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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References
Kolodziejczyk, A. A. & Kim, J. K. The technology and biology of single-cell RNA sequencing. Mol. Cell 58, 610–620 (2015). This paper reports the basic concept and technology of scRNA-seq.
Riemondy, K. A. & Ransom, M. Recovery and analysis of transcriptome subsets from pooled single-cell RNA-seq libraries. Nucleic Acids Res. 47, e20–e20 (2019). This paper reports the importance of selecting informative genes to deal with the bottleneck of sequencing.
Felder, R. M. & Brent, R. Active learning: An introduction. ASQ Higher Educ. Brief. 2, 1–5 (2009). A Review article that presents traditional active learning.
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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
