Recent advances in single-cell technologies have enabled high-throughput molecular profiling of cells across modalities and locations. Single-cell transcriptomics data can now be complemented by chromatin accessibility, surface protein expression, adaptive immune receptor repertoire profiling and spatial information. The increasing availability of single-cell data across modalities has motivated the development of novel computational methods to help analysts derive biological insights.
In recent years, spatial genomics methods have been rapidly developing, addressing the limitation of single-cell sequencing that lacks spatial information. The detection results more accurately reflect the original tissue morphology, which is of great significance to life science research. Additionally, we can analyze information such as gene expression, protein expression, and metabolites in tissues through spatial genomics detection. This can be combined with H&E staining and traditional pathology results to bring deeper insights into disease research.
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