Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Compressed Perturb-seq enables highly efficient genetic screens

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.

This is a preview of subscription content, access via your institution

Access options

Rent or buy this article

Prices vary by article type



Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Compressed Perturb-seq.


  1. 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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. 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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. 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.

    Article  Google Scholar 

  4. Cleary, B. & Regev, A. The necessity and power of random, under-sampled experiments in biology. Preprint at (2020). This review article presents a general framework for efficient data collection using random experiments.

  5. 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.

Download references

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This is a summary of: Yao, D. et al. Scalable genetic screening for regulatory circuits using compressed Perturb-seq. Nat. Biotechnol. (2023).

Rights and permissions

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Compressed Perturb-seq enables highly efficient genetic screens. Nat Biotechnol (2023).

Download citation

  • Published:

  • DOI:


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing