{Reference Type}: Journal Article
{Title}: scCross: a deep generative model for unifying single-cell multi-omics with seamless integration, cross-modal generation, and in silico exploration.
{Author}: Yang X;Mann KK;Wu H;Ding J;
{Journal}: Genome Biol
{Volume}: 25
{Issue}: 1
{Year}: 2024 Jul 29
暂无{DOI}: 10.1186/s13059-024-03338-z
{Abstract}: Single-cell multi-omics data reveal complex cellular states, providing significant insights into cellular dynamics and disease. Yet, integration of multi-omics data presents challenges. Some modalities have not reached the robustness or clarity of established transcriptomics. Coupled with data scarcity for less established modalities and integration intricacies, these challenges limit our ability to maximize single-cell omics benefits. We introduce scCross, a tool leveraging variational autoencoders, generative adversarial networks, and the mutual nearest neighbors (MNN) technique for modality alignment. By enabling single-cell cross-modal data generation, multi-omics data simulation, and in silico cellular perturbations, scCross enhances the utility of single-cell multi-omics studies.