Across distinct experimental datasets, SRLST consistently outperforms existing methods in delineating cortical organization, identifying small discontinuous tissue compartments, and capturing complex intratumor heterogeneity.
Abstract
Abstract Motivation Spatial transcriptomics (ST) enables molecular profiling within native tissue architecture, yet accurate delineation of spatial domains in ST data is challenging, as it demands the coordinated integration of transcriptomic, spatial, and tissue histological information. Results We present SRLST, an unsupervised representation learning framework that holistically harmonize these three complementary data modalities to precisely uncover tissue organization. SRLST employs a dual-graph variational autoencoding strategy to jointly model spatial proximity and morphological relations, fusing these with gene-expression embeddings into a unified latent space. Across distinct experimental datasets, SRLST consistently outperforms existing methods in delineating cortical organization, identifying small discontinuous tissue compartments, and capturing complex intratumor heterogeneity. Availability and implementation The code implementation of the SRLST algorithm is available at https://github.com/lanbiolab/SRLST.
StKAN is introduced, a novel framework integrating Kolmogorov-Arnold Network with variational autoencoder to effectively model spatially resolved gene expression with graph attention network and shows strong potential for downstream analyses, offering deeper insights into disease pathology and tumor invasion.
Jing Lin, Ai-Jing Feng, Yan-Kun Cao et al.· Computational biology and ch...· 0 citations
Abstract Spatial transcriptomics enables high-resolution profiling of gene expression within intact tissue architecture, providing new opportunities to study the spatial organization of tissue structures and cell types. However, identifying spatial domains that are reproducible across samples and individuals remains ch...
Shi-Wei Fu, Han Li, Wei Vivian Li· Briefings in Bioinformatics· 0 citations
PaSTel is introduced, a hierarchical multimodal pretraining framework that integrates biological priors at three levels that consistently outperforms existing vision and vision-omics encoders, demonstrating that incorporating multiscale biological priors yields more informative and transferable representations for spat...
Azim Dehghani Amirabad, Jun-Chao Zhu, Pushpak Pati et al.· 0 citations
Spatial transcriptomics (STs) have become a valuable approach for understanding the growth and development of organisms. Despite the recent emergence of numerous ST models, accurately identifying spatial domains remains challenging owing to the trade-off between preserving local details and reducing noise. Here, we int...
SpaAlign is introduced, a novel two-stage framework for the robust integration of multiple omics and histological data that creates a unified semantic space, followed by a self-supervised clustering stage that enforces structural coherence.
Accurately identifying spatial domains based on spatial transcriptomics (ST) data can greatly promote our understanding of cellular composition and tissue organization. While graph neural networks (GNNs) have shown significant advancements in spatial clustering, they tend to be insensitive to noisy edges, leading to in...
Li-Hong Peng, Long Yang, Min Chen et al.· Genomics, Proteomics & Bioin...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
