Package: sicegar 0.3.0

sicegar: Analysis of Single-Cell Viral Growth Curves

Aims to quantify time intensity data by using sigmoidal and double sigmoidal curves. It fits straight lines, sigmoidal, and double sigmoidal curves on to time vs intensity data. Then all the fits are used to make decision on which model best describes the data. This method was first developed in the context of single-cell viral growth analysis (for details, see Caglar et al. (2018) <doi:10.7717/peerj.4251>), and the package name stands for "SIngle CEll Growth Analysis in R".

Authors:Johanna Hardin [aut, cre], Samuel Butler [aut], Phineus Choi [aut], Thomas Matheis [aut], Mira Terdiman [aut], M. Umut Caglar [aut], Claus O. Wilke [aut]

sicegar_0.3.0.tar.gz
sicegar_0.3.0.zip(r-4.7-any)sicegar_0.3.0.zip(r-4.6-any)sicegar_0.3.0.zip(r-4.5-any)
sicegar_0.3.0.tgz(r-4.6-any)sicegar_0.3.0.tgz(r-4.5-any)
sicegar_0.3.0.tar.gz(r-4.7-any)sicegar_0.3.0.tar.gz(r-4.6-any)
sicegar_0.3.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
sicegar/json (API)

# Install 'sicegar' in R:
install.packages('sicegar', repos = c('https://hardin47.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/hardin47/sicegar/issues

Pkgdown/docs site:https://hardin47.github.io

On CRAN:

Conda:

6.80 score 2 scripts 361 downloads 2 mentions 21 exports 33 dependencies

Last updated from:4205fbe399. Checks:7 NOTE, 2 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develNOTE186
source / vignettesOK207
linux-releaseNOTE150
macos-releaseNOTE177
macos-oldrelNOTE170
windows-develNOTE112
windows-releaseNOTE101
windows-oldrelNOTE93
wasm-releaseOK112

Exports:categorizecategorize_h0dataCheckdoublesigmoidalFitFormuladoublesigmoidalFitFormula_h0doublesigmoidalFitFunctiondoublesigmoidalFitFunction_h0figureModelCurvesfitAndCategorizemultipleFitFunctionmultipleFitFunction_h0normalizeDataparameterCalculationparameterCalculation_h0preCategorizesameSourceDataChecksigmoidalFitFormulasigmoidalFitFormula_h0sigmoidalFitFunctionsigmoidalFitFunction_h0unnormalizeData

Dependencies:clicpp11dplyrfarverfBasicsgenericsggplot2gluegssgtableisobandlabelinglifecyclemagrittrMASSminpack.lmpillarpkgconfigR6RColorBrewerrlangS7scalesspatialstabledisttibbletidyselecttimeDatetimeSeriesutf8vctrsviridisLitewithr

Adjusting data to use with sicegar
Adjusting the data structure | Too few time points / observations | Decreasing trend
Rendered fromadjusting_data.Rmdusingknitr::rmarkdown

Last update: 2025-11-15
Started: 2025-09-30

Allowing the lower asymptote parameter to vary freely
Introduction | Fitting the models to the data | Model fitting components (h0 free) | Model parameters
Rendered fromh0_functions.Rmdusingknitr::rmarkdown

Last update: 2025-11-15
Started: 2025-09-30

Calculation of additional parameters of interest
Additional parameters for the sigmoidal model | Additional parameters for the double-sigmoidal model
Rendered fromadditional_parameters.Rmdusingknitr::rmarkdown

Last update: 2025-11-07
Started: 2017-06-22

Fitting individual models
Fitting the models to the data
Rendered fromfitting_individual_models.Rmdusingknitr::rmarkdown

Last update: 2025-09-30
Started: 2017-06-23

Identifying the best-fitting model category
The decision process
Rendered fromcategorizing_fits.Rmdusingknitr::rmarkdown

Last update: 2025-09-30
Started: 2017-06-23

Introduction
Example fit on simulated input data | The fit object
Rendered fromintroduction.Rmdusingknitr::rmarkdown

Last update: 2025-09-30
Started: 2017-06-21

Plotting the fitted models
Rendered fromplotting_fitted_models.Rmdusingknitr::rmarkdown

Last update: 2025-09-30
Started: 2017-06-23