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arXiv · 2402.09755

Data Smoothing Filling Method based on ScRNA-Seq Data Zero-Value Identification

Linfeng Jiang·Yuan Zhu
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Abstract

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

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Linfeng Jiang, Yuan Zhu. 2024-02-15. Data Smoothing Filling Method based on ScRNA-Seq Data Zero-Value Identification. https://arxiv.org/abs/2402.09755

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