Abstract
Single-cell RNA sequencing (scRNA-seq) has revolutionized our understanding of cellular diversity by enabling the profiling of gene expression at the single-cell level. However, the analysis of scRNA-seq data presents unique challenges due to the high sparsity, noise, and complexity of the data. Computational methods are essential for processing, analyzing, and interpreting scRNA-seq data, and recent advancements in bioinformatics tools have significantly improved the resolution and scalability of these analyses. This article discusses the key computational approaches used in scRNA-seq data analysis, including quality control, normalization, dimensionality reduction, clustering, and differential expression analysis. We also explore the challenges of scRNA-seq analysis and the future directions of this rapidly evolving field.
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