Biology subjects

Xia, C.-R.

Publications and source records attributed to Xia, C.-R..

3 recordsLinked to original sources

bioRxiv2024

High-fidelity disentangled cellular embeddings for large-scale heterogeneous spatial omics via DECIPHER

The functional role of a cell, shaped by the sophisticated interplay between its molecular identity and spatial context, is often obscured in current spatial modeling. Aiming to model large-scale heterogeneous spatial data in silico properly, DECIPHER produces high-fidelity disentangled embeddings, not only achieving superior performance in systematic benchmarks, but also empowering various real-world applications. We further demonstrated that DECIPHER is scalable to atlas-scale datasets, enabling global analysis which is largely infeasible to current state-of-the-arts.

bioinformatics↗

bioRxiv2024

Learning phenotype associated signature in spatial transcriptomics with PASSAGE

Spatially resolved transcriptomics (SRT) is poised to advance our understanding of cellular organization within complex tissues under various physiological and pathological conditions at unprecedented resolution. Despite the development of numerous computational tools that facilitate the automatic identification of statistically significant intra-/inter-slice patterns (like spatial domains), these methods typically operate in an unsupervised manner, without leveraging sample characteristics like physiological/pathological states. Here we present PASSAGE (Phenotype Associated Spatial Signature Analysis with Graph-based Embedding), a rationally-designed deep learning framework for characterizing phenotype-associated signatures across multiple heterogeneous spatial slices effectively. In addition to its outstanding performance in systematic benchmarks, we have demonstrated PASSAGEs unique capability in identifying sophisticated signatures in multiple real-world datasets. The full package of PASSAGE is available at https://github.com/gao-lab/PASSAGE.

bioinformatics↗

bioRxiv2023

Spatial-linked alignment tool (SLAT) for aligning heterogenous slices properly

Spatially resolved omics technologies reveal the spatial organization of cells in various biological systems. Integrative and comparative analyses of spatial omics data depend on proper slice alignment, which should take both omics profiles and spatial context into account. Here we propose SLAT (Spatially-Linked Alignment Tool), a graph-based algorithm for efficient and effective alignment of spatial omics data. Adopting a graph adversarial matching strategy, SLAT is the first algorithm capable of aligning heterogenous spatial data across distinct technologies and modalities. Systematic benchmarks demonstrate SLATs superior precision, robustness, and speed vis a vis existing methods. Applications to multiple real-world datasets further show SLATs utility in enhancing cell-typing resolution, integrating multiple modalities for regulatory inference, and mapping fine-scale spatial-temporal changes during development. The full SLAT package is available at https://github.com/gao-lab/SLAT.

bioinformatics↗