Our study identifies reference-free ‘absolute’ feature quantification as the root cause of irreproducibility in multi-omics measurement and data integration and establishes the advantages of ratio-based multi-
Several methods for single-cell multimodal integration have recently been presented. Most of them have been proposed for the integration of bimodal data15,16,17,18,19,20,21,22,23. Fewer trimodal integration methods have been developed. MOFA+24has been proposed for trimodal integration with complet...
the sparsity of each group was set the same in bothXandY. The sample size isn. To correlate the subset of variables inXwith the subset of variables inY, we first set a latent variable ϒ
Medical data are diverse-not only because of the variety of modalities, dimensionality, and characteristics in general, but also within a given protocol due to factors such as acquisition differences, medical device brand, or local demographics. FL can help address specific sources of bias through ...
integration fashion, with two different algorithms. Since we were interested in high accuracy in class patient subtyping, we used prior information as a new view in the integration process. We found that the integrative clustering outperforms the single view approaches on all the datasets. We also...
types, co-projection (joint dimensionality reduction) methods are efficient approaches [9,10,11,12,13,14,15,16,17,18]. Co-projection methods came into focus of data integration analysis due to their prominent ability to integrate large-scale, diverse, and heterogeneous biomedical data. For ...
Easier data integration.Integrating data from different sources through preprocessing creates a cohesive view. This reveals relationships and patterns that would otherwise be hidden in fragmented data, and it enables deeper analysis and more informed decision-making. ...
The increasing demand for data with high spatial and temporal resolutions has led to the use of a large number of sensors in infrastructure health monitoring systems, and consequently to large datasets with high dimensionality. Handling large datasets can become extremely cumbersome, rendering existing ...
Online single-cell data integration through projecting heterogeneous datasets into a common cell-embedding space Article Open access 17 October 2022 Batch alignment of single-cell transcriptomics data using deep metric learning Article Open access 21 February 2023 Paired single-cell multi-omics data...
Fig. 5: Integration of a multi-omics human cell atlas. a,b, UMAP visualizations of the integrated cell embeddings, colored by omics layers (a) and cell types (b). The pink circles highlight cells labeled as ‘Excitatory neurons’ in scRNA-seq but ‘Astrocytes’ in scATAC-seq. The blue...