ST-LDAW: A Topic-Model and Damped Weighted Least-Squares Method for Integrative Deconvolution of Single-Cell and Spatial Transcriptomics

Integrating single-cell RNA sequencing (scRNA-seq) with spatial transcriptomics (ST) enables the projection of cell-type–resolved transcriptional programs onto tissue architecture. However, existing integration methods are often unstable because spot-level inference is performed directly in high-dimensional gene space, where extreme sparsity, measurement noise, and strong multicollinearity among marker genes amplify estimation variance. As a result, inferred cell-type proportions may be dominated by a small subset of genes, making them highly sensitive to noise and systematically distorting rare or low-abundance cell types. Here, we present ST-LDAW, a computational framework explicitly designed to address these challenges. ST-LDAW combines probabilistic topic modeling with damped weighted least-squares optimization to enhance robustness at both the representation and inference levels. Topic-based modeling reduces dimensionality and mitigates gene-level noise by capturing coherent transcriptional programs, while damped weighting constrains the influence of unstable or low-confidence features, preventing variance inflation and overfitting during deconvolution. Benchmarking on simulated spatial mixtures demonstrates that ST-LDAW achieves a recall rate of 94% and an accuracy of 80%, surpassing existing regression-based and mapping-based methods in sensitivity and precision. These results highlight ST-LDAW's ability to reliably identify cell types in complex, sparse datasets, and its robust performance in handling rare or low-abundance cell types. Application to breast cancer ST data further reveals subtype-specific cellular composition, functional heterogeneity, intercellular communication patterns, and key epithelial hub genes