A review on integration methods for single-cell data_Journal of Biomedical Engineering

Authors：

PAN Duo , LI Huamei , LIU Hongde ,  SUN Xiao

State Key Laboratory of Bioelectronics, School of Biological Sciences and Medical Engineering, Southeast University, Nanjing 210096, P.R.China;

Corresponding author：

Keywords：

single-cell RNA sequencing; multi-modality; data integration; cell type; cell atlas

DOI：

10.7507/1001-5515.202104073

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The emergence of single-cell sequencing technology enables people to observe cells with unprecedented precision. However, it is difficult to capture the information on all cells and genes in one single-cell RNA sequencing (scRNA-seq) experiment. Single-cell data of a single modality cannot explain cell state and system changes in detail. The integrative analysis of single-cell data aims to address these two types of problems. Integrating multiple scRNA-seq data can collect complete cell types and provide a powerful boost for the construction of cell atlases. Integrating single-cell multimodal data can be used to study the causal relationship and gene regulation mechanism across modalities. The development and application of data integration methods helps fully explore the richness and relevance of single-cell data and discover meaningful biological changes. Based on this, this article reviews the basic principles, methods and applications of multiple scRNA-seq data integration and single-cell multimodal data integration. Moreover, the advantages and disadvantages of existing methods are discussed. Finally, the future development is prospected.

Citation： PAN Duo, LI Huamei, LIU Hongde, SUN Xiao. A review on integration methods for single-cell data. Journal of Biomedical Engineering, 2021, 38(5): 1010-1017. doi: 10.7507/1001-5515.202104073 Copy

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7.	Butler A, Hoffman P, Smibert P, et al. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat Biotechnol, 2018, 36(5): 411-420.
8.	Lin Y, Ghazanfar S, Wang K, et al. scMerge leverages factor analysis, stable expression, and pseudoreplication to merge multiple single-cell RNA-seq datasets. Proc Natl Acad Sci U S A, 2019, 116(20): 9775-9784.
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10.	Gagnon-Bartsch J A, Speed T P. Using control genes to correct for unwanted variation in microarray data. Biostatistics, 2012, 13(3): 539-552.
11.	Polański K, Young M D, Miao Z, et al. BBKNN: fast batch alignment of single cell transcriptomes. Bioinformatics, 2020, 36(3): 964-965.
12.	Barkas N, Petukhov V, Nikolaeva D, et al. Joint analysis of heterogeneous single-cell RNA-seq dataset collections. Nat Methods, 2019, 16(8): 695-698.
13.	Welch J D, Kozareva V, Ferreira A, et al. Single-cell multi-omic integration compares and contrasts features of brain cell identity. Cell, 2019, 177(7): 1873-1887.
14.	Yang Z, Michailidis G. A non-negative matrix factorization method for detecting modules in heterogeneous omics multi-modal data. Bioinformatics, 2016, 32(1): 1-8.
15.	Shaham U, Stanton K P, Zhao J, et al. Removal of batch effects using distribution-matching residual networks. Bioinformatics, 2017, 33(16): 2539-2546.
16.	Zou B, Zhang T, Zhou R, et al. DeepMNN: deep learning-based single-cell RNA sequencing data batch correction using mutual nearest neighbors, Front Genet, 2021, 12: 708981.
17.	Lotfollahi M, Wolf F A, Theis F J. ScGen predicts single-cell perturbation responses. Nat Methods, 2019, 16(8): 715-721.
