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

INTRODUCTION Single-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 present scMuffin, an R package that enables the characterization of cell identity in solid tumors on the basis of multiple and complementary criteria applied on SC gene expression data. RESULTS scMuffin provides a series of functions to calculate several different qualitative and quantitative scores, such as: expression of marker sets for normal and tumor conditions, pathway activity, cell state trajectories, CNVs, chromatin state 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. As a proof-of-concept, we applied scMuffin to a public SC expression dataset of human high-grade gliomas, where we found that some chromosomal amplifications might underlie the invasive tumor phenotype and identified rare quiescent cells that may deserve further investigations as candidate cancer stem cells. CONCLUSIONS The 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.