Using an experimental and computational framework inspired by compressed sensing, we greatly reduced the number of measurements needed to run Perturb-seq. Our compressed Perturb-seq strategy relies on collecting measurements comprising random linear combinations of genetic perturbations, followed by deconvolving the perturbation effects on the transcriptome using sparsity-exploiting algorithms.
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References
Dixit, A. et al. Perturb-seq: dissecting molecular circuits with scalable single-cell RNA profiling of pooled genetic screens. Cell 167, 1853–1866 (2016). This was one of the initial papers that developed Perturb-seq.
Replogle, J. M. et al. Mapping information-rich genotype-phenotype landscapes with genome-scale Perturb-seq. Cell 185, 2559–2575 (2022). This paper represents the largest Perturb-seq screen conducted to date.
Candes, E. J. & Wakin, M. B. An introduction to compressive sampling. IEEE Signal Process. Mag. 25, 21–30 (2008). This review article presents an overview of compressed sensing.
Cleary, B. & Regev, A. The necessity and power of random, under-sampled experiments in biology. Preprint at https://doi.org/10.48550/arXiv.2012.12961 (2020). This review article presents a general framework for efficient data collection using random experiments.
Sharan, V. et al. Compressed factorization: fast and accurate low-rank factorization of compressively-sensed data. Proc. 36th Int. Conf. Machine Learning 5690–5700 (2019). This paper provides the theoretical basis for the computational method introduced in our paper.
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This is a summary of: Yao, D. et al. Scalable genetic screening for regulatory circuits using compressed Perturb-seq. Nat. Biotechnol. https://doi.org/10.1038/s41587-023-01964-9 (2023).
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Compressed Perturb-seq enables highly efficient genetic screens. Nat Biotechnol (2023). https://doi.org/10.1038/s41587-023-02003-3
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DOI: https://doi.org/10.1038/s41587-023-02003-3
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