18.	Wang Dongfang, Hou Siyu, Zhang Lei, et al. IMAP: integration of multiple single-cell datasets by adversarial paired transfer networks. Genome Biol, 2021, 22(1): 63.
19.	Zhang Q, He Y, Luo N, et al. Landscape and dynamics of single immune cells in hepatocellular carcinoma. Cell, 2019, 179(4): 829-845.
20.	Liao J, Yu Z, Chen Y, et al. Single-cell RNA sequencing of human kidney. Sci Data, 2020, 7(1): 4.
21.	Trujillo C A, Gao R, Negraes P D, et al. Complex oscillatory waves emerging from cortical organoids model early human brain network development. Cell Stem Cell, 2019, 25(4): 558-569.e7.
22.	Qi F, Qian S, Zhang S, et al. Single cell RNA sequencing of 13 human tissues identify cell types and receptors of human coronaviruses. Biochem Biophys Res Commun, 2020, 526(1): 135-140.
23.	Zhang H, Kang Z, Gong H, et al. Digestive system is a potential route of COVID-19: an analysis of single-cell coexpression pattern of key proteins in viral entry process. Gut, 2020, 69(6): 1010-1018.
24.	Liao M, Liu Y, Yuan J, et al. Single-cell landscape of bronchoalveolar immune cells in patients with COVID-19. Nat Med, 2020, 26(6): 842-844.
25.	Zhang J Y, Wang X M, Xing X, et al. Single-cell landscape of immunological responses in patients with COVID-19. Nat Immunol, 2020, 21(9): 1107-1118.
26.	Tran H T, Ang K S, Chevrier M, et al. A benchmark of batch-effect correction methods for single-cell RNA sequencing data. Genome Biol, 2020, 21(1): 12.
27.	Teichmann S, Efremova M. Method of the year 2019: single-cell multimodal omics. Nat Methods, 2020, 17(1): 1.
28.	Zhu C, Preissl S, Ren B. Single-cell multimodal omics: the power of many. Nat Methods, 2020, 17(1): 11-14.
29.	Colomé-Tatché M, Theis F J. Statistical single cell multi-omics integration. Curr Opin Syst Biol, 2018, 7: 54-59.
30.	Pliner H, Packer J S, Mcfaline-Figueroa J L, et al. Cicero predicts cis-Regulatory DNA interactions from single-cell chromatin accessibility data. Mol Cell, 2018, 71(5): 858-871.
31.	Mo A, Mukamel E A, Davis F P, et al. Epigenomic signatures of neuronal diversity in the mammalian brain. Neuron, 2015, 86(6): 1369-1384.
32.	Shen R, Olshen A B, Ladanyi M. Integrative clustering of multiple genomic data types using a joint latent variable model with application to breast and lung cancer subtype analysis. Bioinformatics, 2009, 25(22): 2906-2912.
33.	Wang B, Mezlini A M, Demir F, et al. Similarity network fusion for aggregating data types on a genomic scale. Nat Methods, 2014, 11(3): 333-337.
34.	Duren Z, Chen X, Zamanighomi M, et al. Integrative analysis of single-cell genomics data by coupled nonnegative matrix factorizations. Proc Natl Acad Sci U S A, 2018, 115(30): 7723-7728.
35.	Zeng W, Chen X, Duren Z, et al. DC3 is a method for deconvolution and coupled clustering from bulk and single-cell genomics data. Nat Commun, 2019, 10(1): 4613.
36.	Argelaguet R, Velten B, Arnol D, et al. Multi-Omics factor analysis-a framework for unsupervised integration of multi-omics data sets. Mol Syst Biol, 2018, 14(6): e8124.
37.	Welch J D, Hartemink A J, Prins J F. MATCHER: manifold alignment reveals correspondence between single cell transcriptome and epigenome dynamics. Genome Biol, 2017, 18(1): 138.
38.	Argelaguet R, Clark S J, Mohammed H, et al. Multi-omics profiling of mouse gastrulation at single-cell resolution. Nature, 2019, 576(7787): 487-491.
39.	Lake B B, Chen S, Sos B C, et al. Integrative single-cell analysis of transcriptional and epigenetic states in the human adult brain. Nat Biotechnol, 2018, 36(1): 70-80.

