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stochprofML
View on CRAN: Click
here
Download and install stochprofML package within the R console
Install from CRAN:
install.packages("stochprofML")
Install from Github:
library("remotes")
install_github("cran/stochprofML")
Install by package version:
library("remotes")
install_version("stochprofML", "2.0.3")
Attach the package and use:
library("stochprofML")
Maintained by
Lisa Amrhein
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2014-05-22
Latest Update: 2020-06-10
Description:
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 ). 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)
).
How to cite:
Lisa Amrhein (2014). stochprofML: Stochastic Profiling using Maximum Likelihood Estimation. R package version 2.0.3, https://cran.r-project.org/web/packages/stochprofML
Previous versions and publish date:
Other packages that cited stochprofML R package
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Other R packages that stochprofML depends,
imports, suggests or enhances
Functions, R codes and Examples using
the stochprofML R package
Some associated functions: analyze.sod2 . analyze.toycluster . calculate.ci.EXPLN . calculate.ci.LNLN . calculate.ci.rLNLN . comb.summands . d.sum.of.mixtures.EXPLN . d.sum.of.mixtures.LNLN . d.sum.of.mixtures.rLNLN . generate.toydata . mix.d.sum.of.mixtures.EXPLN . mix.d.sum.of.mixtures.LNLN . mix.d.sum.of.mixtures.rLNLN . penalty.constraint.EXPLN . penalty.constraint.LNLN . penalty.constraint.rLNLN . set.model.functions . sod2 . stochasticProfilingData . stochasticProfilingML . stochprof.loop . stochprof.results.EXPLN . stochprof.results.LNLN . stochprof.results.rLNLN . stochprof.search.EXPLN . stochprof.search.LNLN . stochprof.search.rLNLN . stochprofML-package . toycluster.EXPLN . toycluster.LNLN . toycluster.rLNLN .
Some associated R codes: analyze.sod2.R . analyze.toycluster.R . backtransform.par.EXPLN.R . backtransform.par.LNLN.R . backtransform.par.rLNLN.R . calculate.ci.EXPLN.R . calculate.ci.LNLN.R . calculate.ci.rLNLN.R . comb.summands.R . d.sum.of.exp.types.R . d.sum.of.lognormal.types.R . d.sum.of.lognormals.R . d.sum.of.mixtures.EXPLN.R . d.sum.of.mixtures.LNLN.R . d.sum.of.mixtures.rLNLN.R . d.sum.of.types.LNLN.R . d.sum.of.types.rLNLN.R . draw.parameters.EXPLN.R . draw.parameters.LNLN.R . draw.parameters.rLNLN.R . generate.toydata.R . get.range.EXPLN.R . get.range.LNLN.R . get.range.rLNLN.R . lognormal.exp.convolution.R . mix.d.sum.of.mixtures.EXPLN.R . mix.d.sum.of.mixtures.LNLN.R . mix.d.sum.of.mixtures.rLNLN.R . penalty.constraint.EXPLN.R . penalty.constraint.LNLN.R . penalty.constraint.rLNLN.R . r.sum.of.mixtures.EXPLN.R . r.sum.of.mixtures.LNLN.R . r.sum.of.mixtures.rLNLN.R . set.model.functions.R . stochasticProfilingData.R . stochasticProfilingML.R . stochprof.expit.R . stochprof.logit.R . stochprof.loop.R . stochprof.results.EXPLN.R . stochprof.results.LNLN.R . stochprof.results.rLNLN.R . stochprof.search.EXPLN.R . stochprof.search.LNLN.R . stochprof.search.rLNLN.R . stochprofML-internal.R . transform.par.EXPLN.R . transform.par.LNLN.R . transform.par.rLNLN.R . Full stochprofML package functions and examples
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