2020/12/23 by Brian Cleary, Aviv Regev, Cleary, Brian +1 · 1 citation
Biochemistry, Genetics and Molecular Biology · #Advanced Fluorescence Microscopy Techniques #Applications (stat.AP) #Bioinformatics and Genomic Networks #FOS: Biological sciences #FOS: Computer and information sciences #Gene Regulatory Network Analysis #Quantitative Methods (q-bio.QM)
paper · pdf · doi:10.48550/arxiv.2012.12961
openalex publication_date 2020/12/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
A vast array of transformative technologies developed over the past decade has enabled measurement and perturbation at ever increasing scale, yet our understanding of many systems remains limited by experimental capacity. Overcoming this limitation is not simply a matter of reducing costs with existing approaches; for complex biological systems it will likely never be possible to comprehensively measure and perturb every combination of variables of interest. There is, however, a growing body of work - much of it foundational and precedent setting - that extracts a surprising amount of information from highly under sampled data. For a wide array of biological questions, especially the study of genetic interactions, approaches like these will be crucial to obtain a comprehensive understanding. Yet, there is no coherent framework that unifies these methods, provides a rigorous mathematical foundation to understand their limitations and capabilities, allows us to understand through a common lens their surprising successes, and suggests how we might crystalize the key concepts to transform experimental biology. Here, we review prior work on this topic - both the biology and the mathematical foundations of randomization and low dimensional inference - and propose a general framework to make data collection in a wide array of studies vastly more efficient using random experiments and composite experiments.