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

Project description

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:, or visit our GitLab repository: `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.

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