Efficient Generation of Transcriptomic Profiles by Random Composite Measurements
Open accessPublished version of a preprint
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.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
A subset of expression measurements may be enough to infer many transcriptional states accurately.
“many transcriptional states could be faithfully inferred from a subset of gene expression measurements32,33”
From Combinatorial single-cell CRISPR screens by direct guide RNA capture and targeted sequencing (2020) · cites this paper as [32]
The approach is complementary to efforts based on composite measurements, compressed sensing, and rich readouts to predict unmeasured genetic interactions.
“This provides a complementary approach to efforts based on composite measurements, compressed sensing, and rich readouts to predict unmeasured GIs (7, 28).”
From Exploring genetic interaction manifolds constructed from rich single-cell phenotypes (2019) · cites this paper as [28]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Scikit-learn: Machine Learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Müller, A., Nothman, J., Louppe, G., et al. (2012) · arXiv · doi:10.48550/arxiv.1201.0490 · Cited by 71,016 · see citing works
Donoho, D. L. (2006) · IEEE Transactions on Information Theory · doi:10.1109/tit.2006.871582 · Cited by 23,994 · see citing works
Donoho, D. L. (2004) · Cited by 17,049 · see citing works
Candes, E. J., Romberg, J., & Tao, T. (2006) · IEEE Transactions on Information Theory · doi:10.1109/tit.2005.862083 · Cited by 15,639 · see citing works
A Global Geometric Framework for Nonlinear Dimensionality Reduction
Tenenbaum, J. B., Silva, V. D., & Langford, J. C. (2000) · Science · doi:10.1126/science.290.5500.2319 · Cited by 13,585 · see citing works
Heinz, S., Benner, C., Spann, N., Bertolino, E., Lin, Y. C., Laslo, P., Cheng, J. X., Murre, C., Singh, H., & Glass, C. K. (2010) · Molecular Cell · doi:10.1016/j.molcel.2010.05.004 · Cited by 13,382 · see citing works
An Introduction To Compressive Sampling
Candes, E. J., & Wakin, M. B. (2008) · IEEE Signal Processing Magazine · doi:10.1109/msp.2007.914731 · Cited by 10,017 · see citing works
The cancer genome atlas pan-cancer analysis project
The Cancer Genome Atlas Research Network, Weinstein, J. N., Collisson, E. A., Mills, G. B., Shaw, K. R. M., Ozenberger, B. A., Ellrott, K., Shmulevich, I., Sander, C., & Stuart, J. M. (2013) · Nature Genetics · doi:10.1038/ng.2764 · Cited by 9,092 · see citing works
Trapnell, C., Cacchiarelli, D., Grimsby, J., Pokharel, P., Li, S., Morse, M., Lennon, N. J., Livak, K. J., Mikkelsen, T. S., & Rinn, J. L. (2014) · Nature Biotechnology · doi:10.1038/nbt.2859 · Cited by 7,314 · see citing works
Molecular signatures database (MSigDB) 3.0
Liberzon, A., Subramanian, A., Pinchback, R., Thorvaldsdóttir, H., Tamayo, P., & Mesirov, J. P. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btr260 · Cited by 7,111 · see citing works
Spatial reconstruction of single-cell gene expression data
Satija, R., Farrell, J. A., Gennert, D., Schier, A. F., & Regev, A. (2015) · Nature Biotechnology · doi:10.1038/nbt.3192 · Cited by 7,055 · see citing works
Self-citationDecoding by Linear Programming
Candès, E. J., & Tao, T. (2005) · IEEE Transactions on Information Theory · doi:10.1109/tit.2005.858979 · Cited by 6,987 · see citing works