1. Hedlund E, Deng Qiaolin. Single-cell RNA sequencing: technical advancements and biological applications. Mol Aspects Med, 2018, 59: 36-46.
2. Aldridge S, Teichmann S A. Single cell transcriptomics comes of age. Nat Commun, 2020, 11(1): 4307.
3. Stuart T, Satija R. Integrative single-cell analysis. Nat Rev Genet, 2019, 20(5): 257-272.
4. Haghverdi L, Lun A, Morgan M D, et al. Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors. Nat Biotechnol, 2018, 36(5): 421-427.
5. Korsunsky I, Millard N, FAN J, et al. Fast, sensitive and accurate integration of single-cell data with harmony. Nat Methods, 2019, 16(12): 1289-1296.
6. Stuart T, Butler A, Hoffman P, et al. Comprehensive integration of single-cell data. Cell, 2019, 177(7): 1888-1902.
7. Butler A, Hoffman P, Smibert P, et al. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat Biotechnol, 2018, 36(5): 411-420.
8. Lin Y, Ghazanfar S, Wang K, et al. scMerge leverages factor analysis, stable expression, and pseudoreplication to merge multiple single-cell RNA-seq datasets. Proc Natl Acad Sci U S A, 2019, 116(20): 9775-9784.
9. Hie B, Bryson B, Berger B. Efficient integration of heterogeneous single-cell transcriptomes using Scanorama. Nat Biotechnol, 2019, 37(6): 685-691.
10. Gagnon-Bartsch J A, Speed T P. Using control genes to correct for unwanted variation in microarray data. Biostatistics, 2012, 13(3): 539-552.
11. Polański K, Young M D, Miao Z, et al. BBKNN: fast batch alignment of single cell transcriptomes. Bioinformatics, 2020, 36(3): 964-965.
12. Barkas N, Petukhov V, Nikolaeva D, et al. Joint analysis of heterogeneous single-cell RNA-seq dataset collections. Nat Methods, 2019, 16(8): 695-698.
13. Welch J D, Kozareva V, Ferreira A, et al. Single-cell multi-omic integration compares and contrasts features of brain cell identity. Cell, 2019, 177(7): 1873-1887.
14. Yang Z, Michailidis G. A non-negative matrix factorization method for detecting modules in heterogeneous omics multi-modal data. Bioinformatics, 2016, 32(1): 1-8.
15. Shaham U, Stanton K P, Zhao J, et al. Removal of batch effects using distribution-matching residual networks. Bioinformatics, 2017, 33(16): 2539-2546.
16. Zou B, Zhang T, Zhou R, et al. DeepMNN: deep learning-based single-cell RNA sequencing data batch correction using mutual nearest neighbors, Front Genet, 2021, 12: 708981.
17. Lotfollahi M, Wolf F A, Theis F J. ScGen predicts single-cell perturbation responses. Nat Methods, 2019, 16(8): 715-721.
18. Wang Dongfang, Hou Siyu, Zhang Lei, et al. IMAP: integration of multiple single-cell datasets by adversarial paired transfer networks. Genome Biol, 2021, 22(1): 63.
19. Zhang Q, He Y, Luo N, et al. Landscape and dynamics of single immune cells in hepatocellular carcinoma. Cell, 2019, 179(4): 829-845.
20. Liao J, Yu Z, Chen Y, et al. Single-cell RNA sequencing of human kidney. Sci Data, 2020, 7(1): 4.
21. Trujillo C A, Gao R, Negraes P D, et al. Complex oscillatory waves emerging from cortical organoids model early human brain network development. Cell Stem Cell, 2019, 25(4): 558-569.e7.
22. Qi F, Qian S, Zhang S, et al. Single cell RNA sequencing of 13 human tissues identify cell types and receptors of human coronaviruses. Biochem Biophys Res Commun, 2020, 526(1): 135-140.
23. Zhang H, Kang Z, Gong H, et al. Digestive system is a potential route of COVID-19: an analysis of single-cell coexpression pattern of key proteins in viral entry process. Gut, 2020, 69(6): 1010-1018.
24. Liao M, Liu Y, Yuan J, et al. Single-cell landscape of bronchoalveolar immune cells in patients with COVID-19. Nat Med, 2020, 26(6): 842-844.
25. Zhang J Y, Wang X M, Xing X, et al. Single-cell landscape of immunological responses in patients with COVID-19. Nat Immunol, 2020, 21(9): 1107-1118.
26. Tran H T, Ang K S, Chevrier M, et al. A benchmark of batch-effect correction methods for single-cell RNA sequencing data. Genome Biol, 2020, 21(1): 12.
27. Teichmann S, Efremova M. Method of the year 2019: single-cell multimodal omics. Nat Methods, 2020, 17(1): 1.
28. Zhu C, Preissl S, Ren B. Single-cell multimodal omics: the power of many. Nat Methods, 2020, 17(1): 11-14.
29. Colomé-Tatché M, Theis F J. Statistical single cell multi-omics integration. Curr Opin Syst Biol, 2018, 7: 54-59.
30. Pliner H, Packer J S, Mcfaline-Figueroa J L, et al. Cicero predicts cis-Regulatory DNA interactions from single-cell chromatin accessibility data. Mol Cell, 2018, 71(5): 858-871.
31. Mo A, Mukamel E A, Davis F P, et al. Epigenomic signatures of neuronal diversity in the mammalian brain. Neuron, 2015, 86(6): 1369-1384.
32. Shen R, Olshen A B, Ladanyi M. Integrative clustering of multiple genomic data types using a joint latent variable model with application to breast and lung cancer subtype analysis. Bioinformatics, 2009, 25(22): 2906-2912.
33. Wang B, Mezlini A M, Demir F, et al. Similarity network fusion for aggregating data types on a genomic scale. Nat Methods, 2014, 11(3): 333-337.
34. Duren Z, Chen X, Zamanighomi M, et al. Integrative analysis of single-cell genomics data by coupled nonnegative matrix factorizations. Proc Natl Acad Sci U S A, 2018, 115(30): 7723-7728.
35. Zeng W, Chen X, Duren Z, et al. DC3 is a method for deconvolution and coupled clustering from bulk and single-cell genomics data. Nat Commun, 2019, 10(1): 4613.
36. Argelaguet R, Velten B, Arnol D, et al. Multi-Omics factor analysis-a framework for unsupervised integration of multi-omics data sets. Mol Syst Biol, 2018, 14(6): e8124.
37. Welch J D, Hartemink A J, Prins J F. MATCHER: manifold alignment reveals correspondence between single cell transcriptome and epigenome dynamics. Genome Biol, 2017, 18(1): 138.
38. Argelaguet R, Clark S J, Mohammed H, et al. Multi-omics profiling of mouse gastrulation at single-cell resolution. Nature, 2019, 576(7787): 487-491.
39. Lake B B, Chen S, Sos B C, et al. Integrative single-cell analysis of transcriptional and epigenetic states in the human adult brain. Nat Biotechnol, 2018, 36(1): 70-80.

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