New Statistical and Computational methods for Spatial transcriptomics data

Single-cell genome sequencing techniques have recently emerged, enabling high-throughput transcriptome profiling at the level of individual cells. Among these, spatial transcriptomics stands out as a groundbreaking molecular profiling approach that measures gene activity within the precise spatial context of tissue samples, effectively mapping where specific gene expression occurs. Research leveraging these technologies has accelerated the discovery of novel cell types, clarified cellular heterogeneity, revealed gene regulatory mechanisms, and provided insights into cell developmental dynamics. These advances have driven significant discoveries in the study of complex and aggressive human diseases, including Alzheimer’s disease and various cancers.Despite the enormous promise of spatial transcriptomics technologies for advancing disease research, researchers face substantial challenges in quantitative analysis, from data preprocessing through to downstream data analysis and data integration. There is an urgent need for novel analytical strategies to fully capitalize on the rich information provided by these large-scale datasets. Our goal is to address a range of analytical challenges presented by complex spatial transcriptomics data, such as:• Quality control and preprocessing of spatial transcriptomics data• Annotation of single-cell and tissue-level characteristics in spatial datasets• Detection of hidden cell subgroups and identification of key biomarkers• Integration of spatial transcriptomics with single-cell RNA sequencing data and clinical data Topic editors: Wei Vivian Li, Dongyuan Song, Jianhua Hu Source: https://www.frontiersin.org/research-topics/75526/new-statistical-and-computational-methods-for-spatial-transcriptomics-data