Integrative analysis of 111 reference human epigenomes
Roadmap Epigenomics Consortium, Kundaje, A., Meuleman, W., Ernst, J., Bilenky, M., Yen, A., Heravi-Moussavi, A., Kheradpour, P., Zhang, Z., Wang, J., et al. (2015) · Nature · doi:10.1038/nature14248 · Cited by 6,368 · see citing works
Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma
Patel, A. P., Tirosh, I., Trombetta, J. J., Shalek, A. K., Gillespie, S. M., Wakimoto, H., Cahill, D. P., Nahed, B. V., Curry, W. T., Martuza, R. L., et al. (2014) · Science · doi:10.1126/science.1254257 · Cited by 5,063 · see citing works
Self-citationDissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq
Tirosh, I., Izar, B., Prakadan, S. M., Wadsworth, M. H., Treacy, D., Trombetta, J. J., Rotem, A., Rodman, C., Lian, C., Murphy, G., et al. (2016) · Science · doi:10.1126/science.aad0501 · Cited by 4,944 · see citing works
Self-citationApproximate nearest neighbors: towards removing the curse of dimensionality
Indyk, P., & Motwani, R. (1998) · Proceedings of the thirtieth annual ACM symposium on Theory of computing - STOC '98 · doi:10.1145/276698.276876 · Cited by 4,188 · see citing works
A Next Generation Connectivity Map: L1000 Platform and the First 1,000,000 Profiles
Subramanian, A., Narayan, R., Corsello, S. M., Peck, D. D., Natoli, T. E., Lu, X., Gould, J., Davis, J. F., Tubelli, A. A., Asiedu, J. K., et al. (2017) · Cell · doi:10.1016/j.cell.2017.10.049 · Cited by 3,515 · see citing works
A Next Generation Connectivity Map: L1000 platform and the first 1,000,000 profilesPreprint
Subramanian, A., Narayan, R., Corsello, S. M., et al. (2017) · bioRxiv · doi:10.1101/136168 · Cited by 3,129 · see citing works
Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq
Zeisel, A., Muñoz-Manchado, A. B., Codeluppi, S., Lönnerberg, P., La Manno, G., Juréus, A., Marques, S., Munguba, H., He, L., Betsholtz, C., et al. (2015) · Science · doi:10.1126/science.aaa1934 · Cited by 3,124 · see citing works
Extensions of Lipschitz mappings into a Hilbert space
Johnson, W. B., & Lindenstrauss, J. (1984) · Contemporary mathematics - American Mathematical Society · doi:10.1090/conm/026/737400 · Cited by 2,583 · see citing works
Knowledge-based analysis of microarray gene expression data by using support vector machines
Brown, M. P. S., Noble, W. S., Lin, D. M., Cristianini, N., Sugnet, C. W., Furey, T. S., Ares, M., & Haussler, D. H. (2000) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.97.1.262 · Cited by 2,310 · see citing works
Online Learning for Matrix Factorization and Sparse CodingPreprint
Mairal, J., Bach, F., Ponce, J., & Sapiro, G. (2009) · arXiv (Cornell University) · doi:10.1145/1756006.1756008 · Cited by 2,305 · see citing works
Bendall, S. C., Simonds, E. F., Qiu, P., Amir, E.-A. D., Krutzik, P. O., Finck, R., Bruggner, R. V., Melamed, R., Trejo, A., Ornatsky, O. I., et al. (2011) · Science · doi:10.1126/science.1198704 · Cited by 2,279 · see citing works
Modern Multidimensional Scaling: Theory and Applications
Borg, I., & Groenen, P. (2003) · Journal of Educational Measurement · doi:10.1111/j.1745-3984.2003.tb01108.x · Cited by 2,267 · see citing works
Lustig, M. S., Donoho, D. L., Santos, J. M., & Pauly, J. M. (2008) · IEEE Signal Processing Magazine · doi:10.1109/msp.2007.914728 · Cited by 2,256 · see citing works
Biclustering of expression data.
