View source: R/generate.toydata.R
generate.toydata | R Documentation |
Generation of a dataset of 500 i.i.d measurements as considered in the stochastic profiling model. Afterwards estimation of the model parameters and comparison of the estimates with the true value.
generate.toydata(model = "LN-LN")
model |
the chosen stochastic profiling model: "LN-LN", "rLN-LN" or "EXP-LN" |
This function first generates a dataset of 500 i.i.d. 10-cell samplings as considered in the stochastic profiling models "LN-LN", "rLN-LN" and "EXP-LN". The employed parameters are TY=2 (i.e. two different types of cells are assumed) and p=c(0.2,0.8) for all models. Furthermore, mu=c(1.5,-1.5) and sigma=0.2 for the LN-LN model, mu=c(1.5,-1.5) and sigma=(0.2,0.6) for the rLN-LN model, and mu=1.5, sigma=0.2 and lambda=0.5 for the EXP-LN model. The generated data is displayed in a histogram together with the theoretical probability density function. At the end of the estimation procedure, the profile log-likelihood plots are shown. Finally, the true and the estimated probability density functions are compared and the estimation results are printed.
A list as returned by stochprof.loop
, i.e. the following components:
mle |
maximum likelihood estimate |
neg-loglikeli |
value of the negative log-likelihood function at maximum likelihood estimate |
ci |
approximate marginal maximum likelihood confidence intervals for the maximum likelihood estimate |
pargrid |
matrix containing parameter combinations and according values of the target function |
bic |
Bayesian information criterion value |
adj.bic |
adjusted Bayesian information criterion value which takes into account the numbers of parameters that were estimated during the preanalysis of a gene cluster. Is only calculated if parameter |
pen |
penalization for densities not fulfilling required constraints. If |
Lisa Amrhein, Christiane Fuchs
Maintainer: Lisa Amrhein <amrheinlisa@gmail.com>
"Parameterizing cell-to-cell regulatory heterogeneities via stochastic transcriptional profiles" by Sameer S Bajikar*, Christiane Fuchs*, Andreas Roller, Fabian J Theis^ and Kevin A Janes^: PNAS 2014, 111(5), E626-635 (* joint first authors, ^ joint last authors) <doi:10.1073/pnas.1311647111>
"Pheno-seq - linking visual features and gene expression in 3D cell culture systems" by Stephan M. Tirier, Jeongbin Park, Friedrich Preusser, Lisa Amrhein, Zuguang Gu, Simon Steiger, Jan-Philipp Mallm, Teresa Krieger, Marcel Waschow, Bjoern Eismann, Marta Gut, Ivo G. Gut, Karsten Rippe, Matthias Schlesner, Fabian Theis, Christiane Fuchs, Claudia R. Ball, Hanno Glimm, Roland Eils & Christian Conrad: Sci Rep 9, 12367 (2019) <doi:10.1038/s41598-019-48771-4>
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