Brian Cleary

Current Research

Our group works at the interface of the limits of algorithmic learning and the limits of biological experimentation in pursuit of the organizing principles of molecular, cellular and tissue processes.

The AlgoBioLab combines theory, computation, and wet-lab experimentation to understand basic principles of biological organization. We study molecular and genetic organization at the level of cells and tissues; how evolution shapes organization in high dimensional, complex living systems; and how biological organization interacts with fundamental principles of statistics to determine the limits of experimentation. Most of our work is motivated by curiosity and a fondness for deep thinking, but we also do a fair amount of computational and experimental method development when it suits us.

Selected Publications

• Cleary, B., Simonton, B., Bezney, J., Murray, E., Alam, S., Sinha, A., Habibi, E., Marshall, J., Lander, E.S., Chen, F. and Regev, A., 2021. Compressed sensing for highly efficient imaging transcriptomics. Nature Biotechnology, 39(8), pp. 936-942.
• Cleary, B., Cong, L., Cheung, A., Lander, E.S. and Regev, A., 2017. Efficient generation of transcriptomic profiles by random composite measurements. Cell, 171(6), pp. 1424-1436.
• Cleary, B. and Regev, A., 2020. The necessity and power of random, under-sampled experiments in biology. arXiv preprint arXiv:2012.12961.
• Schiebinger, G., Shu, J., Tabaka, M., Cleary, B., Subramanian, V., Solomon, A., Gould, J., Liu, S., Lin, S., Berube, P. and Lee, L., 2019. Optimal-transport analysis of single-cell gene expression identifies developmental trajectories in reprogramming. Cell, 176(4), pp. 928-943.
• Einav, T. and Cleary, B., 2022. Extrapolating missing antibody-virus measurements across serological studies. Cell Systems, 13(7), pp. 561-573.
• Hong, D., Dey, R., Lin, X., Cleary, B. and Dobriban, E., 2022. Group testing via hypergraph factorization applied to COVID-19. Nature Communications, 13(1), pp. 1-13.
• Cleary, B., Hay, J.A., Blumenstiel, B., Harden, M., Cipicchio, M., Bezney, J., Simonton, B., Hong, D., Senghore, M., Sesay, A.K. and Gabriel, S., 2021. Using viral load and epidemic dynamics to optimize pooled testing in resource-constrained settings. Science translational medicine, 13(589), p. eabf1568.
• Cleary, B., Brito, I.L., Huang, K., Gevers, D., Shea, T., Young, S. and Alm, E.J., 2015. Detection of low-abundance bacterial strains in metagenomic datasets by eigengenome partitioning. Nature biotechnology, 33(10), pp. 1053-1060.