Cheng, Y., & Church, G. M. (2000) · PubMed · Cited by 2,098 · see citing works
Singular value decomposition for genome-wide expression data processing and modeling
Alter, O., Brown, P. O., & Botstein, D. (2000) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.97.18.10101 · Cited by 1,989 · see citing works
Highly multiplexed imaging of tumor tissues with subcellular resolution by mass cytometry
Giesen, C., Wang, H. A. O., Schapiro, D., Zivanovic, N., Jacobs, A., Hattendorf, B., Schüffler, P. J., Grolimund, D., Buhmann, J. M., Brandt, S., et al. (2014) · Nature Methods · doi:10.1038/nmeth.2869 · Cited by 1,951 · see citing works
The Immunological Genome Project: networks of gene expression in immune cells
The Immunological Genome Project Consortium, Heng, T. S. P., Painter, M. W., Elpek, K., Lukacs-Kornek, V., Mauermann, N., Turley, S. J., Koller, D., Kim, F. S., Wagers, A. J., et al. (2008) · Nature Immunology · doi:10.1038/ni1008-1091 · Cited by 1,864 · see citing works
Dixit, A., Parnas, O., Li, B., Chen, J., Fulco, C. P., Jerby-Arnon, L., Marjanovic, N. D., Dionne, D., Burks, T., Raychowdhury, R., et al. (2016) · Cell · doi:10.1016/j.cell.2016.11.038 · Cited by 1,863 · see citing works
Self-citationCompressed sensing : theory and applications
Eldar, Y. C., & Kutyniok, G. (2012) · Cited by 1,840 · see citing works
Massively parallel single-cell RNA-seq for marker-free decomposition of tissues into cell types
Jaitin, D. A., Kenigsberg, E., Keren-Shaul, H., Elefant, N., Paul, F., Zaretsky, I., Mildner, A., Cohen, N., Jung, S., Tanay, A., et al. (2014) · Science · doi:10.1126/science.1247651 · Cited by 1,795 · see citing works
Adult mouse cortical cell taxonomy revealed by single cell transcriptomics
Tasic, B., Menon, V., Nguyen, T. N., Kim, T. K., Jarsky, T., Yao, Z., Levi, B., Gray, L. T., Sorensen, S. A., Dolbeare, T., et al. (2016) · Nature Neuroscience · doi:10.1038/nn.4216 · Cited by 1,714 · see citing works
Segal, E., Shapira, M., Regev, A., Pe’er, D., Botstein, D., Koller, D., & Friedman, N. (2003) · Nature Genetics · doi:10.1038/ng1165 · Cited by 1,686 · see citing works
Self-citationBayesian approach to single-cell differential expression analysis
Kharchenko, P. V., Silberstein, L., & Scadden, D. T. (2014) · Nature Methods · doi:10.1038/nmeth.2967 · Cited by 1,348 · see citing works
Single-Cell RNA-Seq Reveals Dynamic, Random Monoallelic Gene Expression in Mammalian Cells
Deng, Q., Ramsköld, D., Reinius, B., & Sandberg, R. (2014) · Science · doi:10.1126/science.1245316 · Cited by 1,345 · see citing works
Single-cell messenger RNA sequencing reveals rare intestinal cell types
Grün, D., Lyubimova, A., Kester, L., Wiebrands, K., Basak, O., Sasaki, N., Clevers, H., & van Oudenaarden, A. (2015) · Nature · doi:10.1038/nature14966 · Cited by 1,234 · see citing works
Adamson, B., Norman, T. M., Jost, M., Cho, M. Y., Nuñez, J. K., Chen, Y., Villalta, J. E., Gilbert, L. A., Horlbeck, M. A., Hein, M. Y., et al. (2016) · Cell · doi:10.1016/j.cell.2016.11.048 · Cited by 1,193 · see citing works
Self-citationSingle-cell transcriptomics reveals bimodality in expression and splicing in immune cells
Shalek, A. K., Satija, R., Adiconis, X., Gertner, R. S., Gaublomme, J. T., Raychowdhury, R., Schwartz, S., Yosef, N., Malboeuf, C., Lu, D., et al. (2013) · Nature · doi:10.1038/nature12172 · Cited by 1,190 · see citing works
Self-citationComprehensive Classification of Retinal Bipolar Neurons by Single-Cell Transcriptomics
