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However, due to the limitations of the current spatial techniques, the cellular information cannot be directly measured but instead spatial spots typically varying from a diameter of 0.2 to 100 \u00b5m are characterized. Therefore, it is vital to apply computational strategies for inferring the cellular composition within each spatial spot. The main objective of this review is to summarize the most recent progresses in estimating the exact cellular proportions for each spatial spot, and to prospect the future directions of this field.<\/jats:p>","DOI":"10.1093\/gpbjnl\/qzae057","type":"journal-article","created":{"date-parts":[[2024,8,7]],"date-time":"2024-08-07T23:57:34Z","timestamp":1723075054000},"source":"Crossref","is-referenced-by-count":7,"title":["Computational Strategies and Algorithms for Inferring Cellular Composition of Spatial Transcriptomics Data"],"prefix":"10.1093","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6414-6349","authenticated-orcid":false,"given":"Xiuying","family":"Liu","sequence":"first","affiliation":[{"name":"Changping Laboratory , Beijing 102206, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8558-5401","authenticated-orcid":false,"given":"Xianwen","family":"Ren","sequence":"additional","affiliation":[{"name":"Changping Laboratory , Beijing 102206, 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