Showing 8 of total 8 results (show query)
bioc
netZooR:Unified methods for the inference and analysis of gene regulatory networks
netZooR unifies the implementations of several Network Zoo methods (netzoo, netzoo.github.io) into a single package by creating interfaces between network inference and network analysis methods. Currently, the package has 3 methods for network inference including PANDA and its optimized implementation OTTER (network reconstruction using mutliple lines of biological evidence), LIONESS (single-sample network inference), and EGRET (genotype-specific networks). Network analysis methods include CONDOR (community detection), ALPACA (differential community detection), CRANE (significance estimation of differential modules), MONSTER (estimation of network transition states). In addition, YARN allows to process gene expresssion data for tissue-specific analyses and SAMBAR infers missing mutation data based on pathway information.
Maintained by Tara Eicher. Last updated 12 days ago.
networkinferencenetworkgeneregulationgeneexpressiontranscriptionmicroarraygraphandnetworkgene-regulatory-networktranscription-factors
105 stars 7.98 scorebioc
MetNet:Inferring metabolic networks from untargeted high-resolution mass spectrometry data
MetNet contains functionality to infer metabolic network topologies from quantitative data and high-resolution mass/charge information. Using statistical models (including correlation, mutual information, regression and Bayes statistics) and quantitative data (intensity values of features) adjacency matrices are inferred that can be combined to a consensus matrix. Mass differences calculated between mass/charge values of features will be matched against a data frame of supplied mass/charge differences referring to transformations of enzymatic activities. In a third step, the two levels of information are combined to form a adjacency matrix inferred from both quantitative and structure information.
Maintained by Thomas Naake. Last updated 5 months ago.
immunooncologymetabolomicsmassspectrometrynetworkregression
4.70 score 1 scriptsbioc
nethet:A bioconductor package for high-dimensional exploration of biological network heterogeneity
Package nethet is an implementation of statistical solid methodology enabling the analysis of network heterogeneity from high-dimensional data. It combines several implementations of recent statistical innovations useful for estimation and comparison of networks in a heterogeneous, high-dimensional setting. In particular, we provide code for formal two-sample testing in Gaussian graphical models (differential network and GGM-GSA; Stadler and Mukherjee, 2013, 2014) and make a novel network-based clustering algorithm available (mixed graphical lasso, Stadler and Mukherjee, 2013).
Maintained by Nicolas Staedler. Last updated 5 months ago.
4.30 score 7 scriptsbioc
splineTimeR:Time-course differential gene expression data analysis using spline regression models followed by gene association network reconstruction
This package provides functions for differential gene expression analysis of gene expression time-course data. Natural cubic spline regression models are used. Identified genes may further be used for pathway enrichment analysis and/or the reconstruction of time dependent gene regulatory association networks.
Maintained by Herbert Braselmann. Last updated 5 months ago.
geneexpressiondifferentialexpressiontimecourseregressiongenesetenrichmentnetworkenrichmentnetworkinferencegraphandnetwork
4.01 score 17 scripts
cran
GeneNet:Modeling and Inferring Gene Networks
Analyzes gene expression (time series) data with focus on the inference of gene networks. In particular, GeneNet implements the methods of Schaefer and Strimmer (2005a,b,c) and Opgen-Rhein and Strimmer (2006, 2007) for learning large-scale gene association networks (including assignment of putative directions).
Maintained by Korbinian Strimmer. Last updated 3 years ago.
3.18 score 5 dependents
mjafin
GeneCycle:Identification of Periodically Expressed Genes
The GeneCycle package implements the approaches of Wichert et al. (2004) <doi:10.1093/bioinformatics/btg364>, Ahdesmaki et al. (2005) <doi:10.1186/1471-2105-6-117> and Ahdesmaki et al. (2007) <DOI:10.1186/1471-2105-8-233> for detecting periodically expressed genes from gene expression time series data.
Maintained by Miika Ahdesmaki. Last updated 4 years ago.
1 stars 2.81 score 64 scripts
anastasiou-andreas
ccid:Cross-Covariance Isolate Detect: a New Change-Point Method for Estimating Dynamic Functional Connectivity
Provides efficient implementation of the Cross-Covariance Isolate Detect (CCID) methodology for the estimation of the number and location of multiple change-points in the second-order (cross-covariance or network) structure of multivariate, possibly high-dimensional time series. The method is motivated by the detection of change points in functional connectivity networks for functional magnetic resonance imaging (fMRI), electroencephalography (EEG), magentoencephalography (MEG) and electrocorticography (ECoG) data. The main routines in the package have been extensively tested on fMRI data. For details on the CCID methodology, please see Anastasiou et al (2020).
Maintained by Andreas Anastasiou. Last updated 4 years ago.
2.70 score 2 scriptscran
SparseTSCGM:Sparse Time Series Chain Graphical Models
Computes sparse vector autoregressive coefficients and precision matrices for time series chain graphical models. Fentaw Abegaz and Ernst Wit (2013) <doi:10.1093/biostatistics/kxt005>.
Maintained by Fentaw Abegaz. Last updated 4 years ago.
2 stars 1.30 score