Shekhar, K., Lapan, S. W., Whitney, I. E., Tran, N. M., Macosko, E. Z., Kowalczyk, M., Adiconis, X., Levin, J. Z., Nemesh, J., Goldman, M., et al. (2016) · Cell · doi:10.1016/j.cell.2016.07.054 · Cited by 1,176 · see citing works
Self-citationMultiplexed ion beam imaging of human breast tumors
Angelo, M., Bendall, S. C., Finck, R., Hale, M. B., Hitzman, C., Borowsky, A. D., Levenson, R. M., Lowe, J. B., Liu, S. D., Zhao, S., et al. (2014) · Nature Medicine · doi:10.1038/nm.3488 · Cited by 1,108 · see citing works
Transcriptional Heterogeneity and Lineage Commitment in Myeloid Progenitors
Paul, F., Arkin, Y., Giladi, A., Jaitin, D. A., Kenigsberg, E., Keren-Shaul, H., Winter, D., Lara-Astiaso, D., Gury, M., Weiner, A., et al. (2015) · Cell · doi:10.1016/j.cell.2015.11.013 · Cited by 1,082 · see citing works
Single-cell RNA-seq reveals dynamic paracrine control of cellular variation
Shalek, A. K., Satija, R., Shuga, J., Trombetta, J. J., Gennert, D., Lu, D., Chen, P., Gertner, R. S., Gaublomme, J. T., Yosef, N., et al. (2014) · Nature · doi:10.1038/nature13437 · Cited by 1,000 · see citing works
Self-citationAn elementary proof of a theorem of Johnson and Lindenstrauss
Dasgupta, S., & Gupta, A. (2002) · Random Structures and Algorithms · doi:10.1002/rsa.10073 · Cited by 927 · see citing works
Discovering statistically significant biclusters in gene expression data
Tanay, A., Sharan, R., & Ron Shamir, A. (2002) · Bioinformatics · doi:10.1093/bioinformatics/18.suppl_1.s136 · Cited by 857 · see citing works
Extensions of Lipschitz mappings into a Hilbert space
Johnson, W. B. (1984) · Medical Entomology and Zoology · Cited by 661 · see citing works
Revealing the vectors of cellular identity with single-cell genomics
Wagner, A., Regev, A., & Yosef, N. (2016) · Nature Biotechnology · doi:10.1038/nbt.3711 · Cited by 632 · see citing works
Self-citationIterative signature algorithm for the analysis of large-scale gene expression data
Bergmann, S., Ihmels, J., & Barkai, N. (2003) · Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics · doi:10.1103/physreve.67.031902 · Cited by 422 · see citing works
Duan, Q., Flynn, C., Niepel, M., Hafner, M. A., Muhlich, J. L., Fernandez, N., Rouillard, A. D., Tan, C. M., Chen, E. Y., Golub, T. R., et al. (2014) · Nucleic Acids Research · doi:10.1093/nar/gku476 · Cited by 309 · see citing works
Evolvability and hierarchy in rewired bacterial gene networks
Isalan, M., Lemerle, C., Michalodimitrakis, K., Horn, C., Beltrao, P., Raineri, E., Garriga-Canut, M., & Serrano, L. (2008) · Nature · doi:10.1038/nature06847 · Cited by 307 · see citing works
The Genotype-Tissue Expression (GTEx) project
Lonsdale, J., Thomas, J., Salvatore, M., Phillips, R., Lo, E., Shad, S., Hasz, R., Walters, G., Garcia, F., Young, N., et al. (2013) · Nature Genetics · doi:10.1038/ng.2653 · Cited by 263 · see citing works
Fixed single-cell transcriptomic characterization of human radial glial diversity
Thomsen, E. R., Mich, J. K., Yao, Z., Hodge, R. D., Doyle, A. M., Jang, S., Shehata, S. I., Nelson, A. M., Shapovalova, N. V., Levi, B. P., et al. (2015) · Nature Methods · doi:10.1038/nmeth.3629 · Cited by 227 · see citing works
Gleichman, S., & Eldar, Y. C. (2011) · IEEE Transactions on Information Theory · doi:10.1109/tit.2011.2165821 · Cited by 223 · see citing works
A method for high-throughput gene expression signature analysis
Peck, D., Crawford, E. D., Ross, K. N., Stegmaier, K., Golub, T. R., & Lamb, J. (2006) · Genome biology · doi:10.1186/gb-2006-7-7-r61 · Cited by 205 · see citing works
