PMID- 36523429 OWN - NLM STAT- PubMed-not-MEDLINE VI - 16 TI - Detection of differentially expressed genes in spatial transcriptomics data by spatial analysis of spatial transcriptomics: A novel method based on spatial statistics. PG - 1086168 CI - Copyright © 2022 Qiu, Li, Luo, Zhu, Wang and Jiang. LA - eng PT - Journal Article PL - Switzerland TA - Front Neurosci JT - Frontiers in neuroscience JID - 101478481 IS - 1662-4548 (Print) LID - 10.3389/fnins.2022.1086168 [doi] FAU - Qiu, Zhihua AU - Qiu Z AD - Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China. AD - Department of Neurology, The Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, China. FAU - Li, Shaojun AU - Li S AD - Department of Neurology, The Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, China. FAU - Luo, Ming AU - Luo M AD - Department of Neurology, The Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, China. FAU - Zhu, Shuanggen AU - Zhu S AD - Department of Neurology, People's Hospital of Longhua, Shenzhen, China. FAU - Wang, Zhijian AU - Wang Z AD - Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China. FAU - Jiang, Yongjun AU - Jiang Y AD - Department of Neurology, The Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, China. IS - 1662-453X (Linking) OTO - NOTNLM OT - DEGs OT - saSpatial OT - spatial statistics OT - spatial transcriptomics OT - stroke PMC - PMC9745188 LR - 20221217 DP - 2022 DEP - 20221129 AB - Background: Spatial transcriptomics (STs) simultaneously obtains the location and amount of gene expression within a tissue section. However, current methods like FindMarkers calculated the differentially expressed genes (DEGs) based on the classical statistics, which should abolish the spatial information. Materials and methods: A new method named spatial analysis of spatial transcriptomics (saSpatial) was developed for both the location and the amount of gene expression. Then saSpatial was applied to detect DEGs in both inter- and intra-cross sections. DEGs detected by saSpatial were compared with those detected by FindMarkers. Results: Spatial analysis of spatial transcriptomics was founded on the basis of spatial statistics. It was able to detect DEGs in different regions in the normal brain section. As for the brain with ischemic stroke, saSpatial revealed the DEGs for the ischemic core and penumbra. In addition, saSpatial characterized the genetic heterogeneity in the normal and ischemic cortex. Compared to FindMarkers, a larger number of valuable DEGs were found by saSpatial. Conclusion: Spatial analysis of spatial transcriptomics was able to effectively detect DEGs in STs data. It was a simple and valuable tool that could help potential researchers to find more valuable genes in the future research.