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While previous computational methods have relied on gene expression information alone for clustering single-cell populations, more recent methods have begun to leverage spatial location and histology information to improve cell clustering and cell-type identification. In this study, using seven semi-synthetic datasets with real spatial locations, simulated gene expression and histology images as well as ground truth cell-type labels, we evaluate 15 clustering methods based on clustering accuracy, robustness to data variation and input parameters, computational efficiency, and software usability. Our analysis demonstrates that even though incorporating the additional spatial and histology information leads to increased accuracy in some datasets, it does not consistently improve clustering compared with using only gene expression data. Our results indicate that for the clustering of spatial transcriptomics data, there are still opportunities to enhance the overall accuracy and robustness by improving information extraction and feature selection from spatial and histology data.<\/jats:p>","DOI":"10.1093\/bib\/bbac475","type":"journal-article","created":{"date-parts":[[2022,11,22]],"date-time":"2022-11-22T01:11:29Z","timestamp":1669079489000},"source":"Crossref","is-referenced-by-count":67,"title":["Benchmarking cell-type clustering methods for spatially resolved transcriptomics data"],"prefix":"10.1093","volume":"24","author":[{"given":"Andrew","family":"Cheng","sequence":"first","affiliation":[{"name":"Department of Computer Science, Rutgers, The State University of New Jersey , 110 Frelinghuysen Road, Piscataway, 08854, NJ , USA"}]},{"given":"Guanyu","family":"Hu","sequence":"additional","affiliation":[{"name":"Department of Statistics, University of Missouri-Columbia , 146 Middlebush Hall, Columbia, 65211, MO , USA"}]},{"given":"Wei Vivian","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Biostatistics and Epidemiology , Rutgers School of Public Health, , 683 Hoes Lane West, Piscataway, 08854, NJ , USA"},{"name":"Rutgers, The State University of New Jersey , Rutgers School of Public Health, , 683 Hoes Lane West, Piscataway, 08854, NJ , USA"},{"name":"Department of Statistics, University of California , Riverside, 900 University Ave., Riverside, 92521, CA , USA"}]}],"member":"286","published-online":{"date-parts":[[2022,11,21]]},"reference":[{"issue":"1","key":"2023022309490179400_","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1038\/s41592-020-01038-7","article-title":"Spatially resolved transcriptomics adds a new dimension to genomics","volume":"18","author":"Larsson","year":"2021","journal-title":"Nat 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