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| Item Type: | Article |
|---|---|
| Title: | Compound-SNE: comparative alignment of t-SNEs for multiple single-cell omics data visualization |
| Creators Name: | Cess, C.G. and Haghverdi, L. |
| Abstract: | One of the first steps in single-cell omics data analysis is visualization, which allows researchers to see how well-separated cell-types are from each other. When visualizing multiple datasets at once, data integration/batch correction methods are used to merge the datasets. While needed for downstream analyses, these methods modify features space (e.g. gene expression)/PCA space in order to mix cell-types between batches as well as possible. This obscures sample-specific features and breaks down local embedding structures that can be seen when a sample is embedded alone. Therefore, in order to improve in visual comparisons between large numbers of samples (e.g. multiple patients, omic modalities, different time points), we introduce Compound-SNE, which performs what we term a soft alignment of samples in embedding space. We show that Compound-SNE is able to align cell-types in embedding space across samples, while preserving local embedding structures from when samples are embedded independently. |
| Keywords: | Data Integration, Data Visualization, Soft Alignment, Multi-Modal, Multi-View, Single-Cell Omics Data |
| Source: | Bioinformatics |
| ISSN: | 1367-4803 |
| Publisher: | Oxford University Press |
| Page Range: | btae471 |
| Date: | 25 July 2024 |
| Official Publication: | https://doi.org/10.1093/bioinformatics/btae471 |
| PubMed: | View item in PubMed |
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