smashpy 0.1.2

The `SMaSH` (Scalable Marker gene Signal Hunter) framework is a general, scalable codebase for calculating marker genes from single-cell RNA-sequencing data for a variety of different cell annotations as provided by the user, using supervised machine learning approaches. These annotations can be truly general: they can be broad cell types/clusters, detailed sub-types of different broad clusters, cell organ of origin, whether the cell inhabits tumour tissue, surrounding microenvironment, or healthy tissue, and more besides. `SMaSH` implements marker gene extraction using four different models (Random Forest, Balanced Random Forest, XGBoost, and a deep neural network) and two different information gain metrics (Gini impurity for the ensemble learners, and Shapley value for the neural network). For some details on the `SMaSH` implementation please consult our pre-print: https://www.biorxiv.org/content/10.1101/2021.04.08.438978v1, or visit our GitLab repository: https://gitlab.com/cvejic-group/smash. `SMaSH` is integrated with the `ScanPy` framework, working directly from the `AnnData` object of RNA-sequencing counts and a vector of user-defined annotations for each cell according to the marker gene extraction problem.

We’re always happy to hear of any suggestions, issues, bug reports, and possible ideas for collaboration.

• Simone Riva <simo.riva15@gmail.com>, <sgr34@cam.ac.uk>, <sr31@sanger.ac.uk> (University of Cambridge, and Wellcome Sanger Institute)

• Mike Nelson <nelson@ebi.ac.uk> (University of Cambridge, and EMBL-EBI)