<p>In single-cell experiments spanning diverse conditions, distinguishing variation specific to one condition (e.g., treatment) from shared or background variation (e.g., control) is critical for uncovering treatment-specific molecular responses. However, these studies typically yield ultra-high-dimensional data, necessitating effective dimension reduction for reliable biological interpretation. Contrastive dimension reduction methods address this challenge by identifying low-dimensional features enriched in a target dataset relative to a background dataset that captures shared variation. Despite their growing utility, the success of such methods critically depends on the choice of background, yet no formal criterion exists for evaluating or selecting backgrounds. To address this gap, we introduce BasCoD, a statistical testing framework based on spectral subspace inclusion theory, that enables rigorous evaluation and systematic selection of background datasets. Applying BasCoD across a range of single-cell datasets, we show that it effectively identifies suitable backgrounds, substantially improving the contrast and interpretability of the resulting target representations. We further demonstrate how BasCoD can guide the design of contrastive analyses in large-scale single-cell experiments conducted under heterogeneous conditions and elucidate potential interaction effects in perturbation studies.</p>