set.model.functions

<p>model for which one wishes to estimate the parameters: "LN-LN", "rLN-LN" or "EXP-LN"</p>

model

<p>Defines which model to use in the following and sets depending on this decistion some global model-dependent functions. This function is unly needed when using lower leverl functions of the package such as d.sum.of.mixtures which does not have its own model selection input.</p>

 model selection

 stochastic profiling 

 model dependent function definition 

New Version of the R package originally accompanying the paper
"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 <doi:10.1073/pnas.1311647111>). In this paper, we measure expression profiles
from small heterogeneous populations of cells, where each cell is assumed to be from a mixture of
lognormal distributions. We perform maximum likelihood estimation in order to infer the mixture ratio and
the parameters of these lognormal distributions from the cumulated expression measurements.
The main difference of this new package version to the previous one is that it is now possible to use
different n's, i.e. a dataset where each tissue sample originates from a different number of cells.
We used this on pheno-seq data, see: Tirier, S.M., Park, J., Preusser, F. et al. Pheno-seq - linking visual
features and gene expression in 3D cell culture systems. Sci Rep 9, 12367 (2019)
<doi:10.1038/s41598-019-48771-4>).

set.model.functions: Defines some global model-dependent functions

Description

Usage

Arguments

Value