DRomics (version 1.0-2)

DRomics-package: Overview of the DRomics package

Description

DRomics provides several functions for dose-response (or concentration-response) characterization from omics data. It is especially dedicated to omics data obtained using a typical dose-response design, favoring a great number of tested doses (or concentrations, at least 6, and the more the better) rather than a great number of replicates (no need of three replicates). DRomics provides four main functions described as follows:

• omicdata to check and normalize data,

• itemselect to select monotonic or biphasic significant responses,

• drcfit to choose the best-fit model among a predefined family of monotonic and biphasic models to describe each significant response and classify it in a typology of response,

• and bmdcalc to derive a benchmark dose or concentration from each fitted curve.

In the available version data are supposed to be single-channel microarray data transformed in log2, or another type of data that can be directly fitted by least-square regression without any normalization step. In the future this tool will also be able to process RNA-seq data.

Below is proposed an example including each step or the workflow.

See Also

See omicdata, itemselect, drcfit, bmdcalc for details about each function.

Examples

# NOT RUN {
  
# }
# NOT RUN {

# Step 1: importation, check and normalization of data if need
#
## here cyclicloess normalization of a small transcriptomics data set
## (sample of a real data set)

datatxt <- system.file("extdata", "transcripto_sample.txt", package="DRomics")
(o <- omicdata(datatxt, check = TRUE, norm.method = "cyclicloess"))
plot(o)

# Step 2: item selection using the quadratic method
#
## the quadratic method is the one we preconize to select both
## monotonic and biphasic curves from
## a typical dose-response design (with few replicates per dose)

(s_quad <- itemselect(o, select.method = "quadratic", FDR = 0.001))


# Step 3: fit of dose-response models, choice of the best fit for each curve
# and definition of the typology of response
#

(f <- drcfit(s_quad, progressbar = TRUE))
f$fitres
plot(f)

# Step 4: calculation of x-fold and z-SD benchmark doses 
#

(r <- bmdcalc(f, z = 1, x = 10))
plot(r, BMDtype = "zSD", plottype = "ecdf", bytypology = FALSE) 
plot(r, BMDtype = "xfold", plottype = "hist", bytypology = TRUE, hist.bins = 10) 
# }
# NOT RUN {

# About using the DRomics-shiny app
# 

if(interactive()) {
  appDir <- system.file("DRomics-shiny", package = "DRomics")
  shiny::runApp(appDir, display.mode = "normal")
}
# }