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Cell DOI:10.1016/j.cell.2017.10.023

Efficient Generation of Transcriptomic Profiles by Random Composite Measurements.

Publication TypeJournal Article
Year of Publication2017
AuthorsCleary, B, Cong, L, Cheung, A, Lander, ES, Regev, A
JournalCell
Volume171
Issue6
Pages1424-1436.e18
Date Published2017 Nov 30
ISSN1097-4172
KeywordsAlgorithms, Data Compression, Gene Expression Profiling, High-Throughput Nucleotide Sequencing, Humans, K562 Cells, Sequence Analysis, RNA
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.

DOI10.1016/j.cell.2017.10.023
Pubmed

http://www.ncbi.nlm.nih.gov/pubmed/29153835?dopt=Abstract

Alternate JournalCell
PubMed ID29153835
PubMed Central IDPMC5726792
Grant List / / Howard Hughes Medical Institute / United States
RM1 HG006193 / HG / NHGRI NIH HHS / United States
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