Imputation method for dropout in single-cell transcriptome data_Journal of Biomedical Engineering

Authors：

JIANG Chao ^1,3 , HU Longfei ² , XU Chunxiang ¹ , GE Qinyu ¹ ,  ZHAO Xiangwei ¹

1. State Key Laboratory of Bioelectronics, School of Biological Sciences and Medical Engineering, Southeast University, Nanjing 210096, P. R. China;
2. Singleron BiotechCo., Ltd, Nanjing 210018, P. R. China;
3. School of Medicine & Holistic Integrative Medicine, Nanjing University of Chinese Medicine, Nanjing 210023, P. R. China;

Corresponding author：

Keywords：

Single-cell RNA sequencing; Dropout; Statistical model; Low rank matrix completion; Deep learning

DOI：

10.7507/1001-5515.202301009

Video：

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Single-cell transcriptome sequencing (scRNA-seq) can resolve the expression characteristics of cells in tissues with single-cell precision, enabling researchers to quantify cellular heterogeneity within populations with higher resolution, revealing potentially heterogeneous cell populations and the dynamics of complex tissues. However, the presence of a large number of technical zeros in scRNA-seq data will have an impact on downstream analysis of cell clustering, differential genes, cell annotation, and pseudotime, hindering the discovery of meaningful biological signals. The main idea to solve this problem is to make use of the potential correlation between cells and genes, and to impute the technical zeros through the observed data. Based on this, this paper reviewed the basic methods of imputing technical zeros in the scRNA-seq data and discussed the advantages and disadvantages of the existing methods. Finally, recommendations and perspectives on the use and development of the method were provided.

Citation： JIANG Chao, HU Longfei, XU Chunxiang, GE Qinyu, ZHAO Xiangwei. Imputation method for dropout in single-cell transcriptome data. Journal of Biomedical Engineering, 2023, 40(4): 778-783, 791. doi: 10.7507/1001-5515.202301009 Copy

