Single-cell and spatial data processing and batch correction in cancer genomics

Single-cell and spatial omics technologies have transformed our ability to resolve tumor heterogeneity, immune infiltration, and the spatial architecture of the tumor microenvironment at unprecedented resolution. However, the value of these technologies depends heavily on rigorous data processing: batch effects arising from sequencing platforms, sample handling, and multi-site cohorts can obscure true biological signal and compromise downstream analyses, from cell-type annotation to trajectory inference and spatial domain identification. As single-cell and spatial datasets are increasingly integrated across studies and combined with bulk genomic and epigenomic data, robust computational strategies for normalization, batch correction, and cross-platform harmonization have become essential to generating reliable and reproducible insights into cancer biology.This Research Topic aims to advance methodological and applied research on the processing, correction, and integration of single-cell and spatial genomic data in cancer. We welcome contributions spanning computational method development, benchmarking studies, and applied analyses that use these approaches to uncover new insights into tumor genetics and the tumor immune microenvironment.We accept papers on, but not limited to:o Development and benchmarking of batch correction and integration methods for single-cell and spatial cancer genomic datao Cross-platform and cross-cohort harmonization strategies for multi-omics cancer datasetso Computational approaches to resolving tumor heterogeneity and immune infiltration from single-cell and spatial datao Impact of batch effects and correction choices on downstream biological conclusions, including neoantigen and mutational signature analyseso Integration of single-cell, spatial, and bulk genomic/epigenomic data for tumor microenvironment characterizationo Machine learning and statistical approaches for scalable processing of large-scale single-cell and spatial cancer cohortso Applied case studies using corrected single-cell/spatial data to identify novel cancer genomic or immunotherapy-relevant targets Topic editors: Rick Jansen, Mehdi Pirooznia, Tianzhong Yang Source: https://www.frontiersin.org/research-topics/85687/single-cell-and-spatial-data-processing-and-batch-correction-in-cancer-genomics