Statistical single cell multi-omics integration

Current Opinion in Systems Biology

Volumn 7, Issue , 2018, Pages 54-59

  Colome Tatche M ^a,b,c   Theis, F J ^a,c  

^a INSTITUTE OF EPIDEMIOLOGY   (Germany)

^b UNIVERSITY MEDICAL CENTER GRONINGEN   (Netherlands)

^c TECHNICAL UNIVERSITY OF MUNICH   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

CELL FATE; CELL HETEROGENEITY; CHROMATIN STRUCTURE; CLASSIFIER; DNA METHYLATION; EPIGENETICS; GENOMICS; HUMAN; MACHINE LEARNING; METABOLOMICS; OMICS; PROTEOMICS; QUANTITATIVE TRAIT LOCUS; REVIEW; RNA SEQUENCE; SINGLE CELL ANALYSIS; TRANSCRIPTION REGULATION; TRANSCRIPTOMICS;

EID: 85045325679     PISSN: None     EISSN: 24523100     Source Type: Journal    
DOI: 10.1016/j.coisb.2018.01.003     Document Type: Review

References (52)

1. Linnarsson, S., Teichmann, S.A., Single-cell genomics: coming of age. Genome Biol, 17, 2016, 97.
2. Regev, A., Teichmann, S.A., Lander, E.S., Amit, I., Benoist, C., Birney, E., Bodenmiller, B., Campbell, P.J., Carninci, P., Clatworthy, M., et al., Human Cell Atlas Meeting Participants. Science forum: the human cell Atlas. Elife, 6, 2017, 10.7554/eLife.27041 Description of the idea as well as the potential of the Human Cell Atlas Project, aiming to build an open molecular reference map of cell states in healthy human tissues.
3. Sandberg, R., Entering the era of single-cell transcriptomics in biology and medicine. Nat Meth 11 (2014), 22–24.
4. Stegle, O., Teichmann, S.A., Marioni, J.C., Computational and analytical challenges in single-cell transcriptomics. Nat Rev Genet 16 (2015), 133–145 Systematic review of the main challenges in the analysis of single cell transcriptomics data.
5. Tang, F., Barbacioru, C., Wang, Y., Nordman, E., Lee, C., Xu, N., Wang, X., Bodeau, J., Tuch, B.B., Siddiqui, A., Lao, K., Surani, M.A., mRNA-Seq whole-transcriptome analysis of a single cell. Nat Meth 6 (2009), 377–382.
6. Navin, N., Kendall, J., Troge, J., Andrews, P., Rodgers, L., McIndoo, J., Cook, K., Stepansky, A., Levy, D., Esposito, D., et al. Tumour evolution inferred by single-cell sequencing. Nature 472 (2011), 90–94.
7. Falconer, E., Hills, M., Naumann, U., Poon, S.S.S., Chavez, E.A., Sanders, A.D., Zhao, Y., Hirst, M., Lansdorp, P.M., DNA template strand sequencing of single-cells maps genomic rearrangements at high resolution. Nat Meth 9 (2012), 1107–1112.
8. Smallwood, S.A., Lee, H.J., Angermueller, C., Krueger, F., Saadeh, H., Peat, J., Andrews, S.R., Stegle, O., Reik, W., Kelsey, G., Single-cell genome-wide bisulfite sequencing for assessing epigenetic heterogeneity. Nat Meth 11 (2014), 817–820.
9. Rotem, A., Ram, O., Shoresh, N., Sperling, R.A., Goren, A., Weitz, D.A., Bernstein, B.E., Single-cell ChIP-seq reveals cell subpopulations defined by chromatin state. Nat Biotechnol 33 (2015), 1165–1172.
10. Buenrostro, J.D., Wu, B., Litzenburger, U.M., Ruff, D., Gonzales, M.L., Snyder, M.P., Chang, H.Y., Greenleaf, W.J., Single-cell chromatin accessibility reveals principles of regulatory variation. Nature 523 (2015), 486–490.
