A5049112462- Cancer Genomics and Diagnostics
- Single-cell and spatial transcriptomics
- Cell Image Analysis Techniques
Institute of Basic Medical Sciences of the Chinese Academy of Medical Sciences
2022-2024
Abstract Integrating single-cell datasets produced by multiple omics technologies is essential for defining cellular heterogeneity. Mosaic integration, in which different share only some of the measured modalities, poses major challenges, particularly regarding modality alignment and batch effect removal. Here, we present a deep probabilistic framework mosaic integration knowledge transfer (MIDAS) multimodal data. MIDAS simultaneously achieves dimensionality reduction, imputation correction...
A bstract Rapidly developing single-cell multi-omics sequencing technologies generate increasingly large bodies of multimodal data. Integrating data from different technologies, i.e . mosaic data, permits larger-scale investigation with more modalities and can help to better reveal cellular heterogeneity. However, integration involves major challenges, particularly regarding modality alignment batch effect removal. Here we present a deep probabilistic framework for the knowledge transfer...