Sparse Data Imputation
Sparse Data Imputation is a computational method in single-cell transcriptomics that addresses the underrepresentation of gene expression measurements by recovering missing or dropout-induced zeros while preserving biological heterogeneity and technical variability inherent in sparse single-cell RNA-seq data.
253
Publications
15.6K
Citations
903
Authors
384
Institutions
Publications per year
2017–2026
161
Parents
253
Single-cell RNA-seq denoising using a deep count autoencoder
Gökçen Eraslan, Lukas M. Simon, Maria Mircea et al. · Nature Communications · 2019 · 1.2K citations · Full text
scGNN is a novel graph neural network framework for single-cell RNA-Seq analyses
Juexin Wang, Anjun Ma, Yuzhou Chang et al. · Nature Communications · 2021 · 458 citations · Full text
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384
Tongji University
Shanghai, China
7 h-index
18 publications
707 citations
Tsinghua University
Beijing, China
6 h-index
11 publications
764 citations

Pennsylvania State University
State College, United States
6 h-index
12 publications
717 citations

University of Minnesota
Minneapolis, United States
6 h-index
18 publications
2.9K citations
Yale University
New Haven, United States
6 h-index
21 publications
2.8K citations
University of Washington
Seattle, United States
5 h-index
17 publications
999 citations

Southeast University
Nanjing, China
5 h-index
12 publications
470 citations

Chinese Academy of Sciences
Beijing, China
5 h-index
14 publications
2K citations
1–8 of 384
16 publications · 1.3K citations
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Repository · 13 publications · 569 citations
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8 publications · 805 citations
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7 publications · 1.5K citations
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7 publications · 200 citations
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