Pelucchi, P. | bioRxiv Science

bioRxiv2023

Cross-talk quantification in molecular networks with application to pathway-pathway and cell-cell interactions.

Disease phenotypes can be described as the consequence of interactions among molecular processes that are altered beyond resilience. Here, we address the challenge of assessing the possible alteration of intra- and inter-cellular molecular interactions among gene sets, which are intended to represent processes and or cellular phenotypes. We present an approach, designated as "Ulisse", which complements the existing methods of enrichment analysis and cell-cell communication analysis. It can be applied to a gene list as well as multiple ranked gene lists, typically derived in the context of omics or multi-omics studies. The approach highlights the presence of alterations in those components that control the interactions between processes or cells. Crosstalk quantification is supported by two null models. Further, the approach provides an additional way of identifying the genes associated with the phenotype. As a proof-of-concept, we applied Ulisse to study the alteration of pathway crosstalks and cell-cell communications in triple negative breast cancer samples, based on single-cell RNA sequencing. In conclusion, our work supports the usefulness of crosstalk analysis as an additional instrument in the "toolkit" of biomedical research for translating complex biological data into actionable insights.

bioinformatics↗

bioRxiv2022

scMuffin: an R package for resolving solid tumor heterogeneity from single-cell expression data

INTRODUCTIONSingle-cell (SC) gene expression analysis is crucial to dissect the complex cellular heterogeneity of solid tumors, which is one of the main obstacles for the development of effective cancer treatments. Such tumors typically contain a mixture of cells with aberrant genomic and transcriptomic profiles affecting specific sub-populations that might have a pivotal role in cancer progression, whose identification eludes bulk RNA-sequencing approaches. We presentscMuffin, an R package that enables the characterization of cell identity in solid tumors on the basis of a various and complementary analyses on SC gene expression data. RESULTSscMuffin provides a series of functions to calculate qualitative and quantitative scores, such as: expression of marker sets for normal and tumor conditions, pathway activity, cell state trajectories, CNVs, transcriptional complexity and proliferation state. Thus, scMuffin facilitates the combination of various evidences that can be used to distinguish normal and tumoral cells, define cell identities, cluster cells in different ways, link genomic aberrations to phenotypes and identify subtle differences between cell subtypes or cell states. We analysed public SC expression datasets of human high-grade gliomas as a proof-of-concept to show the value of scMuffin and illustrate its user interface. Nevertheless, these analyses lead to interesting findings, which suggest that some chromosomal amplifications might underlie the invasive tumor phenotype and the presence of cells that possess tumor initiating cells characteristics. CONCLUSIONSThe analyses offered by scMuffin and the results achieved in the case study show that our tool helps addressing the main challenges in the bioinformatics analysis of SC expression data from solid tumors.

bioinformatics↗