Efficient Generation of Transcriptomic Profiles by Random Composite Measurements

Brian Cleary; Le Cong; Anthea Cheung; Eric S Lander; Aviv Regev

doi:10.1016/j.cell.2017.10.023

Efficient Generation of Transcriptomic Profiles by Random Composite Measurements

Cell. 2017 Nov 30;171(6):1424-1436.e18. doi: 10.1016/j.cell.2017.10.023. Epub 2017 Nov 16.

Authors

Brian Cleary¹, Le Cong², Anthea Cheung², Eric S Lander³, Aviv Regev⁴

Affiliations

¹ Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Computational and Systems Biology Program, MIT, Cambridge, MA, USA.
² Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
³ Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA; Department of Systems Biology, Harvard Medical School, Boston, MA, USA.
⁴ Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA; Howard Hughes Medical Institute, Chevy Chase, MD, USA. Electronic address: aregev@broadinstitute.org.

Abstract

RNA profiles are an informative phenotype of cellular and tissue states but can be costly to generate at massive scale. Here, we describe how gene expression levels can be efficiently acquired with random composite measurements-in which abundances are combined in a random weighted sum. We show (1) that the similarity between pairs of expression profiles can be approximated with very few composite measurements; (2) that by leveraging sparse, modular representations of gene expression, we can use random composite measurements to recover high-dimensional gene expression levels (with 100 times fewer measurements than genes); and (3) that it is possible to blindly recover gene expression from composite measurements, even without access to training data. Our results suggest new compressive modalities as a foundation for massive scaling in high-throughput measurements and new insights into the interpretation of high-dimensional data.

Keywords: compressed sensing; gene expression; random composite measurements.

MeSH terms

Algorithms*
Data Compression
Gene Expression Profiling / methods*
High-Throughput Nucleotide Sequencing / methods
Humans
K562 Cells
Sequence Analysis, RNA / methods

Abstract

MeSH terms

Grants and funding