11. Jin, W., Tang, Q., Wan, M., Cui, K., Zhang, Y., Ren, G., Ni, B., Sklar, J., Przytycka, T.M., Childs, R., Levens, D., Zhao, K., Genome-wide detection of DNase I hypersensitive sites in single cells and FFPE tissue samples. Nature 528 (2015), 142–146.
12. Cusanovich, D.A., Daza, R., Adey, A., Pliner, H.A., Christiansen, L., Gunderson, K.L., Steemers, F.J., Trapnell, C., Shendure, J., Multiplex single cell profiling of chromatin accessibility by combinatorial cellular indexing. Science 348 (2015), 910–914.
13. Nagano, T., Lubling, Y., Stevens, T.J., Schoenfelder, S., Yaffe, E., Dean, W., Laue, E.D., Tanay, A., Fraser, P., Single-cell Hi-C reveals cell-to-cell variability in chromosome structure. Nature 502 (2013), 59–64.
14. Kind, J., Pagie, L., de Vries, S.S., Nahidiazar, L., Dey, S.S., Bienko, M., Zhan, Y., Lajoie, B., de Graaf, C.A., Amendola, M., et al. Genome-wide maps of nuclear lamina interactions in single human cells. Cell 163 (2015), 134–147.
15. Frei, A.P., Bava, F.-A., Zunder, E.R., Hsieh, E.W.Y., Chen, S.-Y., Nolan, G.P., Gherardini, P.F., Highly multiplexed simultaneous detection of RNAs and proteins in single cells. Nat Meth 13 (2016), 269–275.
16. Fessenden, M., Metabolomics: small molecules, single cells. Nature 540 (2016), 153–155.
17. Macaulay, I.C., Haerty, W., Kumar, P., Li, Y.I., Hu, T.X., Teng, M.J., Goolam, M., Saurat, N., Coupland, P., Shirley, L.M., et al. G&T-seq: parallel sequencing of single-cell genomes and transcriptomes. Nat Meth 12 (2015), 519–522.
18. Dey, S.S., Kester, L., Spanjaard, B., Bienko, M., van Oudenaarden, A., Integrated genome and transcriptome sequencing of the same cell. Nat Biotechnol 33 (2015), 285–289.
19. Angermueller, C., Clark, S.J., Lee, H.J., Macaulay, I.C., Teng, M.J., Hu, T.X., Krueger, F., Smallwood, S.A., Ponting, C.P., Voet, T., Parallel single-cell sequencing links transcriptional and epigenetic heterogeneity. Nat Meth 13 (2016), 229–232.
20. Cheow, L.F., Courtois, E.T., Tan, Y., Viswanathan, R., Xing, Q., Tan, R.Z., Tan, D.S.W., Robson, P., Loh, Y.-H., Quake, S.R., Burkholder, W.F., Single-cell multimodal profiling reveals cellular epigenetic heterogeneity. Nat Meth 13 (2016), 833–836.
21. Hou, Y., Guo, H., Cao, C., Li, X., Hu, B., Zhu, P., Wu, X., Wen, L., Tang, F., Huang, Y., Peng, J., Single-cell triple omics sequencing reveals genetic, epigenetic, and transcriptomic heterogeneity in hepatocellular carcinomas. Cell Res 26 (2016), 304–319 One of a few papers describing the measurement and integration of more than two -omics layers of the same single cell, in particular genomics, epigenomics and transcriptomics.
22. Hu, Y., Huang, K., An, Q., Du, G., Hu, G., Xue, J., Zhu, X., Wang, C.-Y., Xue, Z., Fan, G., Simultaneous profiling of transcriptome and DNA methylome from a single cell. Genome Biol, 17, 2016, 88.
23. Pott, S., Simultaneous measurement of chromatin accessibility, DNA methylation, and nucleosome phasing in single cells. Elife, 6, 2017, 10.7554/eLife.23203.
24. Guo, F., Li, L., Li, J., Wu, X., Hu, B., Zhu, P., Wen, L., Tang, F., Single-cell multi-omics sequencing of mouse early embryos and embryonic stem cells. Cell Res 27 (2017), 967–988.
