trendplot | R Documentation |
Provides a plot of the repartition of dose-response trends per group of items.
trendplot(extendedres, group, facetby, ncol4faceting, add.color = TRUE)
extendedres |
the dataframe of results provided by drcfit (fitres)
or bmdcalc (res)
or a subset of this data frame (selected lines). This dataframe should be extended
with additional columns coming for the group (for example from the functional
annotation of items) and/or for another level (for example the molecular level),
and some lines
can be replicated if their corresponding item has more than one annotation.
This extended dataframe
must at least contain as results of the dose-response modelling
the column giving the trend ( |
group |
the name of the column of |
facetby |
optional argument naming the column of |
ncol4faceting |
number of columns for faceting. |
add.color |
if TRUE a color is added coding for the trend. |
a ggplot object.
Marie-Laure Delignette-Muller
See bmdplotwithgradient
and curvesplot
.
# (1)
# An example from the paper published by Larras et al. 2020
# in Journal of Hazardous Materials
# https://doi.org/10.1016/j.jhazmat.2020.122727
# the dataframe with metabolomic results
resfilename <- system.file("extdata", "triclosanSVmetabres.txt", package="DRomics")
res <- read.table(resfilename, header = TRUE, stringsAsFactors = TRUE)
str(res)
# the 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
extendedres <- merge(x = res, y = annot, by.x = "id", by.y = "metab.code")
head(extendedres)
# (1.a) Trendplot by pathway
trendplot(extendedres, group = "path_class")
# (1.b) Trendplot by pathway without color
trendplot(extendedres, group = "path_class", add.color = FALSE)
# (1.c) Reordering of the groups before plotting
extendedres$path_class <- factor(extendedres$path_class,
levels = sort(levels(extendedres$path_class), decreasing = TRUE))
trendplot(extendedres, group = "path_class", add.color = FALSE)
# (2)
# An example with two molecular levels
#
### Rename metabolomic results
metabextendedres <- extendedres
# Import the dataframe with transcriptomic results
contigresfilename <- system.file("extdata", "triclosanSVcontigres.txt", package = "DRomics")
contigres <- read.table(contigresfilename, header = TRUE, stringsAsFactors = TRUE)
str(contigres)
# Import the dataframe with functional annotation (or any other descriptor/category
# you want to use, here KEGG pathway classes)
contigannotfilename <- system.file("extdata", "triclosanSVcontigannot.txt", package = "DRomics")
contigannot <- read.table(contigannotfilename, header = TRUE, stringsAsFactors = TRUE)
str(contigannot)
# Merging of both previous dataframes
contigextendedres <- merge(x = contigres, y = contigannot, by.x = "id", by.y = "contig")
# to see the structure of this dataframe
str(contigextendedres)
### Merge metabolomic and transcriptomic results
extendedres <- rbind(metabextendedres, contigextendedres)
extendedres$molecular.level <- factor(c(rep("metabolites", nrow(metabextendedres)),
rep("contigs", nrow(contigextendedres))))
str(extendedres)
### trend plot of both molecular levels
# optional inverse alphabetic ordering of groups for the plot
extendedres$path_class <- factor(extendedres$path_class,
levels = sort(levels(extendedres$path_class), decreasing = TRUE))
trendplot(extendedres, group = "path_class", facetby = "molecular.level")
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