.alpha = 0.2)
# (2) an example on a microarray data set (a subsample of a greater data set)
#
datafilename <- system.file("extdata", "transcripto_sample.txt", package="DRomics")
(o <- microarraydata(datafilename, check = TRUE, norm.method = "cyclicloess"))
(s_quad <- itemselect(o, select.method = "quadratic", FDR = 0.001))
(f <- drcfit(s_quad, progressbar = TRUE))
(r <- bmdcalc(f))
# plot split by trend and model with BMR-BMD points added on curves
# adding transparency
curvesplot(r$res, xmax = max(f$omicdata$dose),
line.alpha = 0.2, line.size = 0.8,
addBMD = TRUE, point.alpha = 0.2, point.size = 1.5,
facetby = "trend", facetby2 = "model")
# same plot without scaling and not in log dose scale
curvesplot(r$res, xmax = max(f$omicdata$dose),
line.alpha = 0.2, line.size = 0.8, dose_log_transfo = FALSE,
addBMD = TRUE, point.alpha = 0.2, point.size = 1.5,
scaling = FALSE, facetby = "trend", facetby2 = "model")
# (3) An example from data published by Larras et al. 2020
# in Journal of Hazardous Materials
# https://doi.org/10.1016/j.jhazmat.2020.122727
# a dataframe with metabolomic results (output $res of bmdcalc() or bmdboot() functions)
resfilename <- system.file("extdata", "triclosanSVmetabres.txt", package="DRomics")
res <- read.table(resfilename, header = TRUE, stringsAsFactors = TRUE)
str(res)
# a dataframe with annotation of each item identified in the previous file
# each item may have more than one annotation (-> more than one line)
annotfilename <- system.file("extdata", "triclosanSVmetabannot.txt", package="DRomics")
annot <- read.table(annotfilename, header = TRUE, stringsAsFactors = TRUE)
str(annot)
# Merging of both previous dataframes
# in order to obtain an extenderes dataframe
# bootstrap results and annotation
extendedres <- merge(x = res, y = annot, by.x = "id", by.y = "metab.code")
head(extendedres)
# Plot of the dose-response curves by pathway colored by trend
# with BMR-BMD points added on curves
curvesplot(extendedres, facetby = "path_class", npoints = 100, line.size = 0.5,
colorby = "trend", xmax = 7, addBMD = TRUE)
# The same plot not in log scale
curvesplot(extendedres, facetby = "path_class", npoints = 100, line.size = 0.5,
dose_log_transfo = FALSE, colorby = "trend", xmin = 0, xmax = 7)
# The same plot in log scale without scaling
curvesplot(extendedres, facetby = "path_class", npoints = 100, line.size = 0.5,
colorby = "trend", scaling = FALSE, xmax = 7)
# Plot of the dose-response curves split by pathway and by trend
# for a selection pathway
chosen_path_class <- c("Membrane transport", "Lipid metabolism")
ischosen <- is.element(extendedres$path_class, chosen_path_class)
curvesplot(extendedres[ischosen, ],
facetby = "trend", facetby2 = "path_class",
npoints = 100, line.size = 0.5, xmax = 7)
# Plot of the dose-response curves for a specific pathway
# in this example the "lipid metabolism" pathclass
LMres <- extendedres[extendedres$path_class == "Lipid metabolism", ]
curvesplot(LMres, facetby = "id", npoints = 100, line.size = 0.8, point.size = 2,
colorby = "trend", xmax = 7)
DRomics documentation built on Oct. 16, 2024, 5:09 p.m.
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| curvesplot | R Documentation |
Plot of fitted curves
Description
Provides a plot of all the fitted curves from a dataframe of the main workflow results, possibly extended with additional information (e.g. groups from functional annotation) used to color and/or split the curves.
