BLOBFISH Implementation

DESCRIPTION

@@ -50,6 +50,7 @@ Imports:
     GO.db,
     org.Hs.eg.db,
     Matrix,
+    matrixTests,
     gplots,
     nnet,
     data.table,
@@ -95,6 +96,6 @@ Suggests:
     dorothea
 VignetteEngine: knitr
 VignetteBuilder: knitr
-RoxygenNote: 7.2.3
+RoxygenNote: 7.3.1
 BugReports: https://github.com/netZoo/netZooR/issues
 URL: https://github.com/netZoo/netZooR, https://netzoo.github.io/

NAMESPACE

@@ -1,5 +1,9 @@
 # Generated by roxygen2: do not edit by hand
 
+export(CalculatePValues)
+export(GenerateNullPANDADistribution)
+export(PlotNetwork)
+export(RunBLOBFISH)
 export(adj2el)
 export(adj2regulon)
 export(alpaca)

README.md

@@ -94,6 +94,11 @@ netZooR currently integrates:
 <b>COBRA</b> (Co-expression Batch Reduction Adjustment) Micheletti, Schlauch et al. is method for correction of high-order batch effects such as those that persist in co-expression networks. Batch effects and other covariates are known to induce spurious associations in co-expression networks and confound differential gene expression analyses. These effects are corrected for using various methods prior to downstream analyses such as the inference of co-expression networks and computing differences between them. In differential co-expression analysis, the pairwise joint distribution of genes is considered rather than independently analyzing the distribution of expression levels for each individual gene. Computing co-expression matrices after standard batch correction on gene expression data is not sufficient to account for the possibility of batch-induced changes in the correlation between genes as existing batch correction methods act solely on the marginal distribution of each gene. Consequently, uncorrected, artifactual differential co-expression can skew the correlation structure such that network-based methods that use gene co-expression can produce false, nonbiological associations even using data corrected using standard batch correction. Co-expression Batch Reduction Adjustment (COBRA) addresses this question by computing a batch-corrected gene co-expression matrix based on estimating a conditional covariance matrix. COBRA estimates a reduced set of parameters that express the co-expression matrix as a function of the sample covariates and can be used to control for continuous and categorical covariates. The method is computationally fast and makes use of the inherently modular structure of genomic data to estimate accurate gene regulatory associations and enable functional analysis for high-dimensional genomic data.
 </details>
 
+<details>
+<summary>BLOBFISH</summary>
+<b>BLOBFISH</b> (Bipartite Limited Subnetworks from Multiple Observations using Breadth-First Search with Constrained Hops) Eicher et al. is a method to obtain a subnetwork connecting nodes of interest across observation-specific biological networks. Many biological networks are bipartite, such as expression quantitative trait loci (eQTL) networks, gene regulatory networks, and multi-omic partial correlation networks. However, the size of omics-scale bipartite networks can make them difficult to interpret as a whole; motivating the development of tools that evaluate connectivity between a subset of nodes. In addition, observation-specific networks (i.e., sample-specific or subject-specific networks) introduce the possibility of subsetting robust edges that are consistent across observations. BLOBFISH evaluates connectivity between a subset of nodes in a set of observation-specific bipartite networks by first finding significant edges across observations in comparison to a null distribution, and then using a breadth-first-search to uncover paths between seed nodes limited to a prespecified number of hops.
+</details>
+
 * Source protein-protein interaction network from [STRINGdb](https://string-db.org/) based on a list of protein of interest.
 
 * Plot one PANDA network in [Cytoscape](https://cytoscape.org/).