Package: Rcurvep 1.3.2

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:Jui-Hua Hsieh [aut, cre], Alexander Sedykh [aut], Fred Parham [ctb], Yuhong Wang [ctb], Tongan Zhao [aut], Ruili Huang [ctb]

Rcurvep_1.3.2.tar.gz
Rcurvep_1.3.2.zip(r-4.7)Rcurvep_1.3.2.zip(r-4.6)Rcurvep_1.3.2.zip(r-4.5)
Rcurvep_1.3.2.tgz(r-4.6-any)Rcurvep_1.3.2.tgz(r-4.5-any)
Rcurvep_1.3.2.tar.gz(r-4.7-any)Rcurvep_1.3.2.tar.gz(r-4.6-any)
Rcurvep_1.3.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
Rcurvep/json (API)

# Install 'Rcurvep' in R:
install.packages('Rcurvep', repos = c('https://moggces.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/moggces/rcurvep/issues

Pkgdown/docs site:https://moggces.github.io

Uses libs:
  • openjdk– OpenJDK Java runtime, using Hotspot JIT
Datasets:
  • 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

On CRAN:

Conda:

openjdk

4.76 score 3 stars 19 scripts 572 downloads 1 mentions 14 exports 40 dependencies

Last updated from:7dd6963393. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK233
source / vignettesOK240
linux-release-x86_64OK223
macos-release-arm64OK269
macos-oldrel-arm64OK185
windows-develOK158
windows-releaseOK151
windows-oldrelOK172
wasm-releaseOK176

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:bootclicodetoolscpp11digestdplyrfarverfurrrfuturegenericsggplot2globalsgluegtableisobandlabelinglifecyclelistenvmagrittrparallellypillarpkgconfigpurrrR6rbibutilsRColorBrewerRdpackrJavarlangS7scalesstringistringrtibbletidyrtidyselectutf8vctrsviridisLitewithr

Parallel Computing Examples Using Rcurvep
Set up the packages | datasets from Rcurvep package | When no preferred BMR and would like to do a exhaustive search | Calculate 10 times and compare the results | When preferred BMRs are available for endpoints | Get the BMRs for each endpoint | Join the BMRs to the concentration-response data | Set up the expressions | Fitting based on simulated curves using run_fit | Fitting based on simulated curves using run_fit and pmap | Fitting based on original data | Use modls = hill parameter | Calculate 5 times and compare the results | Use modls = cc2 parameter
Rendered fromfuture_rcurvep.Rmdusingknitr::rmarkdown

Last update: 2025-05-08
Started: 2024-01-08

Practical applications using Rcurvep package
Install the package | Load the library | Load the sample dataset | Rcurvep method | Run Rcurvep on datasets using combi_run_rcurvep() | Run Rcurvep when there is a preferred BMR | Summarize activity data from Rcurvep | Add confidence interval for activity data from Rcurvep | Run Rcurvep when an optimal/preferred BMR is unknown | Estimate the BMR | Display the diagnostic curves for BMR estimation | Run parametric fitting on datasets | Summarize parametric fitting results using a preferred BMR | Inspect concordance between two parametric fits | Add confidence interval for activity data from parametric fitting (currently is only available to 3-parameter Hill equation from tcpl)
Rendered frompractical_rcurvep.Rmdusingknitr::rmarkdown

Last update: 2023-12-23
Started: 2020-01-08