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Abstract
Abstract
Single-cell multi-omics technologies enable profiling of several data-modalities from the same cell. We designed LIBRA, a Neural Network based framework, for learning translations between paired multi-omics profiles into a shared latent space. We demonstrate LIBRA to be state-of-the-art for multi-omics clustering. In addition, LIBRA is more robust with decreasing cell-numbers compared with existing tools. Training LIBRA on paired data-sets, LIBRA predicts multi-omic profiles using only a single data-modality from the same biological system.
Competing Interest Statement
The authors have declared no competing interest.
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