Résumé
Cell-cell communications are widely explored to understand tissue homeostasis and diseases. Numerous computational tools have been developed to infer cellular interactions from transcriptomic or proteomic expression data. However, proteins often carry post-translational modifications (PTMs) that can induce conformational switches and alter their functional properties. A key challenge remains to incorporate PTM data in the inference and analysis of cellular interactions. Here, we propose an extension of our previously published tool BulkSignalR to integrate PTM information in ligand-receptor interactions and downstream pathways predictions. This new functionality is compatible with bulk and single-cell data, and it supports all types of PTMs. Based on two illustrative datasets, we show that this new feature provides deeper insights into biological pathway regulation, and that PTM integration helps reducing false positive results occasionally produced by standard approaches.
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•Extending BulkSignalR to integrate PTMs into ligand–receptor interaction inference•PTM integration improves accuracy and confidence in pathway activation prediction•Application to two cancer datasets reveals context-specific signaling•Framework supports all types of PTMs