Extended Data Fig. 5: Robustness of GLUE feature embeddings.
From: Multi-omics single-cell data integration and regulatory inference with graph-linked embedding
Consistency of feature embeddings as defined by the conservation of feature-feature cosine similarity (Methods), under a, different hyperparameter settings (n=4 repeats with different model random seeds), b, different prior knowledge corruption rates (n=8 repeats with different corruption random seeds), and c, different number of subsampled cells (n=8 repeats with different subsampling random seeds). The error bars indicate mean ± s.d. Feature embeddings are robust across all hyperparameters except for \(\lambda _{{{\mathcal{G}}}}\), which directly controls the contribution of guidance graph. Consistency also remains high (> 0.8) with up to 40% of prior knowledge corrupted, and a minimal of ~4,000 subsampled cells.
