Showing 4 of total 4 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 14 days ago.
networkinferencenetworkgeneregulationgeneexpressiontranscriptionmicroarraygraphandnetworkgene-regulatory-networktranscription-factors
105 stars 7.98 score
cailab-tamu
scTenifoldKnk:In-Silico Knockout Experiments from Single-Cell Gene Regulatory Networks
A workflow based on 'scTenifoldNet' to perform in-silico knockout experiments using single-cell RNA sequencing (scRNA-seq) data from wild-type (WT) control samples as input. First, the package constructs a single-cell gene regulatory network (scGRN) and knocks out a target gene from the adjacency matrix of the WT scGRN by setting the gene’s outdegree edges to zero. Then, it compares the knocked out scGRN with the WT scGRN to identify differentially regulated genes, called virtual-knockout perturbed genes, which are used to assess the impact of the gene knockout and reveal the gene’s function in the analyzed cells.
Maintained by Daniel Osorio. Last updated 3 months ago.
functional-genomicsgene-functiongene-knockoutgene-regulatory-networkvirtual-knockout-experiments
44 stars 4.86 score 11 scriptsbioc
magrene:Motif Analysis In Gene Regulatory Networks
magrene allows the identification and analysis of graph motifs in (duplicated) gene regulatory networks (GRNs), including lambda, V, PPI V, delta, and bifan motifs. GRNs can be tested for motif enrichment by comparing motif frequencies to a null distribution generated from degree-preserving simulated GRNs. Motif frequencies can be analyzed in the context of gene duplications to explore the impact of small-scale and whole-genome duplications on gene regulatory networks. Finally, users can calculate interaction similarity for gene pairs based on the Sorensen-Dice similarity index.
Maintained by Fabrício Almeida-Silva. Last updated 5 months ago.
softwaremotifdiscoverynetworkenrichmentsystemsbiologygraphandnetworkgene-regulatory-networkmotif-analysisnetwork-motifsnetwork-science
1 stars 4.00 score 2 scripts
evanamartin
baycn:Bayesian Inference for Causal Networks
A Bayesian hybrid approach for inferring Directed Acyclic Graphs (DAGs) for continuous, discrete, and mixed data. The algorithm can use the graph inferred by another more efficient graph inference method as input; the input graph may contain false edges or undirected edges but can help reduce the search space to a more manageable size. A Bayesian Markov chain Monte Carlo algorithm is then used to infer the probability of direction and absence for the edges in the network. References: Martin and Fu (2019) <arXiv:1909.10678>.
Maintained by Evan A Martin. Last updated 5 years ago.
directed-acyclic-graphgene-regulatory-network
3 stars 3.18 score 1 scripts