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2.	Picelli S, Bjorklund A K, Faridani O R, et al. Smart-seq2 for sensitive full-length transcriptome profiling in single cells. Nat Methods, 2013, 10(11): 1096-1098..
3.	Zheng G X, Terry J M, Belgrader P, et al. Massively parallel digital transcriptional profiling of single cells. Nat Commun, 2017, 8: E14049..
4.	Yu J, Cheng W, Jia M, et al. Toxicity of perfluorooctanoic acid on zebrafish early embryonic development determined by single-cell RNA sequencing. J Hazard Mater, 2022, 427: E127888..
5.	Petitpre C, Faure L, Uhl P, et al. Single-cell RNA-sequencing analysis of the developing mouse inner ear identifies molecular logic of auditory neuron diversification. Nat Commun, 2022, 13(1): E3878..
6.	Hou W, Ji Z, Ji H, et al. A systematic evaluation of single-cell RNA-sequencing imputation methods. Genome Biol, 2020, 21(1): E218..
7.	Chen M, Zhou X. VIPER: variability-preserving imputation for accurate gene expression recovery in single-cell RNA sequencing studies. Genome Biol, 2018, 19(1): E196..
8.	Tang Wenhao, Bertaux F, Thomas P, et al. bayNorm: Bayesian gene expression recovery, imputation and normalization for single-cell RNA-sequencing data. Bioinformatics, 2020, 36(4): 1174-1181..
9.	Huang M, Wang J, Torre E, et al. SAVER: gene expression recovery for single-cell RNA sequencing. Nat Methods, 2018, 15(7): 539-542..
10.	Zheng Y, Zhong Y, Hu J, et al. SCC: an accurate imputation method for scRNA-seq dropouts based on a mixture model. BMC Bioinformatics, 2021, 22(1): E5..
11.	Li W V, Li J J. An accurate and robust imputation method scImpute for single-cell RNA-seq data. Nat Commun, 2018, 9(1): E997..
12.	Zand M, Ruan J. Network-based single-cell RNA-seq data imputation enhances cell type identification. Genes (Basel), 2020, 11(4): E377..
13.	van Dijk D, Sharma R, Nainys J, et al. Recovering gene interactions from single-cell data using data diffusion. Cell, 2018, 174(3): 716-729..
14.	Xu X, Yu X, Hu G, et al. Propensity score matching enables batch-effect-corrected imputation in single-cell RNA-seq analysis. Brief Bioinform, 2022, 23(4): Ebbac275..
15.	Liu Q, Luo X, Li J, et al. scESI: evolutionary sparse imputation for single-cell transcriptomes from nearest neighbor cells. Brief Bioinform, 2022, 23(5): Ebbac144..
16.	Azim R, Wang S, Dipu S A. CDSImpute: An ensemble similarity imputation method for single-cell RNA sequence dropouts. Comput Biol Med, 2022, 146: E105658..
17.	Malec M, Kurban H, Dalkilic M. ccImpute: an accurate and scalable consensus clustering based algorithm to impute dropout events in the single-cell RNA-seq data. BMC Bioinformatics, 2022, 23(1): E291..
18.	Tran D, Tran B, Nguyen H, et al. A novel method for single-cell data imputation using subspace regression. Sci Rep, 2022, 12(1): E2697..
19.	Leote A C, Wu X, Beyer A. Regulatory network-based imputation of dropouts in single-cell RNA sequencing data. PLoS Comput Biol, 2022, 18(2): Ee1009849..
20.	Jin K, Ou-Yang L, Zhao X M, et al. scTSSR: gene expression recovery for single-cell RNA sequencing using two-side sparse self-representation. Bioinformatics, 2020, 36(10): 3131-3138..
21.	Qi J, Sheng Q, Zhou Y, et al. scMTD: a statistical multidimensional imputation method for single-cell RNA-seq data leveraging transcriptome dynamic information. Cell Biosci, 2022, 12(1): E142..
22.	Peng T, Zhu Q, Yin P, et al. SCRABBLE: single-cell RNA-seq imputation constrained by bulk RNA-seq data. Genome Biol, 2019, 20(1): E88..
23.	Mongia A, Sengupta D, Majumdar A. McImpute: Matrix completion based imputation for single cell RNA-seq Data. Front Genet, 2019, 10: E9..
24.	Xu J, Cai L, Liao B, et al. CMF-Impute: an accurate imputation tool for single-cell RNA-seq data. Bioinformatics, 2020, 36(10): 3139-3147..
