Abstract
Adjacency matrices are useful for storing pairwise interaction data, such as correlations between gene pairs in a pathway or similarities between genes and conditions. The aMatReader app enables users to import one or multiple adjacency matrix files into Cytoscape, where each file represents an edge attribute in a network. Our goal was to import the diverse adjacency matrix formats produced by existing scripts and libraries written in R, MATLAB, and Python, and facilitate importing that data into Cytoscape. To accelerate the import process, aMatReader attempts to predict matrix import parameters by analyzing the first two lines of the file. We also exposed CyREST endpoints to allow researchers to import network matrix data directly into Cytoscape from their language of choice. Many analysis tools deal with networks in the form of an adjacency matrix, and exposing the aMatReader API to automation users enables scripts to transfer those networks directly into Cytoscape with little effort.
Highlights
Adjacency matrices are a strong choice for storing pairwise element interaction data, such as those commonly produced by biological analysis tools to represent a weighted network of relationships between biological components. aMatReader facilitates importing general adjacency matrices into edge attributes of Cytoscape networks. aMatReader aims to enable users to compile Cytoscape networks from one or multiple matrix files by creating edges or edge attributes for nonzero values in the matrix
The Operation section describes how to call the endpoint as a Cytoscape Automation Function
Implementation aMatReader is developed to handle a wide spectrum of adjacency matrix formats
Summary
Adjacency matrices are a strong choice for storing pairwise element interaction data, such as those commonly produced by biological analysis tools to represent a weighted network of relationships between biological components (such as genes, conditions, pathways, times, etc.). aMatReader facilitates importing general adjacency matrices (such as correlation, similarity, and difference data) into edge attributes of Cytoscape networks. aMatReader aims to enable users to compile Cytoscape networks from one or multiple matrix files by creating edges or edge attributes for nonzero values in the matrix.We upgraded the original aMatReader[1] to enable Cytoscape Automation[2] by exposing two new REST endpoints[3,4], bridging the gap between network matrix data in automation scripts and Cytoscape. Adjacency matrices are a strong choice for storing pairwise element interaction data, such as those commonly produced by biological analysis tools to represent a weighted network of relationships between biological components (such as genes, conditions, pathways, times, etc.). AMatReader facilitates importing general adjacency matrices (such as correlation, similarity, and difference data) into edge attributes of Cytoscape networks. We upgraded the original aMatReader[1] to enable Cytoscape Automation[2] by exposing two new REST endpoints[3,4], bridging the gap between network matrix data in automation scripts and Cytoscape. With Cytoscape Automation, biologists can manipulate Cytoscape networks via REST calls and create complex workflows in their language of choice (e.g. Python and R). The Implementation section describes the general approach of aMatReader and its REST endpoints. The Use Case section demonstrates how to import adjacency matrices into Cytoscape via the aMatReader endpoint, and the Discussion section describes the import performance
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