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 (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.

For further information, please visit https://gitlab.com/cvejic-group/smash

We’re always happy to hear of any suggestions, issues, bug reports, and possible ideas for collaboration. <ul> <li>Mike Nelson <nelson@ebi.ac.uk> (University of Cambridge, and EMBL-EBI)</li> <li>Simone Riva <sr31@sanger.ac.uk> (University of Cambridge, and Wellcome Sanger Institute) </li> </ul>

## Project details

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