Efficient Generation of Transcriptomic Profiles by Random Composite Measurements

Cell

Volumn 171, Issue 6, 2017, Pages 1424-1436.e18

  Cleary, Brian ^a,b   Cong, Le ^a   Cheung, Anthea ^a   Lander, Eric S ^a,b,c   Regev, Aviv ^a,b,d  

^a BROAD INSTITUTE OF MIT AND HARVARD   (United States)

^b MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

^c HARVARD MEDICAL SCHOOL   (United States)

^d HOWARD HUGHES MEDICAL INSTITUTE   (United States)

Author keywords

compressed sensing; gene expression; random composite measurements

Indexed keywords

ARTICLE; CONTROLLED STUDY; DATA ANALYSIS; DATA PROCESSING; GENE EXPRESSION PROFILING; MATHEMATICAL ANALYSIS; NONLINEAR SYSTEM; PRIORITY JOURNAL; RANDOM COMPOSITE MEASUREMENT; RNA SEQUENCE; TRANSCRIPTOMICS; ALGORITHM; HIGH THROUGHPUT SEQUENCING; HUMAN; INFORMATION PROCESSING; K-562 CELL LINE; PROCEDURES; SEQUENCE ANALYSIS;

ALGORITHMS; DATA COMPRESSION; GENE EXPRESSION PROFILING; HIGH-THROUGHPUT NUCLEOTIDE SEQUENCING; HUMANS; K562 CELLS; SEQUENCE ANALYSIS, RNA;

EID: 85034441714     PISSN: 00928674     EISSN: 10974172     Source Type: Journal    
DOI: 10.1016/j.cell.2017.10.023     Document Type: Article

References (54)

