Data Smoothing Filling Method based on ScRNA-Seq Data Zero-Value Identification
<|reference_start|>Data Smoothing Filling Method based on ScRNA-Seq Data Zero-Value Identification: Single-cell RNA sequencing (scRNA-seq) determines RNA expression at single-cell resolution. It provides a powerful tool for studying immunity, regulation, and other life activities of cells. However, due to the limitations of the sequencing technique, the scRNA-seq data are represented with sparsity, whichcontains missing gene values, i.e., zero values, called dropout. Therefore, it is necessary to impute missing values before analyzing scRNA-seq data. However, existing imputation computation methods often only focus on the identification of technical zeros or imputing all zeros based on cell similarity. This study proposes a new method (SFAG) to reconstruct the gene expression relationship matrix by usinggraph regularization technology to preserve the high-dimensional manifold information of the data, andto mine the relationship between genes and cells in the data, and then uses a method of averaging the clustering results to fill in the identified technical zeros. Experimental results show that SFAGcan helpimprove downstream analysis and reconstruct cell trajectory<|reference_end|>
@article{jiang2024data,
    title={Data Smoothing Filling Method based on ScRNA-Seq Data Zero-Value
  Identification},
    author={Linfeng Jiang and Yuan Zhu},
    journal={arXiv preprint arXiv:2402.09755},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.09755},
    primaryClass={q-bio.GN cs.IT math.IT}
}