Unveiling tissue heterogeneity through genomic interaction-encoded image representation of RNA-sequencing data.

Genomic sequencing is essential for both biomedical research and clinical practice. While single-cell RNA sequencing (scRNA-seq) provides insights into biological processes at the cellular level, bulk RNA sequencing remains widely used for its scalability and cost-effectiveness. To explore biological heterogeneity, research efforts have been made toward inferring single-cell-like cellular compositions from bulk samples, i.e., deconvolving bulk samples into multiple cell types. However, existing deconvolution methods face two major limitations: (1) reliance on predefined gene signature matrices without accounting for inter-sample variability and (2) susceptibility to noise within biological systems. Here, we propose a cellular-component analysis (CCA) framework by leveraging a genomic-interaction-encoded image representation of RNA-seq data for substantially improved pattern discovery. The framework incorporates sample-specific gene-expression variability and derives signature patterns by utilizing a convolutional variational autoencoder and Gaussian mixture model. An image-domain linear decomposition of bulk RNA-seq data based on these sample-specific, interpretable gene-signature patterns is then performed for CCA and other downstream tasks, such as cancer subtype classification and biomarker discovery. We demonstrate that the proposed technique improves decomposition accuracy by over 14.1% in average Pearson correlation compared to existing techniques by using both simulation and experimental datasets. This approach offers an effective solution for tissue heterogeneity analysis and lays a foundation for a range of clinical and biological applications.