Package: Rcurvep 1.3.1
Jui-Hua Hsieh
Rcurvep: Concentration-Response Data Analysis using Curvep
An R interface for processing concentration-response datasets using Curvep, a response noise filtering algorithm. The algorithm was described in the publications (Sedykh A et al. (2011) <doi:10.1289/ehp.1002476> and Sedykh A (2016) <doi:10.1007/978-1-4939-6346-1_14>). Other parametric fitting approaches (e.g., Hill equation) are also adopted for ease of comparison. 3-parameter Hill equation from 'tcpl' package (Filer D et al., <doi:10.1093/bioinformatics/btw680>) and 4-parameter Hill equation from Curve Class2 approach (Wang Y et al., <doi:10.2174/1875397301004010057>) are available. Also, methods for calculating the confidence interval around the activity metrics are also provided. The methods are based on the bootstrap approach to simulate the datasets (Hsieh J-H et al. <doi:10.1093/toxsci/kfy258>). The simulated datasets can be used to derive the baseline noise threshold in an assay endpoint. This threshold is critical in the toxicological studies to derive the point-of-departure (POD).
Authors:
Rcurvep_1.3.1.tar.gz
Rcurvep_1.3.1.tar.gz(r-4.5-noble)Rcurvep_1.3.1.tar.gz(r-4.4-noble)
Rcurvep_1.3.1.tgz(r-4.4-emscripten)Rcurvep_1.3.1.tgz(r-4.3-emscripten)
Rcurvep.pdf |Rcurvep.html✨
Rcurvep/json (API)
NEWS
| # Install 'Rcurvep' in R: |
| install.packages('Rcurvep', repos = 'https://cloud.r-project.org') |
Bug tracker:https://github.com/moggces/rcurvep/issues2 issues
- zfishbeh - Subsets of concentration response datasets from zebrafish neurotoxicity assays
- zfishdev - Subsets of concentration response datasets from zebrafish developmental toxicity assays
- zfishdev_act - Activity output based on simulated datasets using zfishdev_all dataset
- zfishdev_all - Full sets of concentration response datasets from zebrafish developmental toxicity assays
Last updated 1 years agofrom:a0a26c319a. Checks:3 OK. Indexed: no.
| Target | Result | Latest binary |
|---|---|---|
| Doc / Vignettes | OK | Mar 05 2025 |
| R-4.5-linux | OK | Mar 05 2025 |
| R-4.4-linux | OK | Mar 05 2025 |
Exports:cal_knee_pointcombi_run_rcurvepcreate_datasetcurvepcurvep_defaultsestimate_dataset_bmrfit_cc2_modlfit_modlsget_hill_fit_configmerge_rcurvep_objsrun_fitrun_rcurvepsummarize_fit_outputsummarize_rcurvep_output
Dependencies:bootclicodetoolscolorspacecpp11digestdplyrfansifarverfurrrfuturegenericsggplot2globalsgluegtableisobandlabelinglatticelifecyclelistenvmagrittrMASSMatrixmgcvmunsellnlmeparallellypillarpkgconfigpurrrR6rbibutilsRColorBrewerRdpackrJavarlangscalesstringistringrtibbletidyrtidyselectutf8vctrsviridisLitewithr
Parallel Computing Examples Using Rcurvep
Rendered fromfuture_rcurvep.Rmdusingknitr::rmarkdownon Mar 05 2025.Last update: 2024-01-10
Started: 2024-01-10
Practical applications using Rcurvep package
Rendered frompractical_rcurvep.Rmdusingknitr::rmarkdownon Mar 05 2025.Last update: 2024-01-10
Started: 2021-01-07
Citation
To cite package ‘Rcurvep’ in publications use:
Hsieh J, Sedykh A, Zhao T (2024). Rcurvep: Concentration-Response Data Analysis using Curvep. R package version 1.3.1, https://CRAN.R-project.org/package=Rcurvep.
Corresponding BibTeX entry:
@Manual{,
title = {Rcurvep: Concentration-Response Data Analysis using
Curvep},
author = {Jui-Hua Hsieh and Alexander Sedykh and Tongan Zhao},
year = {2024},
note = {R package version 1.3.1},
url = {https://CRAN.R-project.org/package=Rcurvep},
}
Readme and manuals
Overview
The package provides an R interface for processing concentration-response datasets using Curvep, a response noise filtering algorithm. The algorithm was described in the publications (Sedykh A et al. (2011) doi:10.1289/ehp.1002476 and Sedykh A (2016) doi:10.1007/978-1-4939-6346-1_14).
Other parametric fitting approaches (e.g., Hill equation) are also adopted for ease of comparison. 3-parameter Hill equation from original tcpl package (Filer DL et al., doi:10.1093/bioinformatics/btw680) and 4-parameter Hill equation from Curve Class2 approach (Wang Y et al., doi:10.2174/1875397301004010057) are available.
Also, methods for calculating the confidence interval around the activity metrics are also provided. The methods are based on the bootstrap approach to simulate the datasets (Hsieh J-H et al. doi:10.1093/toxsci/kfy258). The simulated datasets can be used to derive the baseline noise threshold in an assay endpoint. This threshold is critical in the toxicological studies to derive the point-of-departure (POD).
Installation
# the development version from GitHub:
# install.packages("devtools")
devtools::install_github("moggces/Rcurvep")
devtools::install_github("moggces/Rcurvep", dependencies = TRUE, build_vignettes = TRUE)
Package structure
Usage
Run analysis
library(Rcurvep)
data("zfishbeh")
out_curvep <- combi_run_rcurvep(zfishbeh, TRSH = 30) # using Curvep with BMR = 30
out_fit1 <- run_fit(zfishbeh, modls = "cc2") # using Curve Class2 4-parameter hill
out_fit2 <- run_fit(zfishbeh, modls = c("cnst", "hill")) # using tcpl 3-parameter hill + constant model
Find BMR
data("zfishdev_act")
out_bmr <- estimate_dataset_bmr(zfishdev_act)
## $`1`
More Usage
To learn more about Rcurvep, start with the vignettes:
browseVignettes(package = "Rcurvep")
