SMaSH: A scalable, general marker gene identification framework for single-cell RNA sequencing and Spatial Transcriptomics
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.
Release history Release notifications | RSS feed
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.