Heimberg, G., Bhatnagar, R., El-Samad, H., & Thomson, M. (2016) · Cell Systems · doi:10.1016/j.cels.2016.04.001 · Cited by 156 · see citing works
Efficient DNA ligation in DNA–RNA hybrid helices by Chlorella virus DNA ligase
Lohman, G. J. S., Zhang, Y., Zhelkovsky, A. M., et al. (2013) · Nucleic Acids Research · doi:10.1093/nar/gkt1032 · Cited by 109 · see citing works
Sparse Nonlinear Regression: Parameter Estimation and Asymptotic InferencePreprint
Yang, Z., Wang, Z., Liu, H., Eldar, Y. C., & Zhang, T. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1511.04514 · Cited by 31 · see citing works
Imputing gene expression from selectively reduced probe sets
Donner, Y., Feng, T., Benoist, C. O., & Koller, D. (2012) · Nature Methods · doi:10.1038/nmeth.2207 · Cited by 21 · see citing works
New guarantees for Blind Compressed Sensing
Aghagolzadeh, M., & Radha, H. (2015) · Allerton Conference on Communication, Control, and Computing · doi:10.1109/allerton.2015.7447148 · Cited by 12 · see citing works
Recovering key biological constituents through sparse representation of gene expression
Prat, Y., Fromer, M., Linial, N., & Michal Linial, B. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btr002 · Cited by 11 · see citing works
Efficient Dictionary Learning via Very Sparse Random ProjectionsPreprint
Pourkamali‐Anaraki, F., Becker, S., & Hughes, S. M. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1504.01169 · Cited by 4 · see citing works
Tradict enables accurate prediction of eukaryotic transcriptional states from 100 marker genesPreprint
Biswas, S., Kerner, K., Teixeira, P. J. P. L., Dangl, J. L., Jojic, V., & Wigge, P. A. (2016) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/060111 · Cited by 2 · see citing works
Flagged first, then by citations
Research works printed but not linked: 1
- Biswas S Kerner K Teixeira PJPL Dangl JL Jojic V Wigge PA 2016 Tradict enables high fidelity reconstruction of the eukaryotic transcriptome from 100 marker genes bioRxiv 60111
Cited by
Sorted by citations
Zitnik, M., Nguyen, F., Wang, B., Leskovec, J., Goldenberg, A., & Hoffman, M. M. (2019) · Information Fusion · doi:10.1016/j.inffus.2018.09.012 · Cited by 675 · see citing works
Combinatorial single-cell CRISPR screens by direct guide RNA capture and targeted sequencing
Replogle, J. M., Norman, T. M., Xu, A., Hussmann, J. A., Chen, J., Cogan, J. Z., Meer, E. J., Terry, J. M., Riordan, D. P., Srinivas, N., et al. (2020) · Nature Biotechnology · doi:10.1038/s41587-020-0470-y · Cited by 465 · see citing works
Exploring genetic interaction manifolds constructed from rich single-cell phenotypes
Norman, T. M., Horlbeck, M. A., Replogle, J. M., Ge, A. Y., Xu, A., Jost, M., Gilbert, L. A., & Weissman, J. S. (2019) · Science · doi:10.1126/science.aax4438 · Cited by 392 · see citing works
A new era in functional genomics screens
Przybyla, L., & Gilbert, L. A. (2022) · Nature Reviews Genetics · doi:10.1038/s41576-021-00409-w · Cited by 281 · see citing works
Leveraging Uncertainty in Machine Learning Accelerates Biological Discovery and Design
Hie, B., Bryson, B. D., & Berger, B. (2020) · Cell Systems · doi:10.1016/j.cels.2020.09.007 · Cited by 154 · see citing works
“…training a predictive model on the expression values of a set of genes (a feature set) to infer the expression of a different set of genes (a target set) (Figure 7A (Cleary et al., 2017), gene regulatory inference (Mueller et al., 2017), and cross-modality transfer learning (Zhou et al., 2020).”