1. 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. A multiplexed single-cell CRISPR screening platform enables systematic dissection of the unfolded protein response. Cell 167 (2016), 1867–1882.
2. Aghagolzadeh, M., and Radha, H. (2015). New guarantees for blind compressed sensing. arXiv:1508.01842.
3. Alter, O., Brown, P.O., Botstein, D., Singular value decomposition for genome-wide expression data processing and modeling. Proc. Natl. Acad. Sci. USA 97 (2000), 10101–10106.
4. 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. Multiplexed ion beam imaging of human breast tumors. Nat. Med. 20 (2014), 436–442.
5. Bendall, S.C., Simonds, E.F., Qiu, P., Amir, A.D., Krutzik, P.O., Finck, R., Bruggner, R.V., Melamed, R., Trejo, A., Ornatsky, O.I., et al. Single-cell mass cytometry of differential immune and drug responses across a human hematopoietic continuum. Science 332 (2011), 687–696.
6. Bergmann, S., Ihmels, J., Barkai, N., Iterative signature algorithm for the analysis of large-scale gene expression data. Phys. Rev. E Stat. Nonlin. Soft Matter Phys., 67, 2003, 031902.
7. Biswas, S., Kerner, K., Teixeira, P.J.P.L., Dangl, J.L., Jojic, V., Wigge, P.A., Tradict enables high fidelity reconstruction of the eukaryotic transcriptome from 100 marker genes. bioRxiv, 2016, 10.1101/060111.
8. Borg, I., Groenen, P., Modern Multidimensional Scaling: Theory and Applications. 2005, Springer.
9. Brown, M.P., Grundy, W.N., Lin, D., Cristianini, N., Sugnet, C.W., Furey, T.S., Ares, M. Jr., Haussler, D., Knowledge-based analysis of microarray gene expression data by using support vector machines. Proc. Natl. Acad. Sci. USA 97 (2000), 262–267.
10. Candes, E., Tao, T., Decoding by linear programming. IEEE Trans. Inf. Theory 51 (2005), 4203–4215.
11. Candes, E.J., Wakin, M.B., An introduction to compressive sampling. IEEE Signal Process. Mag. 25 (2008), 21–30.
12. Candes, E., Romberg, J., Tao, T., Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information. IEEE Trans. Inf. Theory 52 (2006), 489–509.
13. Cheng, Y., Church, G., Biclustering of expression data. Proc. Int. Conf. Intell. Syst. Mol. Biol. 8 (2000), 93–103.
14. Dasgupta, S., Gupta, A., An elementary proof of a theorem of Johnson and Lindenstrauss. Random Structures Algorithms 22 (2003), 60–65.
15. Deng, Q., Ramsköld, D., Reinius, B., Sandberg, R., Single-cell RNA-seq reveals dynamic, random monoallelic gene expression in mammalian cells. Science 343 (2014), 193–196.
16. 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. Perturb-seq: dissecting molecular circuits with scalable single-cell RNA profiling of pooled genetic screens. Cell 167 (2016), 1853–1866.
17. Donner, Y., Feng, T., Benoist, C., Koller, D., Imputing gene expression from selectively reduced probe sets. Nat. Methods 9 (2012), 1120–1125.
18. Donoho, D.L., Compressed sensing. IEEE Trans. Inf. Theory 52 (2006), 1289–1306.
19. Duan, Q., Flynn, C., Niepel, M., Hafner, M., Muhlich, J.L., Fernandez, N.F., Rouillard, A.D., Tan, C.M., Chen, E.Y., Golub, T.R., et al. LINCS Canvas Browser: interactive web app to query, browse and interrogate LINCS L1000 gene expression signatures. Nucleic Acids Res., 42, 2014, W449-60.
20. Eldar, Y.C., Kutyniok, G., Compressed Sensing: Theory and Applications. 2012, Cambridge.
21. 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. Highly multiplexed imaging of tumor tissues with subcellular resolution by mass cytometry. Nat. Methods 11 (2014), 417–422.
22. Gleichman, S., Eldar, Y.C., Blind compressed sensing. IEEE Trans. Inf. Theory 57 (2011), 6958–6975.
23. Grün, D., Lyubimova, A., Kester, L., Wiebrands, K., Basak, O., Sasaki, N., Clevers, H., van Oudenaarden, A., Single-cell messenger RNA sequencing reveals rare intestinal cell types. Nature 525 (2015), 251–255.
24. GTEx Consortium. The genotype-tissue expression (GTEx) project. Nat. Genet. 45 (2013), 580–585.
25. Heimberg, G., Bhatnagar, R., El-Samad, H., Thomson, M., Low dimensionality in gene expression data enables the accurate extraction of transcriptional programs from shallow sequencing. Cell Syst. 2 (2016), 239–250.
26. Heinz, S., Benner, C., Spann, N., Bertolino, E., Lin, Y.C., Laslo, P., Cheng, J.X., Murre, C., Singh, H., Glass, C.K., Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol. Cell 38 (2010), 576–589.
27. Heng, T.S.P., Painter, M.W., Immunological Genome Project Consortium. The Immunological Genome Project: networks of gene expression in immune cells. Nat. Immunol. 9 (2008), 1091–1094.