Usage
curvesplot(extendedres, xmin, xmax, y0shift = TRUE, scaling = TRUE,
facetby, facetby2, free.y.scales = FALSE, ncol4faceting,
colorby, removelegend = FALSE,
npoints = 500, line.size = 0.5,
line.alpha = 0.8, dose_log_transfo = TRUE,
addBMD = TRUE, BMDtype = c("zSD", "xfold"),
point.size = 1, point.alpha = 0.8)Arguments
extendedres |
the dataframe of results provided by bmdcalc (res)
or a subset of this data frame (selected lines). This dataframe can be extended
with additional columns coming for example from the annotation of items, and some lines
can be replicated if their corresponding item has more than one annotation.
This extended dataframe
must at least contain the column giving the identification of each curve ( |
xmin |
If not defined, a value just below the maximum BMD values is fixed if the x dose scale is in log, or 0 otherwise. |
xmax |
Maximal dose/concentration for definition of the x range (can be defined
as |
y0shift |
If |
scaling |
If |
facetby |
optional argument naming the column of |
facetby2 |
optional argument naming the column of |
free.y.scales |
if TRUE the y scales are free in the different facets. |
ncol4faceting |
Number of columns for facetting (not used if |
colorby |
optional argument naming the column of |
removelegend |
If |
npoints |
Number of points computed on each curve to plot it. |
line.size |
Width of the lines for plotting curves. |
line.alpha |
Transparency of the lines for plotting curves. |
dose_log_transfo |
If TRUE a log transformation of the dose is used in the plot. This option needs a definition of a strictly positive value of xmin in input. |
addBMD |
If TRUE points are added on the curve at BMD-BMR values (requires to have BMD and BMD values in the first argument extendedres). |
BMDtype |
The type of BMD to add, |
point.size |
Size of the BMD-BMR points added on the curves. |
point.alpha |
Transparency of the BMD-BMR points added on the curves. |
Details
For each item of the extended dataframe, the name of the model
(column model) and the values of
the parameters (columns b, c, d, e, f)
are used to compute theoretical dose-response curves in the range
[xmin ; xmax].
Value
a ggplot object.
Author(s)
Marie-Laure Delignette-Muller
See Also
See plot.bmdboot.
Examples
# (1) A toy example on a very small subsample of a microarray data set)
#
datafilename <- system.file("extdata", "transcripto_very_small_sample.txt",
package = "DRomics")
o <- microarraydata(datafilename, check = TRUE, norm.method = "cyclicloess")
s_quad <- itemselect(o, select.method = "quadratic", FDR = 0.01)
f <- drcfit(s_quad, progressbar = TRUE)
r <- bmdcalc(f)
# (1.a)
# Default plot of all the curves with BMD values added as points on the curve
#
curvesplot(r$res, xmax = max(f$omicdata$dose))
# use of line size, point size, transparency
curvesplot(r$res, xmax = max(f$omicdata$dose),
line.alpha = 0.2, line.size = 1, point.alpha = 0.3, point.size = 1.8)
# the same plot with dose not in log scale
# fixing xmin and xmax
curvesplot(r$res, xmin = 0.1, xmax = max(f$omicdata$dose),
dose_log_transfo = FALSE, addBMD = TRUE)
# or not
curvesplot(r$res, dose_log_transfo = FALSE, addBMD = TRUE)
# plot of curves colored by models
curvesplot(r$res, xmax = max(f$omicdata$dose), colorby = "model")
# plot of curves facetted by item
curvesplot(r$res, xmax = max(f$omicdata$dose), facetby = "id")
# plot of curves facetted by trends
curvesplot(r$res, xmax = max(f$omicdata$dose), facetby = "trend")
# the same plot with free y scales
curvesplot(r$res, xmax = max(f$omicdata$dose), facetby = "trend",
free.y.scales = TRUE)
# (1.b)
# Plot of all the curves without shifting y0 values to 0
# and without scaling
curvesplot(r$res, xmax = max(f$omicdata$dose),
scaling = FALSE, y0shift = FALSE)
