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Last data update: 2014.03.03

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Results 1 - 3 of 3 found.
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Hiiragi2013 : Cell-to-cell expression variability followed by signal reinforcement progressively segregates early mouse lineages

Package: Hiiragi2013
Type: Package
Title: Cell-to-cell expression variability followed by signal
reinforcement progressively segregates early mouse lineages
Version: 1.8.0
Author: Andrzej Oles, Wolfgang Huber
Maintainer: Andrzej Oles <andrzej.oles@embl.de>
Description: This package contains the experimental data and a complete executable transcript (vignette) of the statistical analysis presented in the paper "Cell-to-cell expression variability followed by signal reinforcement progressively segregates early mouse lineages" by Y. Ohnishi, W. Huber, A. Tsumura, M. Kang, P. Xenopoulos, K. Kurimoto, A. K. Oles, M. J. Arauzo-Bravo, M. Saitou, A.-K. Hadjantonakis and T. Hiiragi; Nature Cell Biology (2014) 16(1): 27-37. doi: 10.1038/ncb2881."
License: Artistic-2.0
LazyLoad: true
Depends: R (>= 3.0.0), affy, Biobase, boot, clue, cluster, genefilter,
geneplotter, gplots, gtools, KEGGREST, MASS, mouse4302.db,
RColorBrewer, xtable
Imports: grid, lattice, latticeExtra
Suggests: ArrayExpress, BiocStyle
biocViews: ExperimentData, MicroarrayData, qPCRData,
ReproducibleResearch
NeedsCompilation: no
Packaged: 2016-05-07 20:43:09 UTC; biocbuild

● Data Source: BioConductor
● BiocViews: ExperimentData, MicroarrayData, ReproducibleResearch, qPCRData
● 3 images, 6 functions, 4 datasets
● Reverse Depends: 0

ROntoTools : R Onto-Tools suite

Package: ROntoTools
Type: Package
Title: R Onto-Tools suite
Version: 2.0.0
Author: Calin Voichita <calin@wayne.edu> and Sahar Ansari
<saharansari@wayne.edu> and Sorin Draghici <sorin@wayne.edu>
Maintainer: Calin Voichita <calin@wayne.edu>
Description: Suite of tools for functional analysis.
biocViews: NetworkAnalysis, Microarray, GraphsAndNetworks
License: CC BY-NC-ND 4.0 + file LICENSE
Depends: methods, graph, boot, KEGGREST, KEGGgraph, Rgraphviz
Suggests: RUnit, BiocGenerics
RoxygenNote: 5.0.1
NeedsCompilation: no
Packaged: 2016-05-04 04:54:40 UTC; biocbuild

● Data Source: BioConductor
● BiocViews: GraphsAndNetworks, Microarray, NetworkAnalysis
● 9 images, 25 functions, 0 datasets
● Reverse Depends: 0

PAPi : Predict metabolic pathway activity based on metabolomics data

Package: PAPi
Type: Package
Title: Predict metabolic pathway activity based on metabolomics data
Version: 1.12.0
Date: 2013-03-27
Author: Raphael Aggio
Maintainer: Raphael Aggio <raphael.aggio@gmail.com>
Description: The Pathway Activity Profiling - PAPi - is an R package for predicting the activity of metabolic pathways based solely on a metabolomics data set containing a list of metabolites identified and their respective abundances in different biological samples. PAPi generates hypothesis that improves the final biological interpretation. See Aggio, R.B.M; Ruggiero, K. and Villas-Boas, S.G. (2010) - Pathway Activity Profiling (PAPi): from metabolite profile to metabolic pathway activity. Bioinformatics.
License: GPL(>= 2)
Depends: R (>= 2.15.2), svDialogs, KEGGREST
biocViews: MassSpectrometry, Metabolomics
NeedsCompilation: no
Packaged: 2016-05-04 04:55:17 UTC; biocbuild

● Data Source: BioConductor
● BiocViews: MassSpectrometry, Metabolomics
● 0 images, 6 functions, 4 datasets
● Reverse Depends: 0