25. Clark, S.J., Argelaguet, R., Kapourani, C.-A., Stubbs, T.M., Lee, H.J., Krueger, F., Sanguinetti, G., Kelsey, G., Marioni, J.C., Stegle, O., Reik, W., Joint profiling of chromatin accessibility, DNA methylation and transcription in single cells. bioRxiv, 2017, 138685, 10.1101/138685 One of a few papers profiling and integrating more than two -omic layers of the same single cell, combining NOME-seq (chromatin accessibility and DNA methylation) with scRNA-seq.
26. Peterson, V.M., Zhang, K.X., Kumar, N., Wong, J., Li, L., Wilson, D.C., Moore, R., McClanahan, T.K., Sadekova, S., Klappenbach, J.A., Multiplexed quantification of proteins and transcripts in single cells. Nat Biotechnol 35 (2017), 936–939.
27. Stoeckius, M., Hafemeister, C., Stephenson, W., Houck-Loomis, B., Chattopadhyay, P.K., Swerdlow, H., Satija, R., Smibert, P., Simultaneous epitope and transcriptome measurement in single cells. Nat Meth 14 (2017), 865–868.
28. Stephens, Z.D., Lee, S.Y., Faghri, F., Campbell, R.H., Zhai, C., Efron, M.J., Iyer, R., Schatz, M.C., Sinha, S., Robinson, G.E., Big data: astronomical or genomical?. PLoS Biol, 13, 2015, e1002195.
29. Sun, S., A survey of multi-view machine learning. Neural Comput Appl 23 (2013), 2031–2038 A comprehensive review and categorization of machine learning methods dealing with the integration of multi-modal (“multi-view”) data sets, i.e. samples with multiple alternative multivariate observations.
30. Li, Y., Wu, F.-X., Ngom, A., A review on machine learning principles for multi-view biological data integration. Brief Bioinform, 2016, 10.1093/bib/bbw113.
31. Huang, S., Chaudhary, K., Garmire, L.X., More is better: recent progress in multi-omics data integration methods. Front Genet, 8, 2017, 84.
32. Civelek, M., Lusis, A.J., Systems genetics approaches to understand complex traits. Nat Rev Genet 15 (2014), 34–48.
33. Bartel, J., Krumsiek, J., Schramm, K., Adamski, J., Gieger, C., Herder, C., Carstensen, M., Peters, A., Rathmann, W., Roden, M., et al. The human blood metabolome-transcriptome interface. PLoS Genet, 11, 2015, e1005274.
34. Chen, L., Ge, B., Casale, F.P., Vasquez, L., Kwan, T., Garrido-Martín, D., Watt, S., Yan, Y., Kundu, K., Ecker, S., et al. Genetic drivers of epigenetic and transcriptional variation in human immune cells. Cell 167 (2016), 1398–1414 e24.
35. Sass, S., Buettner, F., Mueller, N.S., Theis, F.J., A modular framework for gene set analysis integrating multilevel omics data,. Nucleic Acids Res 41 (2013), 9622–9633.
36. Bock, C., Farlik, M., Sheffield, N.C., Multi-omics of single cells: strategies and applications. Trends Biotechnol 34 (2016), 605–608.
37. Han, K.Y., Kim, K.-T., Joung, J.-G., Son, D.-S., Kim, Y.J., Jo, A., Jeon, H.-J., Moon, H.-S., Yoo, C.E., Chung, W., et al. SIDR: simultaneous isolation and parallel sequencing of genomic DNA and total RNA from single cells. Genome Res, 2017, 10.1101/gr.223263.117.
38. Welch, J.D., Hartemink, A.J., Prins, J.F., MATCHER: manifold alignment reveals correspondence between single cell transcriptome and epigenome dynamics. Genome Biol, 18, 2017, 138 Description of a computational method to align multiple single cell omics layers, albeit assuming they have been measured in different single cells.
