Linghua Wang

Jan 24, 2025 | nature.com | Kyle Coleman |Jeong Hwan Park |Hanying Yan |Isabel Barnfather |Joshua D. Rabinowitz |Yanxiang Deng | +5 more

Correction to: Nature Methods https://doi.org/10.1038/s41592-024-02574-2, published online 15 January 2025. In the version of the article initially published, the Data availability and Code availability sections were inadvertently omitted. These sections have now been included in the HTML and PDF versions of the article. About this articleColeman, K., Schroeder, A., Loth, M. et al. Author Correction: Resolving tissue complexity by multimodal spatial omics modeling with MISO. Nat Methods (2025).

Jan 15, 2025 | nature.com | Kyle Coleman |Jeong Hwan Park |Hanying Yan |Joshua D. Rabinowitz |Yanxiang Deng |Edward Lee | +5 more

AbstractSpatial 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.

Jan 9, 2025 | nature.com | Pavlos Msaouel |Rahul Sheth |Amishi Y. Shah |Amado J. Zurita |Giannicola Genovese |Minghao Dang | +3 more

AbstractWe conducted a phase I trial to determine the optimal dose of triplet therapy with the tyrosine kinase inhibitor sitravatinib plus nivolumab plus ipilimumab in 22 previously untreated patients with advanced clear cell renal cell carcinoma. The primary endpoint was safety.

Jul 4, 2024 | nature.com | Sang T. Kim |Sattva Neelapu |Amishi Y. Shah |Matthew Campbell |don gibbons |Tina Cascone | +10 more

Correction to: Nature Communications https://doi.org/10.1038/s41467-022-29539-3, published online 12 April 2022In this article the funding information for Jordan Kramer was omitted and should have read, ‘J.K. is supported by the CPRIT Research Training Award CPRIT Training Program (RP210028)’. The original article has been corrected.

Oct 7, 2023 | biorxiv.org | Jiahui Jiang |Linghua Wang |Jian Hu

AbstractThe recent advance of spatial transcriptomics (ST) technique provides valuable insights into the organization and interactions of cells within the tumor microenvironment (TME). While various analytical tools have been developed for tasks such as spatial clustering, spatially variable gene identification, and cell type deconvolution, most of them are general methods lacking consideration of histological features in spatial data analysis.