Skip to contents

scMuffin - A MUlti-Features INtegrative approach for SC data analysis

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. scMuffin is 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, such as:

  • gene set expression (cell-level and cluster level);
  • CNV inferece;
  • transcriptional complexity;
  • cell state trajectories;
  • proliferation state;
  • cluster enrichment assessment (quantitative and categorical values).

Typical applications of scMuffin include:

  • characterize the expression of cells and clusters;
  • distinguish normal and tumoral cells;
  • associate clusters with phenotypes;
  • define cell identities;
  • cluster cells in different ways;
  • link genomic aberrations to phenotypes;
  • identify subtle differences between cell subtypes or cell states;
  • compare datasets based on gene set expression.

Documentation: https://emosca-cnr.github.io/scMuffin

Source code: https://github.com/emosca-cnr/scMuffin

Citation: https://doi.org/10.1186/s12859-023-05563-y

Contacts: