Xuhua Yan

Dec 4, 2024 | biorxiv.org | Xuhua Yan |Kok Siong Ang |Lynn van Olst |Alex Edwards

AbstractWith the advent of spatial multi-omics, we can mosaic integrate diverse datasets with partially overlapping modalities to construct consensus multi-modal spatial atlases of the source tissue. SpaMosaic is a spatial multi-omics mosaic integration tool that employs contrastive learning and graph neural networks to construct a modality-agnostic and batch-corrected latent space suited for analyses like spatial domain identification and imputing missing omes.

Dec 3, 2024 | biorxiv.org | Xuhua Yan |Jinmiao Chen |Ruiqing Zheng

AbstractThe integration of single-cell multi-omics datasets is critical for deciphering cellular heterogeneities. Mosaic integration, the most general integration task, poses a greater challenge regarding disparity in modality abundance across datasets. Here, we present ACE, a mosaic integration framework that assembles two types of strategies to handle this problem: modality-alignment based strategy (ACE-align) and regression-based strategy (ACE-spec).

Oct 3, 2024 | biorxiv.org | Kok Siong Ang |Lynn van Olst |Alex Edwards |Xuhua Yan

AbstractWith the advent of spatial multi-omics, we can mosaic integrate such datasets with partially overlapping modalities to construct higher dimensional views of the source tissue. SpaMosaic is a spatial multi-omics mosaic integration tool that employs contrastive learning and graph neural networks to construct a modality-agnostic and batch-corrected latent space suited for analyses like spatial domain identification and imputing missing omes.