25.	Zhang L, Zhang S. Imputing single-cell RNA-seq data by considering cell heterogeneity and prior expression of dropouts. J Mol Cell Biol, 2021, 13(1): 29-40..
26.	Hu Y, Li B, Zhang W, et al. WEDGE: imputation of gene expression values from single-cell RNA-seq datasets using biased matrix decomposition. Brief Bioinform, 2021, 22(5): Ebbab085..
27.	Pan X, Li Z, Qin S, et al. ScLRTC: imputation for single-cell RNA-seq data via low-rank tensor completion. BMC Genomics, 2021, 22(1): E860..
28.	Linderman G C, Zhao J, Roulis M, et al. Zero-preserving imputation of single-cell RNA-seq data. Nat Commun, 2022, 13(1): E192..
29.	Jin K, Li B, Yan H, et al. Imputing dropouts for single-cell RNA sequencing based on multi-objective optimization. Bioinformatics, 2022, 38(12): 3222-3230..
30.	Su Y, Wang F, Zhang S, et al. scWMC: weighted matrix completion-based imputation of scRNA-seq data via prior subspace information. Bioinformatics, 2022, 38(19): 4537-4545..
31.	Chi W, Deng M. Sparsity-penalized stacked denoising autoencoders for imputing single-cell RNA-seq data. Genes (Basel), 2020, 11(5): E532..
32.	Chen S, Yan X, Zheng R, et al. Bubble: a fast single-cell RNA-seq imputation using an autoencoder constrained by bulk RNA-seq data. Brief Bioinform, 2023, 24(1): Ebbac580..
33.	Eraslan G, Simon L M, Mircea M, et al. Single-cell RNA-seq denoising using a deep count autoencoder. Nat Commun, 2019, 10: E390..
34.	Wu X, Zhou Y. GE-Impute: graph embedding-based imputation for single-cell RNA-seq data. Brief Bioinform, 2022, 23(5): Ebbac313..
35.	Wu W, Liu Y, Dai Q, et al. G2S3: A gene graph-based imputation method for single-cell RNA sequencing data. PLoS Comput Biol, 2021, 17(5): Ee1009029..
36.	Talwar D, Mongia A, Sengupta D, et al. AutoImpute: Autoencoder based imputation of single-cell RNA-seq data. Sci Rep, 2018, 8: E16329..
37.	Arisdakessian C, Poirion O, Yunits B, et al. DeepImpute: an accurate, fast, and scalable deep neural network method to impute single-cell RNA-seq data. Genome Biol, 2019, 20(1): E211..
38.	Badsha M B, Li R, Liu B, et al. Imputation of single-cell gene expression with an autoencoder neural network. Quant Biol, 2020, 8(1): 78-94..
39.	Li H, Brouwer C R, Luo W. A universal deep neural network for in-depth cleaning of single-cell RNA-Seq data. Nat Commun, 2022, 13(1): E1901..
40.	Karikomi M, Zhou P, Nie Q. DURIAN: an integrative deconvolution and imputation method for robust signaling analysis of single-cell transcriptomics data. Brief Bioinform, 2022, 23(4): Ebbac223..
41.	Li X, Li S, Huang L, et al. High-throughput single-cell RNA-seq data imputation and characterization with surrogate-assisted automated deep learning. Brief Bioinform, 2022, 23(1): Ebbab368..
42.	Islam M T, Wang J Y, Ren H, et al. Leveraging data-driven self-consistency for high-fidelity gene expression recovery. Nat Commun, 2022, 13(1): E7142..
43.	Xu C, Cai L, Gao J. An efficient scRNA-seq dropout imputation method using graph attention network. BMC Bioinformatics, 2021, 22(1): E582..
44.	Rao J, Zhou X, Lu Y, et al. Imputing single-cell RNA-seq data by combining graph convolution and autoencoder neural networks. iScience, 2021, 24(5): E102393..
45.	Wang J, Ma A, Chang Y, et al. scGNN is a novel graph neural network framework for single-cell RNA-Seq analyses. Nat Commun, 2021, 12(1): E1882..
46.	Liu J, Pan Y, Ruan Z, et al. SCDD: a novel single-cell RNA-seq imputation method with diffusion and denoising. Brief Bioinform, 2022, 23(5): Ebbac398..
47.	Patruno L, Maspero D, Craighero F, et al. A review of computational strategies for denoising and imputation of single-cell transcriptomic data. Brief Bioinform, 2021, 22(4): Ebbaa222..
48.	Wang M, Gan J, Han C, et al. Imputation methods for scRNA sequencing data. Appl Sci, 2022, 12(20): E10684..

