Dose Response for Omics
Build an R object that can be used as data input in DRomics
Selection of significantly responsive items
ECDF plot of a given quantile of a variable calculated by group
Import and check of continuous anchoring apical data
Plot of fitted curves
Dose response modelling for responsive items
ECDF plot of a variable with given confidence intervals on this variab...
Computation of confidence interval on benchmark doses by bootstrap
Computation of benchmark doses for responsive items
Filtering BMDs according to estimation quality
BMD plot optionally with confidence intervals on BMD
BMD plot with color gradient
Import and check of continuous omic data (e.g. metabolomic data)
Import, check and normalization of single-channel microarray data
Performs and plots the results of a PCA on omic data
Import, check and normalization and transformation of RNAseq data
Concentration-response effect of triclosan in Scenedesmus vacuolatus
Selection of groups on which to focus
Plot of a summary of BMD values per group of items
Dose-reponse plot for target items
Plot of the repartition of trends per group
Dose-response kidney transcriptomic effect of Tetrachloroethylene in m...
Several functions are provided for dose-response (or concentration-response) characterization from omics data. 'DRomics' is especially dedicated to omics data obtained using a typical dose-response design, favoring a great number of tested doses (or concentrations) rather than a great number of replicates (no need of replicates). 'DRomics' provides functions 1) to check, normalize and or transform data, 2) to select monotonic or biphasic significantly responding items (e.g. probes, metabolites), 3) to choose the best-fit model among a predefined family of monotonic and biphasic models to describe each selected item, 4) to derive a benchmark dose or concentration and a typology of response from each fitted curve. In the available version data are supposed to be single-channel microarray data in log2, RNAseq data in raw counts, or already pretreated continuous omics data (such as metabolomic data) in log scale. In order to link responses across biological levels based on a common method, 'DRomics' also handles apical data as long as they are continuous and follow a normal distribution for each dose or concentration, with a common standard error. For further details see Delignette-Muller et al (2023) <DOI:10.24072/pcjournal.325> and Larras et al (2018) <DOI:10.1021/acs.est.8b04752>.
Useful links