# (1.c)
# Plot of all the curves colored by model, with one facet per trend
#
curvesplot(r$res, xmax = max(f$omicdata$dose),
facetby = "trend", colorby = "model")
# changing the number of columns
curvesplot(r$res, xmax = max(f$omicdata$dose),
facetby = "trend", colorby = "model", ncol4faceting = 4)
# playing with size and transparency of lines
curvesplot(r$res, xmax = max(f$omicdata$dose),
facetby = "trend", colorby = "model",
line.size = 0.5, line.alpha = 0.8)
curvesplot(r$res, xmax = max(f$omicdata$dose),
facetby = "trend", colorby = "model",
line.size = 0.8, line.alpha = 0.2)
curvesplot(r$res, xmax = max(f$omicdata$dose),
facetby = "trend", line.size = 1, line.alpha = 0.2)
# (2) an example on a microarray data set (a subsample of a greater data set)
#
datafilename <- system.file("extdata", "transcripto_sample.txt", package="DRomics")
(o <- microarraydata(datafilename, check = TRUE, norm.method = "cyclicloess"))
(s_quad <- itemselect(o, select.method = "quadratic", FDR = 0.001))
(f <- drcfit(s_quad, progressbar = TRUE))
(r <- bmdcalc(f))
# plot split by trend and model with BMR-BMD points added on curves
# adding transparency
curvesplot(r$res, xmax = max(f$omicdata$dose),
line.alpha = 0.2, line.size = 0.8,
addBMD = TRUE, point.alpha = 0.2, point.size = 1.5,
facetby = "trend", facetby2 = "model")
# same plot without scaling and not in log dose scale
curvesplot(r$res, xmax = max(f$omicdata$dose),
line.alpha = 0.2, line.size = 0.8, dose_log_transfo = FALSE,
addBMD = TRUE, point.alpha = 0.2, point.size = 1.5,
scaling = FALSE, facetby = "trend", facetby2 = "model")
# (3) An example from data published by Larras et al. 2020
# in Journal of Hazardous Materials
# https://doi.org/10.1016/j.jhazmat.2020.122727
# a dataframe with metabolomic results (output $res of bmdcalc() or bmdboot() functions)
resfilename <- system.file("extdata", "triclosanSVmetabres.txt", package="DRomics")
res <- read.table(resfilename, header = TRUE, stringsAsFactors = TRUE)
str(res)
# a dataframe with annotation of each item identified in the previous file
# each item may have more than one annotation (-> more than one line)
annotfilename <- system.file("extdata", "triclosanSVmetabannot.txt", package="DRomics")
annot <- read.table(annotfilename, header = TRUE, stringsAsFactors = TRUE)
str(annot)
# Merging of both previous dataframes
# in order to obtain an extenderes dataframe
# bootstrap results and annotation
extendedres <- merge(x = res, y = annot, by.x = "id", by.y = "metab.code")
head(extendedres)
# Plot of the dose-response curves by pathway colored by trend
# with BMR-BMD points added on curves
curvesplot(extendedres, facetby = "path_class", npoints = 100, line.size = 0.5,
colorby = "trend", xmax = 7, addBMD = TRUE)
# The same plot not in log scale
curvesplot(extendedres, facetby = "path_class", npoints = 100, line.size = 0.5,
dose_log_transfo = FALSE, colorby = "trend", xmin = 0, xmax = 7)
# The same plot in log scale without scaling
curvesplot(extendedres, facetby = "path_class", npoints = 100, line.size = 0.5,
colorby = "trend", scaling = FALSE, xmax = 7)
# Plot of the dose-response curves split by pathway and by trend
# for a selection pathway
chosen_path_class <- c("Membrane transport", "Lipid metabolism")
ischosen <- is.element(extendedres$path_class, chosen_path_class)
curvesplot(extendedres[ischosen, ],
facetby = "trend", facetby2 = "path_class",
npoints = 100, line.size = 0.5, xmax = 7)
# Plot of the dose-response curves for a specific pathway
# in this example the "lipid metabolism" pathclass
LMres <- extendedres[extendedres$path_class == "Lipid metabolism", ]
curvesplot(LMres, facetby = "id", npoints = 100, line.size = 0.8, point.size = 2,
colorby = "trend", xmax = 7)
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