{"doi":"10.1002/ctm2.70119","title":"Statistical and computational methods for enabling the clinical and translational application of spatial transcriptomics","abstract":"Spatially resolved transcriptomics (SRT) is a collection of innovative genomic technologies that enable gene expression profiling within tissues while preserving spatial context.1 These technologies include imaging-based methods, such as single-molecule in situ hybridization (e.g. MERFISH, SeqFISH+, MERSCOPE, CosMx and 10x Xenium) and in situ sequencing (e.g. STARmap and Ex-seq), which typically achieve cellular or subcellular spatial resolution, as well as sequencing-based methods that offer either spot resolution (e.g. spatial transcriptomics, 10x Visium and Slide-seq), capturing mixtures of cells with heterogeneous cell types, or higher cellular or subcellular resolution (e.g. seq-scope, VisiumHD, stereo-seq and Open-ST). Together, these technologies transformed the study of tissue biology, providing unprecedented insights into the transcriptomic and cellular landscapes of complex tissues. SRT has the potential to revolutionize our understanding of the molecular mechanisms driving disease progression and facilitate the identification of diagnostic biomarkers, thus presenting transformative potential in clinical and translational applications. By capturing gene expression patterns across the entire tissue, SRT enables precise identification of transcripts, cell types and tissue areas that underlie disease aetiology, facilitates refined patient stratification, and supports the development of personalized treatments. For example, SRT has been instrumental in distinguishing tumour cells from their surrounding microenvironment, revealing pathways involved in tumour invasion and metastasis, and highlighting the spatial and transcriptomic heterogeneity across cancer subtypes, thus directly informing clinical oncology by advancing diagnostics and guiding targeted therapies.2-4 Additionally, spatial patterns of drug resistance or immune cell infiltration observed in patient tissue samples can inform treatment strategies and support individualized clinical decision-making.4, 5 Overall, SRT promises to bridge fundamental research with clinical applications, advancing our capacity to diagnose, treat and monitor complex diseases with unprecedented precision. Statistical and computational methods for SRT analysis are essential for harnessing its potential towards clinical applications (Figure 1). In this context, we briefly discuss several important analytical tasks that facilitate the use of SRT in clinical and translational medicine, along with the statistical and computational methods that enable these analyses. Spatially variable genes (SVGs), also known as spatially expressed genes, exhibit specific spatial expression patterns across tissue sections. These genes capture transcriptomic signatures that reflect the topographical organization of complex tissues, contributing to the spatial arrangement of tissue functions.6, 7 Common methods for identifying SVGs include SPARK6 and SPARK-X.7 SPARK uses an over-dispersed Poisson model with a generalized linear spatial component and incorporates multiple spatial kernels to directly model count data. In contrast, SPARK-X provides a non-parametric approach using a robust covariance test for sparse count data, delivering significant computational efficiency. Both methods ensure type I error control and high power, with SPARK offering particularly high statistical power, while SPARK-X achieves notable speed improvements. Both SPARK and SPARK-X have been widely applied in SRT data analysis, uncovering critical biomarkers essential for clinical diagnosis, characterizing disease subtypes, and monitoring disease progression across various conditions. Many identified SVGs serve as cell type markers, displaying spatial expression patterns that reflect the distribution of distinct cell types. However, a significant subset of SVGs, known as cell-type-specific SVGs (ct-SVGs), shows diverse spatial expression patterns within specific cell types.5 SPARK-X analysis has demonstrated that approximately half ","journal":"Clinical and Translational Medicine","year":2024,"id":452036,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":3,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9546,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2024-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":11254,"name":"Xiang Zhou","orcid":"0000-0002-4331-7599","position":1,"is_corresponding":false},{"id":89414,"name":"Peijun Wu","orcid":"0000-0001-9622-8541","position":0,"is_corresponding":true}],"reference_count":9,"raw_metadata":null,"created_at":"2026-07-19T02:02:50.407779Z","pmid":"39644148","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}