Supporting data for "Cross-modal contrastive learning decodes developmental regulatory features through chromatin potential analysis"

Emerging large-scale multimodal single-cell data jointly measures chromatin accessibility and transcription in the same cell, thus reconciling matched data paves integrated route for comprehensive regulatory analysis. <br> Here, we introduce Attune, a cross-modal contrastive learning framework to align paired gene expression and accessibility information. Systematic benchmarking shows Attune's superior performance for omics integration and gene expression prediction. We further introduce Transformer-based cross-modal attention over fine-tuned gene and peak embeddings to infer regulatory interaction and discover significant differential signals of cell subtypes. Applied to hair follicle maturation dataset, Attune reveals chromatin potential for bifunctional transcription factor Gli3 at the gene level. In addition, the paired representations determine transmitted states across neonatal and mature cell types of cortical neuron differentiation at the cell level. Taken together, Attune features a promising paradigm for regulatory inference across omics layers and allows for extending more complex omics analysis. <br>Attune offers a versatile framework for integrating gene expression and chromatin accessibility, enabling the inference of regulatory mechanisms and the prediction of gene expression from cross-modal data.