Data Analysis Functions for 'SBpipe' Package

Provides an API for analysing repetitive parameter estimations and simulations of mathematical models. Examples of mathematical models are Ordinary Differential equations (ODEs) or Stochastic Differential Equations (SDEs) models. Among the analyses for parameter estimation 'sbpiper' calculates statistics and generates plots for parameter density, PCA of the best fits, parameter profile likelihood estimations (PLEs), and 2D parameter PLEs. These results can be generated using all or a subset of the best computed parameter sets. Among the analyses for model simulation 'sbpiper' calculates statistics and generates plots for deterministic and stochastic time courses via cartesian and heatmap plots. Plots for the scan of one or two model parameters can also be generated. This package is primarily used by the software 'SBpipe'. Citation: Dalle Pezze P, Le Novère N. SBpipe: a collection of pipelines for automating repetitive simulation and analysis tasks. BMC Systems Biology. 2017;11:46. .


Introduction

This R package provides an API for analysing repetitive parameter estimations and simulations of mathematical models. Examples of mathematical models are Ordinary Differential equations (ODEs) or Stochastic Differential Equations (SDEs) models. Among the analyses for parameter estimation, SBpiper calculates statistics and generates plots for parameter density, PCA of the best fits, parameter profile likelihood estimations (PLEs), and 2D parameter PLEs. These results can be generated using all or a subset of the best computed parameter sets. Among the analyses for model simulation, SBpiper calculates statistics and generates plots for deterministic and stochastic time courses via cartesian and heatmap plots. Plots for the scan of one or two model parameters can also be generated. This package is primarily used by the software SBpipe.

Citation: Dalle Pezze P, Le Novère N. SBpipe: a collection of pipelines for automating repetitive simulation and analysis tasks. BMC Systems Biology. 2017 Apr;11:46. DOI:10.1186/s12918-017-0423-3

Using this package within SBpipe

This dependency library is automatically installed by SBpipe via provided script or using conda, so no further step is needed. To install SBpipe, see here.

Installation

The stable version of SBpiper can be installed from:

  • CRAN. Start R (≥ 3.2.0) and run:
conda install -c bioconda r-sbpiper

Once installed, the package is loaded as usual:

> library(sbpiper)

Package building (developers)

After cloning this repository, developers can check and build SBpiper using the following commands:

> devtools::check("sbpiper")
> devtools::build("sbpiper")

or outside R with the commands:

R CMD build .
R CMD check *tar.gz --as-cran

Finally, sbpiper is installed with the command:

R CMD INSTALL sbpiper_X.Y.Z.tar.gz

Here are the instructions for testing the conda package for SBpiper. This is stored in the pdp10 conda channel.

# install anaconda-client
conda install anaconda-client
anaconda login

# build the conda package (channel: pdp10):
conda-build conda_recipe/meta.yaml -c conda-forge -c defaults
 
# install the conda package (channel: pdp10):
conda install sbpiper -c pdp10 -c conda-forge -c defaults

Instructions for creating the recipe for SBpiper for the bioconda channel can be found here.

News

Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.

install.packages("sbpiper")

1.9.0 by Piero Dalle Pezze, 8 months ago


https://github.com/pdp10/sbpiper


Report a bug at https://github.com/pdp10/sbpiper/issues


Browse source code at https://github.com/cran/sbpiper


Authors: Piero Dalle Pezze [aut, cre, cph]


Documentation:   PDF Manual  


MIT + file LICENSE license


Imports colorRamps, data.table, factoextra, FactoMineR, ggplot2, grDevices, Hmisc, reshape2, scales, stats, stringr, utils


See at CRAN