ArticleScientific reports2022
A novel method for single-cell data imputation using subspace regression.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Imputing single-cell protein abundance in multiplex tissue imaging.Nature communications · 2025Article
- SAE-Impute: imputation for single-cell data via subspace regression and auto-encoders.BMC bioinformatics · 2024Article
- scGAA: a general gated axial-attention model for accurate cell-type annotation of single-cell RNA-seq data.Scientific reports · 2024Article
- [Imputation method for dropout in single-cell transcriptome data].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2023Review
- Improved downstream functional analysis of single-cell RNA-sequence data using DGAN.Scientific reports · 2023Article
- Imputation method for single-cell RNA-seq data using neural topic model.GigaScience · 2022Article
- scCAN: single-cell clustering using autoencoder and network fusion.Scientific reports · 2022Article
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Authors and funding
4 authors.
Funding
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Abstract
Recent advances in biochemistry and single-cell RNA sequencing (scRNA-seq) have allowed us to monitor the biological systems at the single-cell resolution. However, the low capture of mRNA material within individual cells often leads to inaccurate quantification of genetic material. Consequently, a significant amount of expression values are reported as missing, which are often referred to as dropouts. To overcome this challenge, we develop a novel imputation method, named single-cell Imputation via Subspace Regression (scISR), that can reliably recover the dropout values of scRNA-seq data. The scISR method first uses a hypothesis-testing technique to identify zero-valued entries that are most likely affected by dropout events and then estimates the dropout values using a subspace regression model. Our comprehensive evaluation using 25 publicly available scRNA-seq datasets and various simulation scenarios against five state-of-the-art methods demonstrates that scISR is better than other imputation methods in recovering scRNA-seq expression profiles via imputation. scISR consistently improves the quality of cluster analysis regardless of dropout rates, normalization techniques, and quantification schemes. The source code of scISR can be found on GitHub at https://github.com/duct317/scISR .
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