RNAchat: Integrating machine learning algorithms to identify metapathways based on clinical and multi-omics data

BackgroundUnderstanding the global interactions among cellular pathways and types is essential for unraveling the complex biological mechanisms that underlie various diseases. While many existing studies have primarily focused on omics data, they often fail to integrate clinical heterogeneity, which is crucial for a comprehensive understanding of disease biology. Although significant progress has been made in researching crosstalks between inter-pathways and inter-cells, and tools like CellChat have emerged to analyze these interactions, they have notable limitations. These include the assumption that mRNA expression directly reflects protein activity, a heavy reliance on pre-established ligand-receptor interactions, and an inability to connect "chats" with clinical phenotypes. Furthermore, existing methods have limitations in distinguishing between paracrine signaling (interactions between different cell types) and autocrine signaling (self-signaling within the same cell type). These limitations hinder the ability to translate molecular insights into phenotypic outcomes, thereby limiting clinical applicability. To provide another solution for these challenges, we introduce RNAchat, a novel approach that utilizes machine learning techniques to identify metapathways for crosstalks among pathways at not only the bulk level but also cell types at the single-cell level, incorporating both clinical and multi-omics data. ResultsRNAchat provides another solution by providing an interactive, reproducible platform designed to analyse metapathways between inter-pathways and inter-cell types using multi-omics level data in a clinical context. This tool enables researchers to integrate diverse datasets, conduct exploratory analyses, and identify cooperated pathways. The platform facilitates hypothesis generation and produces intuitive visualizations to support further investigation. As a proof of concept, we applied RNAchat to connect omics data to pain, drug resistance in rheumatoid arthritis (RA), disease severity to RA and Heart Failure (HF). We discovered cooperated molecular pathways associated with different treatments using SHAP (Shapley Additive Explanations) values. Then, we demonstrated how to use it to find cellular interactions at single cell level. Last but not the least, we further extended crosstalk exploration between host and parasites and found potential novel important pathway interaction not mentioned by previous studies. ConclusionWe introduce RNAchat, a computational platform designed to identify pathway communications with clinical and multi-omics data in real-time. This tool supports both user-generated and publicly available datasets, offering a robust solution and enhancing our understanding of complex diseases such as RA and HF. The platform is available at https://github.com/tangmingcan/RNAchat.