Resolving tissue complexity by multimodal spatial omics modeling with MISO

• Published in: Nature methods Vol. 22; no. 3; pp. 530 - 538
• Main Authors: Coleman, Kyle, Schroeder, Amelia, Loth, Melanie, Zhang, Daiwei, Park, Jeong Hwan, Sung, Ji-Youn, Blank, Niklas, Cowan, Alexis J., Qian, Xuyu, Chen, Jianfeng, Jiang, Jiahui, Yan, Hanying, Samarah, Laith Z., Clemenceau, Jean R., Jang, Inyeop, Kim, Minji, Barnfather, Isabel, Rabinowitz, Joshua D., Deng, Yanxiang, Lee, Edward B., Lazar, Alexander, Gao, Jianjun, Furth, Emma E., Hwang, Tae Hyun, Wang, Linghua, Thaiss, Christoph A., Hu, Jian, Li, Mingyao
• Format: Journal Article
• Language: English
• Published: New York Nature Publishing Group US 01.03.2025; Nature Publishing Group
• Subjects: 631/114/1305; 631/114/2415; Algorithms; Animals; Bioinformatics; Biological Microscopy; Biological Techniques; Biomedical and Life Sciences; Biomedical Engineering/Biotechnology; Cluster Analysis; Clustering; Complexity; Computational Biology - methods; Computational efficiency; Datasets; Epigenetics; Feature extraction; Gene expression; Gene Expression Profiling - methods; Genomics - methods; Histology; Humans; Life Sciences; Localization; Medical research; Metabolomics; Metabolomics - methods; Modelling; Proteomics; Software; Spatial analysis; Spatial data; Spatial discrimination; Spatial resolution; Tissues

Spatial molecular profiling has provided biomedical researchers valuable opportunities to better understand the relationship between cellular localization and tissue function. Effectively modeling multimodal spatial omics data is crucial for understanding tissue complexity and underlying biology. Fu...