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

Authors

903

  1. JF
    Jort F. Gemmeke

    KU Leuven, Belgium

    5 publications · 172 citations

    5

    h-index

  2. XZ
    Xiao-Fei Zhang

    Central China Normal University, China

    3 publications · 96 citations

    3

    h-index

  3. JW
    Juexin Wang

    University of Missouri, United States

    3 publications · 495 citations

    3

    h-index

  4. HY
    Hong Yan

    City University of Hong Kong, Hong Kong SAR China

    3 publications · 96 citations

    3

    h-index

  5. QM
    Qin Ma

    The Ohio State University, United States

    3 publications · 495 citations

    3

    h-index

1–5 of 903

Venues

  1. Bioinformatics

    16 publications · 1.3K citations

    15

    h-index

  2. bioRxiv (Cold Spring Harbor Laboratory)

    Repository · 13 publications · 569 citations

    12

    h-index

  3. BMC Bioinformatics

    8 publications · 805 citations

    8

    h-index

  4. Genome biology

    7 publications · 1.5K citations

    7

    h-index

  5. Briefings in Bioinformatics

    7 publications · 200 citations

    6

    h-index

1–5