Efficient Generation of Transcriptomic Profiles by Random Composite Measurements
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) t...
| Main Authors: | , , , , |
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| Format: | Article |
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Elsevier
2018
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| Online Access: | http://hdl.handle.net/1721.1/119820 https://orcid.org/0000-0001-8567-2049 |
| _version_ | 1826209569481687040 |
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| author | Cleary, Brian Cong, Le Cheung, Anthea Lander, Eric Steven Regev, Aviv |
| author2 | Massachusetts Institute of Technology. Department of Biology |
| author_facet | Massachusetts Institute of Technology. Department of Biology Cleary, Brian Cong, Le Cheung, Anthea Lander, Eric Steven Regev, Aviv |
| author_sort | Cleary, Brian |
| collection | MIT |
| description | 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. A roadmap to generate a high-dimensional transcriptomic profile from sequencing a small, random selection of genes. Keywords: gene expression; compressed sensing; random composite measurements |
| first_indexed | 2024-09-23T14:24:33Z |
| format | Article |
| id | mit-1721.1/119820 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T14:24:33Z |
| publishDate | 2018 |
| publisher | Elsevier |
| record_format | dspace |
| spelling | mit-1721.1/1198202022-10-01T21:11:55Z Efficient Generation of Transcriptomic Profiles by Random Composite Measurements Cleary, Brian Cong, Le Cheung, Anthea Lander, Eric Steven Regev, Aviv Massachusetts Institute of Technology. Department of Biology Lander, Eric Steven Regev, Aviv 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. A roadmap to generate a high-dimensional transcriptomic profile from sequencing a small, random selection of genes. Keywords: gene expression; compressed sensing; random composite measurements National Human Genome Research Institute (U.S.) (Grant RM1HG006193) 2018-12-21T15:16:11Z 2018-12-21T15:16:11Z 2017-11 2017-08 2018-12-19T15:43:07Z Article http://purl.org/eprint/type/JournalArticle 0092-8674 1097-4172 http://hdl.handle.net/1721.1/119820 Cleary, Brian et al. “Efficient Generation of Transcriptomic Profiles by Random Composite Measurements.” Cell 171, 6 (November 2017): 1424–1436 © 2017 Elsevier Inc https://orcid.org/0000-0001-8567-2049 http://dx.doi.org/10.1016/J.CELL.2017.10.023 Cell Creative Commons Attribution-NonCommercial-NoDerivs License http://creativecommons.org/licenses/by-nc-nd/4.0/ application/pdf Elsevier PMC |
| spellingShingle | Cleary, Brian Cong, Le Cheung, Anthea Lander, Eric Steven Regev, Aviv Efficient Generation of Transcriptomic Profiles by Random Composite Measurements |
| title | Efficient Generation of Transcriptomic Profiles by Random Composite Measurements |
| title_full | Efficient Generation of Transcriptomic Profiles by Random Composite Measurements |
| title_fullStr | Efficient Generation of Transcriptomic Profiles by Random Composite Measurements |
| title_full_unstemmed | Efficient Generation of Transcriptomic Profiles by Random Composite Measurements |
| title_short | Efficient Generation of Transcriptomic Profiles by Random Composite Measurements |
| title_sort | efficient generation of transcriptomic profiles by random composite measurements |
| url | http://hdl.handle.net/1721.1/119820 https://orcid.org/0000-0001-8567-2049 |
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