References#
Yoav Benjamini and Yosef Hochberg. Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society: Series B (Methodological), 57(1):289–300, 1995.
Yoav Benjamini and Daniel Yekutieli. The control of the false discovery rate in multiple testing under dependency. Annals of Statistics, pages 1165–1188, 2001.
Arthur Gretton, Olivier Bousquet, Alex Smola, and Bernhard Schölkopf. Measuring statistical dependence with hilbert-schmidt norms. In International Conference on Algorithmic Learning Theory, 63–77. Springer, 2005.
Kevin Lebrigand, Joseph Bergenstråhle, Kim Thrane, Annelie Mollbrink, Konstantinos Meletis, Pascal Barbry, Rainer Waldmann, and Joakim Lundeberg. The spatial landscape of gene expression isoforms in tissue sections. Nucleic Acids Research, 51(8):e47, May 2023. doi:10.1093/nar/gkad169.
Huan Liu, Yongqiang Tang, and Hao Helen Zhang. A new chi-square approximation to the distribution of non-negative definite quadratic forms in non-central normal variables. Computational Statistics & Data Analysis, 53(4):853–856, 2009.
Junyoung Park, Changwon Yoon, Cheolwoo Park, and Jeongyoun Ahn. Kernel methods for radial transformed compositional data with many zeros. In International Conference on Machine Learning, 17458–17472. PMLR, 2022.
Jiayu Su, Jun Hou Fung, Haoyu Wang, Dian Yang, David A. Knowles, and Raul Rabadan. On the consistent and scalable detection of spatial patterns. arXiv preprint arXiv:2602.02825, 2026.
Jiayu Su, Yiming Qu, Megan Schertzer, Haochen Yang, Jiahao Jiang, Tenzin Lhakhang, Theodore M Nelson, Stella Park, Qiliang Lai, Xi Fu, Seung-won Choi, David A. Knowles, and Rabadan Raul. Mapping isoforms and regulatory mechanisms from spatial transcriptomics data with splisosm. Nature Biotechnology, pages 1–12, 2026.
Jiayu Su, Jean-Baptiste Reynier, Xi Fu, Guojie Zhong, Jiahao Jiang, Rydberg Supo Escalante, Yiping Wang, Luis Aparicio, Benjamin Izar, David A. Knowles, and Raul Rabadan. Smoother: a unified and modular framework for incorporating structural dependency in spatial omics data. Genome Biology, 24(1):291, December 2023. doi:10.1186/s13059-023-03138-x.
Kun Zhang, Jonas Peters, Dominik Janzing, and Bernhard Schölkopf. Kernel-based conditional independence test and application in causal discovery. arXiv preprint arXiv:1202.3775, 2012.
Jiaqiang Zhu, Shiquan Sun, and Xiang Zhou. SPARK-X: non-parametric modeling enables scalable and robust detection of spatial expression patterns for large spatial transcriptomic studies. Genome Biology, 22(1):184, June 2021. doi:10.1186/s13059-021-02404-0.