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) ), and the package name stands for "SIngle CEll Growth Analysis in R".

R package sicegar

Originally written by M. Umut Caglar and Claus O. Wilke. Updates and current maintenance by Johanna Hardin, Sam Butler, Phineus Choi, Thomas Matheis, and Mira Terdiman.

This package aims to quantify time intensity data by using sigmoidal and double sigmoidal curves. It fits sigmoidal and double sigmoidal curves on to time vs intensity data. Then both fits are used to make a decision on which model (sigmoidal, double sigmoidal, no signal, or ambiguous) best describes the data. No signal means the intensity does not reach a high enough point or does not change at all over time. Sigmoidal means intensity starts from a small number than climbs to a maximum. Double sigmoidal means intensity starts from a small number, climbs to a maximum then starts to decay. Once the decision has been reached, the algorithm returns the parameters associated with the sigmoidal or double-sigmoidal model quantifying the time-intensity curve.

The package name stands for "SIngle CEll Growth Analysis in R".

CRAN: https://cran.r-project.org/package=sicegar/

Source: https://github.com/hardin47/sicegar/

Vignettes: https://hardin47.github.io/sicegar/