analyze.sod2: Analysis of SOD2 data in stochastic profiling model

In lisaamrhein/stochprofML: Stochastic Profiling using Maximum Likelihood Estimation

Description

Estimation of the model parameters for the SOD2 dataset provided in this package.

Usage

analyze.sod2(model = "LN-LN", TY = 2, use.constraints = F)

Arguments

model	model for which one wishes to estimate the parameters: "LN-LN", "rLN-LN" or "EXP-LN"
TY	number of types of cells that is assumed in the stochastic model
use.constraints	if TRUE, constraints on the individual population densities are applied; see penalty.constraint.LNLN, penalty.constraint.rLNLN and penalty.constraint.EXPLN for details.

Details

The sod2 dataset contains real 10-cell samplings from the detoxifying enzyme, SOD2. This function estimates the parameters of the stochastic profiling models for this data. At the end, it graphically represents a histogram of the SOD2 data together with the estimated probability density function.

Value

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 (not applicable here, hence NULL)
pen	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.

Author(s)

Lisa Amrhein, Christiane Fuchs

Maintainer: Lisa Amrhein <[e-mail]>

References

"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>

lisaamrhein/stochprofML documentation built on Dec. 25, 2021, 9:02 p.m.