Crosstalkr: An open-source R package to facilitate drug target identification

Abstract

In the last few decades, interest in graph-based analysis of biological networks has grown substantially. Protein-protein interaction networks are one of the most common biological networks, and represent the molecular relationships between every known protein and every other known protein. Integration of these interactomic data into bioinformatic pipelines may increase the translational potential of discoveries made through analysis of multi-omic datasets. Crosstalkr provides a unified toolkit for drug target and disease subnetwork identification, two of the most common uses of protein protein interaction networks. First, crosstalkr enables users to download and leverage high-quality protein-protein interaction networks from online repositories. Users can then filter these large networks into manageable subnetworks using a variety of methods. For example, network filtration can be done using random walks with restarts, starting at the user-provided seed proteins. Affinity scores from a given random walk with restarts are compared to a bootstrapped null distribution to assess statistical significance. Random walks are implemented using sparse matrix multiplication to facilitate fast execution. Next, users can perform in-silico repression experiments to assess the relative importance of nodes in their network. At this step, users can supply protein or gene expression data to make node rankings more meaningful. The default behavior evaluates the human interactome. However, users can evaluate more than 1000 non-human protein-protein interaction networks as a result of integration with StringDB. It is a free, open-source R package designed to allow users to integrate functional analysis using the protein-protein interaction network into existing bioinformatic pipelines. A beta version of crosstalkr available on CRAN (https://cran.rstudio.com/web/packages/crosstalkr/index.html).

Graph ‘texture’ features as novel metrics that can summarize complex biological graphs

“…In order to identify relevant subnetworks of the human PPI network we used the R package crosstalkr to identify subnetworks based on proteins of interest and their interactions (Weaver and Scott 2023). The crosstalk between a set of proteins of interest was generated based on random walks of length 100 and minimum connectivity score for edges of 1.…”

GraphComm: A Graph-based Deep Learning Method to Predict Cell-Cell Communication in single-cell RNAseq data

“…As single-cell RNAseq is increasingly used to study cell types and states, there is an imminent need for computational methods that can perform prediction of validated ligand-receptor activity with count matrices of gene expression. Methods such as CellPhoneDB 13 , Crosstalkr 14 , Connectome 15 , NicheNet 16 and CellChat 17 , have paved the way for developing cell-cell communication methods that can generate results at both the bulk and single-cell level. These methods have provided interesting results in novel CCC activity within scRNA transcriptomic data.…”