1. Tang F, Barbacioru C, Wang Y, et al. mRNA-Seq whole-transcriptome analysis of a single cell. Nat Methods, 2009, 6(5): 377-382..
2. Picelli S, Bjorklund A K, Faridani O R, et al. Smart-seq2 for sensitive full-length transcriptome profiling in single cells. Nat Methods, 2013, 10(11): 1096-1098..
3. Zheng G X, Terry J M, Belgrader P, et al. Massively parallel digital transcriptional profiling of single cells. Nat Commun, 2017, 8: E14049..
4. Yu J, Cheng W, Jia M, et al. Toxicity of perfluorooctanoic acid on zebrafish early embryonic development determined by single-cell RNA sequencing. J Hazard Mater, 2022, 427: E127888..
5. Petitpre C, Faure L, Uhl P, et al. Single-cell RNA-sequencing analysis of the developing mouse inner ear identifies molecular logic of auditory neuron diversification. Nat Commun, 2022, 13(1): E3878..
6. Hou W, Ji Z, Ji H, et al. A systematic evaluation of single-cell RNA-sequencing imputation methods. Genome Biol, 2020, 21(1): E218..
7. Chen M, Zhou X. VIPER: variability-preserving imputation for accurate gene expression recovery in single-cell RNA sequencing studies. Genome Biol, 2018, 19(1): E196..
8. Tang Wenhao, Bertaux F, Thomas P, et al. bayNorm: Bayesian gene expression recovery, imputation and normalization for single-cell RNA-sequencing data. Bioinformatics, 2020, 36(4): 1174-1181..
9. Huang M, Wang J, Torre E, et al. SAVER: gene expression recovery for single-cell RNA sequencing. Nat Methods, 2018, 15(7): 539-542..
10. Zheng Y, Zhong Y, Hu J, et al. SCC: an accurate imputation method for scRNA-seq dropouts based on a mixture model. BMC Bioinformatics, 2021, 22(1): E5..
11. Li W V, Li J J. An accurate and robust imputation method scImpute for single-cell RNA-seq data. Nat Commun, 2018, 9(1): E997..
12. Zand M, Ruan J. Network-based single-cell RNA-seq data imputation enhances cell type identification. Genes (Basel), 2020, 11(4): E377..
13. van Dijk D, Sharma R, Nainys J, et al. Recovering gene interactions from single-cell data using data diffusion. Cell, 2018, 174(3): 716-729..
14. Xu X, Yu X, Hu G, et al. Propensity score matching enables batch-effect-corrected imputation in single-cell RNA-seq analysis. Brief Bioinform, 2022, 23(4): Ebbac275..
15. Liu Q, Luo X, Li J, et al. scESI: evolutionary sparse imputation for single-cell transcriptomes from nearest neighbor cells. Brief Bioinform, 2022, 23(5): Ebbac144..
16. Azim R, Wang S, Dipu S A. CDSImpute: An ensemble similarity imputation method for single-cell RNA sequence dropouts. Comput Biol Med, 2022, 146: E105658..
17. Malec M, Kurban H, Dalkilic M. ccImpute: an accurate and scalable consensus clustering based algorithm to impute dropout events in the single-cell RNA-seq data. BMC Bioinformatics, 2022, 23(1): E291..
18. Tran D, Tran B, Nguyen H, et al. A novel method for single-cell data imputation using subspace regression. Sci Rep, 2022, 12(1): E2697..
19. Leote A C, Wu X, Beyer A. Regulatory network-based imputation of dropouts in single-cell RNA sequencing data. PLoS Comput Biol, 2022, 18(2): Ee1009849..
20. Jin K, Ou-Yang L, Zhao X M, et al. scTSSR: gene expression recovery for single-cell RNA sequencing using two-side sparse self-representation. Bioinformatics, 2020, 36(10): 3131-3138..
21. Qi J, Sheng Q, Zhou Y, et al. scMTD: a statistical multidimensional imputation method for single-cell RNA-seq data leveraging transcriptome dynamic information. Cell Biosci, 2022, 12(1): E142..
22. Peng T, Zhu Q, Yin P, et al. SCRABBLE: single-cell RNA-seq imputation constrained by bulk RNA-seq data. Genome Biol, 2019, 20(1): E88..
23. Mongia A, Sengupta D, Majumdar A. McImpute: Matrix completion based imputation for single cell RNA-seq Data. Front Genet, 2019, 10: E9..
24. Xu J, Cai L, Liao B, et al. CMF-Impute: an accurate imputation tool for single-cell RNA-seq data. Bioinformatics, 2020, 36(10): 3139-3147..
