AI RESEARCH
A Large-Scale Comparative Analysis of Imputation Methods for Single-Cell RNA Sequencing Data
arXiv CS.LG
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ArXi:2603.24626v1 Announce Type: cross Single-cell RNA sequencing (scRNA-seq) is inherently affected by sparsity caused by dropout events, in which expressed genes are recorded as zeros due to technical limitations. These artifacts distort gene expression distributions and can compromise downstream analyses. Numerous imputation methods have been proposed to address this, and these methods encompass a wide range of approaches from traditional statistical models to recently developed deep learning (DL)-based methods.