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SMaSH: A scalable, general marker gene identification framework for single-cell RNA sequencing and Spatial Transcriptomics

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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 (see Figure below) please consult our pre-print: [COMING SOON]. `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.

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We’re always happy to hear of any suggestions, issues, bug reports, and possible ideas for collaboration. - Mike Nelson <> (University of Cambridge, and EMBL-EBI)- Simone Riva <> (University of Cambridge, and Wellcome Sanger Institute)

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