SpecCG: Spectrally Enhanced Joint Cell–Gene Representation Learning for Spatial Transcriptomics

Spatial transcriptomics (ST) provides spatially resolved gene expression profiles for characterizing tissue organization and cellular heterogeneity. However, existing representation learning methods largely overlook global gene expression patterns obscured by noise and are predominantly cell-centric, thereby failing to adequately model tissue-scale expression organization and gene-level dependency structures. To overcome these limitations, we propose SpecCG, a Spectrally enhanced joint Cell–Gene representation learning framework for spatial transcriptomics. Specifically, SpecCG first performs adaptive spectral modulation on spatial maps of gene expression to enhance informative global expression patterns obscured by noisy observations. Further, it employs a multi-view architecture to integrate local spatial-transcriptional context with spectrally enhanced global expression patterns for discriminative cell representations, while modeling gene co-expression dependencies as complementary biological signals. Finally, SpecCG introduces a gene–cell joint decoding module to couple cell and gene embeddings through joint reconstruction, enabling reciprocal supervision and coordinated cell–gene representation learning. Extensive experiments on eight public ST datasets demonstrate that SpecCG consistently outperforms state-of-the-art baselines and yields spatially coherent, biologically interpretable domains.