stochprofML: Stochastic Profiling using Maximum Likelihood Estimation

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>).

Version: 2.0.0
Depends: R (≥ 2.0)
Imports: MASS, numDeriv
Published: 2019-11-19
Author: Lisa Amrhein [aut, cre], Christiane Fuchs [aut], Christoph Kurz [ctb] (Author to function comb.summands.R')
Maintainer: Lisa Amrhein <amrheinlisa at gmail.com>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
Materials: NEWS
CRAN checks: stochprofML results

Downloads:

Reference manual: stochprofML.pdf
Package source: stochprofML_2.0.0.tar.gz
Windows binaries: r-devel: stochprofML_2.0.0.zip, r-devel-gcc8: stochprofML_2.0.0.zip, r-release: stochprofML_2.0.0.zip, r-oldrel: stochprofML_2.0.0.zip
OS X binaries: r-release: stochprofML_2.0.0.tgz, r-oldrel: stochprofML_2.0.0.tgz
Old sources: stochprofML archive

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