curvesplot: Plot of fitted curves

View source: R/curvesplot.R

curvesplotR 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 = 0, xmax, y0shift = TRUE, scaling = FALSE,
                       facetby, facetby2, free.y.scales = FALSE, ncol4faceting,
                       colorby, removelegend = FALSE,  
                        npoints = 500, line.size = 0.2, 
                        line.alpha = 1, dose_log_transfo = FALSE)

Arguments

extendedres

the dataframe of results provided by bmdcalc (res) or drcfit (fitres) 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).

xmin

Minimal dose/concentration for definition of the x range (by default 0).

xmax

Maximal dose/concentration for definition of the x range (can be defined as max(f$omicdata$dose) with f the output of drcfit()).

y0shift

If TRUE (default choice) curves are all shifted to have the theoretical signal at the control at 0.

scaling

If TRUE 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

Size 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.

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)

# (1.a) 
# Default plot of all the curves 
#
curvesplot(f$fitres, xmax = max(f$omicdata$dose))



# the same plot with dose in log scale (need xmin != 0 in input)
curvesplot(f$fitres, xmin = 0.1, xmax = max(f$omicdata$dose),
  dose_log_transfo = TRUE)

# the equivalent using the output of bmdcalc
(r <- bmdcalc(f))
curvesplot(r$res, xmax = max(f$omicdata$dose))

# 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 playing with y0shift and scaling
#
# without shifting y0 values to 0
curvesplot(f$fitres, xmax = max(f$omicdata$dose), y0shift = FALSE)

# with shifting y0 values to 0 and scaling
curvesplot(f$fitres, xmax = max(f$omicdata$dose), 
  y0shift = TRUE, scaling = TRUE)

# (1.c) 
# Plot of all the curves colored by model, with one facet per trend
#
curvesplot(f$fitres, xmax = max(f$omicdata$dose), 
  facetby = "trend", colorby = "model")

# changing the number of columns
curvesplot(f$fitres, xmax = max(f$omicdata$dose), 
  facetby = "trend", colorby = "model", ncol4faceting = 4)

# playing with size and transparency of lines
curvesplot(f$fitres, xmax = max(f$omicdata$dose), 
  facetby = "trend", colorby = "model", 
  line.size = 1, line.alpha = 0.5)
  
# (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
curvesplot(f$fitres, xmax = max(f$omicdata$dose), 
facetby = "trend", facetby2 = "model")

# plot split by trend and model with scaling
curvesplot(f$fitres, xmax = max(f$omicdata$dose),
scaling = TRUE,
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
curvesplot(extendedres, facetby = "path_class", npoints = 100, line.size = 1,
           colorby = "trend",
           xmin = 0, xmax = 8) 

# Plot of the scaled dose-response curves by pathway colored by trend
curvesplot(extendedres, facetby = "path_class", npoints = 100, line.size = 1,
           colorby = "trend", scaling = TRUE,
           xmin = 0, xmax = 8) 


# 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 = 1,
           xmin = 0, xmax = 8) 

# 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 = 1,
           colorby = "trend",
           xmin = 0, xmax = 8) 




aursiber/DRomics documentation built on Nov. 28, 2022, 11:55 a.m.