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")the chosen stochastic profiling model: "LN-LN", "rLN-LN" or "EXP-LN"
A list as returned by stochprof.loop, i.e. the following components:
maximum likelihood estimate
value of the negative log-likelihood function at maximum likelihood estimate
approximate marginal maximum likelihood confidence intervals for the maximum likelihood estimate
matrix containing parameter combinations and according values of the target function
Bayesian information criterion value
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 subgroups is given, otherwise set to NULL.
penalization for densities not fulfilling required constraints. If use.constraints is FALSE, this has no practical meaning. If use.constraints is TRUE, this value is included in loglikeli.
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.
"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>