BackgroundRood, J. E., Hupalowska, A., & Regev, A. (2024) · Cell · doi:10.1016/j.cell.2024.07.035 · Cited by 139 · see citing works
Self-citationJoint single-cell measurements of nuclear proteins and RNA in vivo
Chung, H., Parkhurst, C. N., Magee, E. M., Phillips, D., Habibi, E., Chen, F., Yeung, B. Z., Waldman, J., Artis, D., & Regev, A. (2021) · Nature Methods · doi:10.1038/s41592-021-01278-1 · Cited by 144 · see citing works
Self-citationScalable genetic screening for regulatory circuits using compressed Perturb-seq
Yao, D., Binan, L., Bezney, J., Simonton, B., Freedman, J., Frangieh, C. J., Dey, K., Geiger-Schuller, K., Eraslan, B., Gusev, A., et al. (2024) · Nature Biotechnology · doi:10.1038/s41587-023-01964-9 · Cited by 101 · see citing works
Self-citationPrediction of single-cell RNA expression profiles in live cells by Raman microscopy with Raman2RNA
Kobayashi-Kirschvink, K. J., Comiter, C. S., Gaddam, S., Joren, T., Grody, E. I., Ounadjela, J. R., Zhang, K., Ge, B., Kang, J. W., Xavier, R. J., et al. (2024) · Nature Biotechnology · doi:10.1038/s41587-023-02082-2 · Cited by 93 · see citing works
Self-citationSparse dictionary learning recovers pleiotropy from human cell fitness screens
Pan, J., Kwon, J. J., Talamas, J. A., Borah, A. A., Vazquez, F., Boehm, J. S., Tsherniak, A., Zitnik, M., McFarland, J. M., & Hahn, W. C. (2022) · Cell Systems · doi:10.1016/j.cels.2021.12.005 · Cited by 37 · see citing works
- Authors
- Cleary, B., Cong, L., Cheung, A., Lander, E. S., & Regev, A.
- Affiliations
- Broad Institute (US)
- Corresponding author
- Regev, Aviv
- Venue
- Cell
- Publisher
- Elsevier BV
- Published
- 2017-11
- Volume
- 171
- Issue
- 6
- Pages
- 1424-1436.e18
- ISSN
- 0092-8674 (ISSN-L)
- PMC
- PMC5726792
- OpenAlex
- W2768455369
- Cited by
- 114
- References
- 62
- Citations by year
- 2026: 2 · 2025: 8 · 2024: 14 · 2023: 9 · 2022: 14 · 2021: 14 · 2020: 16 · 2019: 20 · 2018: 14 · 2017: 2 · 2016: 1
- FWCI
- 5.81
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Cell (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc-nd)
- PMC
- Funders
- NHGRI CEGS (RM1HG006193)
- HHMI
- Klarman Cell Obervatory
- CRI
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Physical Sciences › Engineering › Computational Mechanics › Sparse and Compressive Sensing Techniques
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Keywords
- gene expression profiling, transcriptomics, compressed sensing, sparse representation, high-dimensional data
- MeSH terms
- Algorithms, Humans, Sequence Analysis, RNA, K562 Cells, Gene Expression Profiling, Data Compression, High-Throughput Nucleotide Sequencing
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
