NetZooR

<a href="https://netzoo.github.io/netZooR/"><img src="https://img.shields.io/badge/docs-passing-green"></a>

netZooR is tested on: (OS: Ubuntu + macOS) X (R: release + devel) with Bioconductor release + devel

Description

netZooR is an R package to reconstruct, analyse, and plot biological networks.

Features

netZooR currently integrates:

<details> <summary>PANDA</summary> <b>PANDA</b> (Passing Attributes between Networks for Data Assimilation) <a href="http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0064832">Glass et al. 2013</a>: PANDA is a method for estimating bipartite gene regulatory networks (GRNs) consisting of two types of nodes: transcription factors (TFs) and genes. An edge between TF $i$ and gene $j$ indicates that gene $j$ is regulated by TF $i$. The edge weight represents the strength of evidence for this regulatory relationship obtained by integrating three types of biological data: gene expression data, protein-protein interaction (PPI) data, and transcription factor binding motif (TFBM) data. PANDA is an iterative approach that begins with a seed GRN estimated from TFBMs and uses message passing between data types to refine the seed network to a final GRN that is consistent with the information contained in gene expression, PPI, and TFBM data. </details> <details> <summary>CONDOR</summary> <b>CONDOR</b> (COmplex Network Description Of Regulators) <a href="http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005033">Platig et al. 2016</a>: CONDOR is a tool for community detection in bipartite networks. Many community detection methods for unipartite networks are based on the concept of maximizing a modularity metric that compares the weight of edges within communities to the weight of edges between communities, prioritizing community assignments with higher values of the former relative to the latter. CONDOR extends this concept to bipartite networks by optimizing a bipartite version of modularity defined by <a href="https://pubmed.ncbi.nlm.nih.gov/18233893/">Barber (2007)</a>. To enable bipartite community detection on large networks such gene regulatory networks, CONDOR uses a fast unipartite modularity maximization method on one of the two unipartite projections of the bipartite network. In Platig et al. (2016), CONDOR is applied to bipartite networks of single nucleotide polymorphisms (SNPs) and gene expression, where a network edge from a SNP node to a gene node is indicative of an association between the SNP and the gene expression level, commonly known as an expression quantitative trait locus (eQTL). Communities detected with CONDOR contained local hub nodes ("core SNPs") enriched for association with disease, suggesting that functional eQTL relationships are encoded at the community level. </details> <details> <summary>LIONESS</summary> <b>LIONESS</b> (Linear Interpolation to Obtain Network Estimates for Single Samples) <a href="https://doi.org/10.1016/j.isci.2019.03.021">Kuijjer et al. 2019</a>: LIONESS is a flexible method for single-sample network integration. The machinery behind LIONESS is a leave-one-out approach. To construct a single-sample network for sample $i$, a first network is estimated on the full dataset and a second network is estimated on the dataset with sample $i$ withheld. The single-sample network is then estimated based on the difference between these two networks. Any method that can be used to estimate a network can be used with LIONESS to estimate single-sample networks. Two common use cases are the use of LIONESS to generate single-sample GRNs based on PANDA and the use of LIONESS to generate single-sample Pearson correlation networks. </details> <details> <summary>ALPACA</summary> <b>ALPACA</b> (ALtered Partitions Across Community Architectures) <a href="https://www.nature.com/articles/s41540-018-0052-5">Padi and Quackenbush 2018</a>: ALPACA is a method for differential network analysis that is based on a novel approach to comparison of network community structures. Comparisons of community structure have typically been accomplished by assessing which nodes switch community membership between networks ("community comparison") or by computing the edge weight differences by subtracting the adjacency matrices of two networks and then performing community detection on the resulting differential network ("edge subtraction"). Both these approaches have important limitations. Community comparison is subject to a resolution limit and cannot detect differences smaller than the average community size in a network. Edge subtraction transfers noise from both of the original networks to the differential network, leading to an imprecise estimator. Moreover, positive and negative edge differences cannot be distinguished in the subsequent community detection performed on the differential network.

In contrast to community comparison and edge subtraction, ALPACA compares the community structure of two networks by optimizing a new metric: "differential modularity". In the ALPACA algorithm, one network is defined as the reference network and the second is defined as the perturbed network. The differential modularity metric measures the extent to which edges in a community in the perturbed network differ from those that would be expected by random chance according to a null distribution based on the reference network. Community structure of the perturbed network is determined by maximizing this differential modularity. The resulting communities are "differential modules" that show how the perturbed network differs from the reference network at the community level.

</details> <details> <summary>SAMBAR</summary> <b>SAMBAR</b> (Subtyping Agglomerated Mutations By Annotation Relations) <a href="https://www.nature.com/articles/s41416-018-0109-7">Kuijjer et al.</a>: SAMBAR is a tool for studying cancer subtypes based on patterns of somatic mutations in curated biological pathways. Rather than characterize cancer according to mutations at the gene level, SAMBAR agglomerates mutations within pathways to define a pathway mutation score. To avoid bias based on pathway representation, these pathway mutation scores correct for the number of genes in each pathway as well as the number of times each gene is represented in the universe of pathways. By taking a pathway rather than gene-by-gene lens, SAMBAR both de-sparsifies somatic mutation data and incorporates important prior biological knowledge. Kuijjer et al. (2018) demonstrate that SAMBAR is capable of outperforming other methods for cancer subtyping, producing subtypes with greater between-subtype distances; the authors use SAMBAR for a pan-cancer subtyping analysis that identifies four diverse pan-cancer subtypes linked to distinct molecular processes. </details> <details> <summary>MONSTER</summary> <b>MONSTER</b> (Modeling Network State Transitions from Expression and Regulatory data) <a href="https://doi.org/10.1186/s12918-017-0517-y">Schlauch et al.</a>: MONSTER is a method for estimating transitions between network states by modeling the adjacency matrix of one state as a linear transformation of the adjacency matrix of another. Like LIONESS, MONSTER is a flexible method that does not require a particular type of network structure. MONSTER models the perturbation of an initial network A into a perturbed network B according to a matrix product B = AT. T is a transition matrix encoding the changes that map A to B. When A and B are gene regulatory networks, i.e., bipartite networks between TFs and genes, the MONSTER framework leads naturally to the definition of TF involvement as the sum of the off-diagonal weights for a transcription factor $i$ in the transition matrix T. This perspective enables MONSTER to identify differentially involved TFs that contribute to network transitions differently between different conditions. This dimension cannot be captured from a traditional differential expression analysis of TFs, which will not detect TFs that have the same concentration between conditions. </details> <details> <summary>OTTER</summary> <b>OTTER</b> (Optimization to Estimate Regulation) <a href="https://www.biorxiv.org/content/10.1101/2020.06.23.167999v2.abstract">Weighill et al.</a>: OTTER is a GRN inference method based on the idea that observed biological data (PPI data and gene co-expression data) are projections of a bipartite GRN between TFs and genes. Specifically, PPI data represent the projection of the GRN onto the TF-TF space and gene co-expression data represent the projection of the GRN onto the gene-gene space. OTTER reframes the problem of GRN inference as a problem of relaxed graph matching and finds a GRN that has optimal agreement with the observed PPI and coexpression data. The OTTER objective function is tunable in two ways: first, one can prioritize matching the PPI data or the coexpression da