39. Butler, A., Satija, R., Integrated analysis of single cell transcriptomic data across conditions, technologies, and species. bioRxiv, 2017, 164889, 10.1101/164889 Computational integration of single cell transcriptomic measurements performed on different populations of single cells.
40. Witten, D.M., Tibshirani, R., Hastie, T., A penalized matrix decomposition, with applications to sparse principal components and canonical correlation analysis. Biostatistics 10 (2009), 515–534.
41. Lê Cao, K.-A., Martin, P.G.P., Robert-Granié, C., Besse, P., Sparse canonical methods for biological data integration: application to a cross-platform study. BMC Bioinf, 10, 2009, 34.
42. Waaijenborg, S., Verselewel de Witt Hamer, P.C., Zwinderman, A.H., Quantifying the association between gene expressions and DNA-markers by penalized canonical correlation analysis. Stat Appl Genet Mol Biol, 7, 2008 Article3.
43. van der Maaten, L., Hinton, G., Visualizing data using t-SNE. J Mach Learn Res 9 (2008), 2579–2605.
44. Haghverdi, L., Büttner, M., Wolf, F.A., Buettner, F., Theis, F.J., Diffusion pseudotime robustly reconstructs lineage branching. Nat Meth 13 (2016), 845–848.
45. Blondel, V.D., Guillaume, J.-L., Lambiotte, R., Lefebvre, E., Fast unfolding of communities in large networks. J Stat Mech, 2008, 2008, P10008.
46. Levine, J.H., Simonds, E.F., Bendall, S.C., Davis, K.L., Amir, E.-A.D., Tadmor, M.D., Litvin, O., Fienberg, H.G., Jager, A., Zunder, E.R., et al. Data-driven phenotypic dissection of AML reveals progenitor-like cells that correlate with prognosis. Cell 162 (2015), 184–197.
47. Amores, J., Multiple instance classification: review, taxonomy and comparative study. Artif Intell 201 (2013), 81–105.
48. Kang, H.M., Subramaniam, M., Targ, S., Nguyen, M., Maliskova, L., Wan, E., Wong, S., Byrnes, L., Lanata, C., Gate, R., et al. Multiplexing droplet-based single cell RNA-sequencing using natural genetic barcodes. bioRxiv, 2017, 118778, 10.1101/118778.
49. Angerer, P., Simon, L., Tritschler, S., Wolf, F.A., Fischer, D., Theis, F.J., Single cells make big data: new challenges and opportunities in transcriptomics. Curr Opin Struct Biol 4 (2017), 85–91 Review of computational challenges arising from the increasing size of scRNAseq data sets, highlighting opportunities and challenges in the context of big data analytics.
50. Zheng, G.X.Y., Terry, J.M., Belgrader, P., Ryvkin, P., Bent, Z.W., Wilson, R., Ziraldo, S.B., Wheeler, T.D., McDermott, G.P., Zhu, J., et al. Massively parallel digital transcriptional profiling of single cells. Nat Commun, 8, 2017, 14049.
51. Rosenberg, A.B., Roco, C., Muscat, R.A., Kuchina, A., Mukherjee, S., Chen, W., Peeler, D.J., Yao, Z., Tasic, B., Sellers, D.L., Pun, S.H., Seelig, G., Scaling single cell transcriptomics through split pool barcoding. bioRxiv, 2017, 105163, 10.1101/105163.
52. Hon, C.-C., Shin, J.W., Carninci, P., Stubbington, M.J.T., The human cell Atlas: technical approaches and challenges. Brief Funct Genomics, 2017, 10.1093/bfgp/elx029.