28. Jaitin, D.A., Kenigsberg, E., Keren-Shaul, H., Elefant, N., Paul, F., Zaretsky, I., Mildner, A., Cohen, N., Jung, S., Tanay, A., Amit, I., Massively parallel single-cell RNA-seq for marker-free decomposition of tissues into cell types. Science 343 (2014), 776–779.
29. Johnson, W.B., Lindenstrauss, J., Extensions of Lipschitz mappings into a Hilbert space. Contemp. Math. 26 (1984), 189–206.
30. Kharchenko, P.V., Silberstein, L., Scadden, D.T., Bayesian approach to single-cell differential expression analysis. Nat. Methods 11 (2014), 740–742.
31. Kundaje, A., Meuleman, W., Ernst, J., Bilenky, M., Yen, A., Heravi-Moussavi, A., Kheradpour, P., Zhang, Z., Wang, J., Ziller, M.J., et al., Roadmap Epigenomics Consortium. Integrative analysis of 111 reference human epigenomes. Nature 518 (2015), 317–330.
32. Liberzon, A., Subramanian, A., Pinchback, R., Thorvaldsdóttir, H., Tamayo, P., Mesirov, J.P., Molecular signatures database (MSigDB) 3.0. Bioinformatics 27 (2011), 1739–1740.
33. Lohman, G.J.S., Zhang, Y., Zhelkovsky, A.M., Cantor, E.J., Evans, T.C. Jr., Efficient DNA ligation in DNA-RNA hybrid helices by Chlorella virus DNA ligase. Nucleic Acids Res. 42 (2014), 1831–1844.
34. Lustig, M., Donoho, D.L., Santos, J.M., Pauly, J.M., Compressed sensing MRI. IEEE Signal Process. Mag. 25 (2008), 72–82.
35. Mairal, J., Bach, F., Ponce, J., Sapiro, G., Online learning for matrix factorization and sparse coding. J. Mach. Learn. Res. 11 (2010), 19–60.
36. 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. Transcriptional heterogeneity and lineage commitment in myeloid progenitors. Cell 163 (2015), 1663–1677.
37. Peck, D., Crawford, E.D., Ross, K.N., Stegmaier, K., Golub, T.R., Lamb, J., A method for high-throughput gene expression signature analysis. Genome Biol., 7, 2006, R61.
38. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al. Scikit-learn: machine learning in Python. Mach. Learn. 12 (2012), 2825–2830.
39. Pourkamali-anaraki, F., Becker, S., and Hughes, S.M. (2015). Efficient dictionary learning via very sparse random projections. arXiv:1504.01169.
40. Prat, Y., Fromer, M., Linial, N., Linial, M., Recovering key biological constituents through sparse representation of gene expression. Bioinformatics 27 (2011), 655–661.
41. Segal, E., Shapira, M., Regev, A., Pe'er, D., Botstein, D., Koller, D., Friedman, N., Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data. Nat. Genet. 34 (2003), 166–176.
42. 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. Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells. Nature 498 (2013), 236–240.
43. 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. Single-cell RNA-seq reveals dynamic paracrine control of cellular variation. Nature 510 (2014), 363–369.
44. 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. Comprehensive classification of retinal bipolar neurons by single-cell transcriptomics. Cell 166 (2016), 1308–1323.
45. 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. A next generation connectivity map: l1000 platform and the first 1,000,000 profiles. bioRxiv, 2017, 10.1101/136168.
46. Tanay, A., Sharan, R., Shamir, R., Discovering statistically significant biclusters in gene expression data. Bioinformatics 18:Suppl 1 (2002), S136–S144.
47. Tasic, B., Menon, V., Nguyen, T.N.T., Kim, T.T.K., Jarsky, T., Yao, Z., Levi, B.B., Gray, L.T., Sorensen, S.A., Dolbeare, T., et al. Adult mouse cortical cell taxonomy revealed by single cell transcriptomics. Nat. Neurosci. 19 (2016), 335–346.
48. Tenenbaum, J.B., de Silva, V., Langford, J.C., A global geometric framework for nonlinear dimensionality reduction. Science 290 (2000), 2319–2323.
49. 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., Ramanathan, S., Fixed single-cell transcriptomic characterization of human radial glial diversity. Nat. Methods 13 (2016), 87–93.
50. 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. Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq. Science 352 (2016), 189–196.
51. Wagner, A., Regev, A., Yosef, N., Revealing the vectors of cellular identity with single-cell genomics. Nat. Biotechnol. 34 (2016), 1145–1160.
52. 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., Cancer Genome Atlas Research Network. The Cancer Genome Atlas Pan-Cancer analysis project. Nat. Genet. 45 (2013), 1113–1120.
53. Yang, Z., Wang, Z., Liu, H., Eldar, Y.C., and Zhang, T. (2016). Sparse nonlinear regression: parameter estimation and asymptotic inference. arXiv:1511.04514.
54. Zeisel, A., Manchado, A.B.M., Codeluppi, S., Lonnerberg, P., La Manno, G., Jureus, A., Marques, S., Munguba, H., He, L., Betsholtz, C., et al. Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq. Science 347 (2015), 1138–1142.