An in-depth benchmark framework for evaluating single cell RNA-seq dropout imputation methods and the development of an improved algorithm afMF

Jinghan Huang; Anson C. M. Chow; Nelson L. S. Tang; Sheung Chi Phillip Yam

doi:10.1002/ctm2.70283

Clinical and Translational Medicine ›› 2025, Vol. 15 ›› Issue (4) : e70283 DOI: 10.1002/ctm2.70283

LETTER TO THE JOURNAL

An in-depth benchmark framework for evaluating single cell RNA-seq dropout imputation methods and the development of an improved algorithm afMF

Author information +

History +

PDF

Cite this article

Download citation ▾

Jinghan Huang, Anson C. M. Chow, Nelson L. S. Tang, Sheung Chi Phillip Yam. An in-depth benchmark framework for evaluating single cell RNA-seq dropout imputation methods and the development of an improved algorithm afMF. Clinical and Translational Medicine, 2025, 15(4): e70283 DOI:10.1002/ctm2.70283

登录浏览全文

4963

注册一个新账户忘记密码

References

Publishing order | Descend order by publishing year | Descend order by cited within

[1]	Jiang R, Sun T, Song D, Li JJ. Statistics or biology: the zero-inflation controversy about scRNA-seq data. Genome Biol. 2022; 23(1): 31.

[2]	Brooks TG, Lahens NF, Mrčela A, Grant GR. Challenges and best practices in omics benchmarking. Nat Rev Genet. 2024. Published online January 12.

[3]	Linderman GC, Zhao J, Roulis M, et al. Zero-preserving imputation of single-cell RNA-seq data. Nat Commun. 2022; 13(1): 192.

[4]	Lopez R, Regier J, Cole MB, Jordan MI, Yosef N. Deep generative modeling for single-cell transcriptomics. Nat Methods. 2018; 15(12): 1053-1058.

[5]	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.

[6]	Qiu P. Embracing the dropouts in single-cell RNA-seq analysis. Nat Commun. 2020; 11(1): 1169.

[7]	Hou W, Ji Z, Ji H, Hicks SC. A systematic evaluation of single-cell RNA-sequencing imputation methods. Genome Biol. 2020; 21(1): 218.

[8]	Dai C, Jiang Y, Yin C, et al. scIMC: a platform for benchmarking comparison and visualization analysis of scRNA-seq data imputation methods. Nucleic Acids Res. 2022; 50(9): 4877-4899.

[9]	Cheng Y, Ma X, Yuan L, Sun Z, Wang P. Evaluating imputation methods for single-cell RNA-seq data. BMC Bioinformatics. 2023; 24(1): 302.

[10]	Kim TH, Zhou X, Chen M. Demystifying “drop-outs” in single-cell UMI data. Genome Biol. 2020; 21(1): 196.

[11]	Clarke ZA, Andrews TS, Atif J, et al. Tutorial: guidelines for annotating single-cell transcriptomic maps using automated and manual methods. Nat Protoc. 2021; 16(6): 2749-2764.

RIGHTS & PERMISSIONS

2025 The Author(s). Clinical and Translational Medicine published by John Wiley & Sons Australia, Ltd on behalf of Shanghai Institute of Clinical Bioinformatics.