Denoising techniques and their role in enhancing plant single-cell RNA-seq data quality
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Single-cell RNA sequencing (scRNA-Seq) significantly advances our ability to explore complex biological systems by providing gene expression profiles at the cellular level. However, this technology is still vulnerable to technical noise, including dropout effects and insufficient detection sensitivity, which can obscure authentic biological signals. While various denoising techniques have been proposed to address these challenges, their effectiveness has primarily been assessed using human and mouse datasets, creating a notable gap in understanding how these methods apply to plant systems. This research develops a pipeline to thoroughly benchmark the study of three advanced denoising methodologies MAGIC, Deep Count Autoencoder (DCA), and scVI--applied to plant single-cell transcriptomics data. The study evaluates how denoising affects critical downstream analyses, such as clustering accuracy, the resolution of transcriptional subpopulations, and the ability to recover marker genes. Additionally, we consider computational factors such as runtime efficiency, scalability, and reproducibility, which are crucial for integrating these methods into plant research workflows. In contrast to studies that prioritize marker gene discovery, this research positions denoising as an essential process for improving data quality and interpretability within plant scRNA-seq workflows. The findings create a replicable framework for benchmarking denoising methods in non-model organisms and highlight specific trade-offs researchers must consider when selecting a denoising strategy. It also offers options for automatic hyperparameter tuning models like DCA and SCVI.
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M.S.