Single Cell Analytics Evolution
Single-cell analytics focuses on individual cells to study the unique characteristics and cellular heterogeneity which often gets masked in bulk analyses. It uses multi-omics techniques like scRNA-seq, single-cell ATAC-seq, proteomics, and metabolomics to reveal gene expression, chromatin accessibility, protein dynamics, and metabolic states. The applications range from the identification of rare cell types and subpopulations to cellular processes like development, disease progression, and drug responses. Single-cell analytics also integrates machine learning for pattern recognition and dynamic modeling to accelerate research in fields like personalized medicine, regenerative therapies, and drug discovery. (1)
Advancements in technologies like droplet-based sequencing, spatial transcriptomics, and multimodal profiling have accelerated the growth of single-cell genomics. These methods are capable of generating vast datasets containing millions of data points, each representing a unique combination of gene expression, spatial context, and other molecular characteristics. However, the analytical and visualization landscape for this data has struggled to keep pace with the scale and complexity of these datasets.
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