Inspired by active learning approaches, we have developed a computational method that selects minimal gene sets capable of reliably identifying cell-types and transcriptional states in large sets of single-cell RNA-sequencing data. As the procedure focuses computational resources on poorly classified cells, active support vector machine (ActiveSVM) scales to data sets with over one million cells.
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This is a summary of: Chen, X. et al. Minimal gene set discovery in single-cell mRNA-seq datasets with ActiveSVM. Nat. Comput. Sci. https://doi.org/10.1038/s43588-022-00263-8 (2022)
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ActiveSVM selects minimal gene sets from gene expression data. Nat Comput Sci 2, 420–421 (2022). https://doi.org/10.1038/s43588-022-00267-4
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DOI: https://doi.org/10.1038/s43588-022-00267-4
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