25. Zhang L, Zhang S. Imputing single-cell RNA-seq data by considering cell heterogeneity and prior expression of dropouts. J Mol Cell Biol, 2021, 13(1): 29-40..
26. Hu Y, Li B, Zhang W, et al. WEDGE: imputation of gene expression values from single-cell RNA-seq datasets using biased matrix decomposition. Brief Bioinform, 2021, 22(5): Ebbab085..
27. Pan X, Li Z, Qin S, et al. ScLRTC: imputation for single-cell RNA-seq data via low-rank tensor completion. BMC Genomics, 2021, 22(1): E860..
28. Linderman G C, Zhao J, Roulis M, et al. Zero-preserving imputation of single-cell RNA-seq data. Nat Commun, 2022, 13(1): E192..
29. Jin K, Li B, Yan H, et al. Imputing dropouts for single-cell RNA sequencing based on multi-objective optimization. Bioinformatics, 2022, 38(12): 3222-3230..
30. Su Y, Wang F, Zhang S, et al. scWMC: weighted matrix completion-based imputation of scRNA-seq data via prior subspace information. Bioinformatics, 2022, 38(19): 4537-4545..
31. Chi W, Deng M. Sparsity-penalized stacked denoising autoencoders for imputing single-cell RNA-seq data. Genes (Basel), 2020, 11(5): E532..
32. Chen S, Yan X, Zheng R, et al. Bubble: a fast single-cell RNA-seq imputation using an autoencoder constrained by bulk RNA-seq data. Brief Bioinform, 2023, 24(1): Ebbac580..
33. Eraslan G, Simon L M, Mircea M, et al. Single-cell RNA-seq denoising using a deep count autoencoder. Nat Commun, 2019, 10: E390..
34. Wu X, Zhou Y. GE-Impute: graph embedding-based imputation for single-cell RNA-seq data. Brief Bioinform, 2022, 23(5): Ebbac313..
35. Wu W, Liu Y, Dai Q, et al. G2S3: A gene graph-based imputation method for single-cell RNA sequencing data. PLoS Comput Biol, 2021, 17(5): Ee1009029..
36. Talwar D, Mongia A, Sengupta D, et al. AutoImpute: Autoencoder based imputation of single-cell RNA-seq data. Sci Rep, 2018, 8: E16329..
37. Arisdakessian C, Poirion O, Yunits B, et al. DeepImpute: an accurate, fast, and scalable deep neural network method to impute single-cell RNA-seq data. Genome Biol, 2019, 20(1): E211..
38. Badsha M B, Li R, Liu B, et al. Imputation of single-cell gene expression with an autoencoder neural network. Quant Biol, 2020, 8(1): 78-94..
39. Li H, Brouwer C R, Luo W. A universal deep neural network for in-depth cleaning of single-cell RNA-Seq data. Nat Commun, 2022, 13(1): E1901..
40. Karikomi M, Zhou P, Nie Q. DURIAN: an integrative deconvolution and imputation method for robust signaling analysis of single-cell transcriptomics data. Brief Bioinform, 2022, 23(4): Ebbac223..
41. Li X, Li S, Huang L, et al. High-throughput single-cell RNA-seq data imputation and characterization with surrogate-assisted automated deep learning. Brief Bioinform, 2022, 23(1): Ebbab368..
42. Islam M T, Wang J Y, Ren H, et al. Leveraging data-driven self-consistency for high-fidelity gene expression recovery. Nat Commun, 2022, 13(1): E7142..
43. Xu C, Cai L, Gao J. An efficient scRNA-seq dropout imputation method using graph attention network. BMC Bioinformatics, 2021, 22(1): E582..
44. Rao J, Zhou X, Lu Y, et al. Imputing single-cell RNA-seq data by combining graph convolution and autoencoder neural networks. iScience, 2021, 24(5): E102393..
45. Wang J, Ma A, Chang Y, et al. scGNN is a novel graph neural network framework for single-cell RNA-Seq analyses. Nat Commun, 2021, 12(1): E1882..
46. Liu J, Pan Y, Ruan Z, et al. SCDD: a novel single-cell RNA-seq imputation method with diffusion and denoising. Brief Bioinform, 2022, 23(5): Ebbac398..
47. Patruno L, Maspero D, Craighero F, et al. A review of computational strategies for denoising and imputation of single-cell transcriptomic data. Brief Bioinform, 2021, 22(4): Ebbaa222..
48. Wang M, Gan J, Han C, et al. Imputation methods for scRNA sequencing data. Appl Sci, 2022, 12(20): E10684..

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