Article ; Online: Deep generative model embedding of single-cell RNA-Seq profiles on hyperspheres and hyperbolic spaces.
2021 Volume 12, Issue 1, Page(s) 2554
Abstract: Single-cell RNA-Seq (scRNA-seq) is invaluable for studying biological systems. Dimensionality reduction is a crucial step in interpreting the relation between cells in scRNA-seq data. However, current dimensionality reduction methods are often confounded ...
| Abstract | Single-cell RNA-Seq (scRNA-seq) is invaluable for studying biological systems. Dimensionality reduction is a crucial step in interpreting the relation between cells in scRNA-seq data. However, current dimensionality reduction methods are often confounded by multiple simultaneous technical and biological variability, result in "crowding" of cells in the center of the latent space, or inadequately capture temporal relationships. Here, we introduce scPhere, a scalable deep generative model to embed cells into low-dimensional hyperspherical or hyperbolic spaces to accurately represent scRNA-seq data. ScPhere addresses multi-level, complex batch factors, facilitates the interactive visualization of large datasets, resolves cell crowding, and uncovers temporal trajectories. We demonstrate scPhere on nine large datasets in complex tissue from human patients or animal development. Our results show how scPhere facilitates the interpretation of scRNA-seq data by generating batch-invariant embeddings to map data from new individuals, identifies cell types affected by biological variables, infers cells' spatial positions in pre-defined biological specimens, and highlights complex cellular relations. |
|---|---|
| MeSH term(s) | Animals ; Colon ; Computational Biology/methods ; Epithelial Cells ; Gene Expression Profiling/methods ; Humans ; Machine Learning ; RNA-Seq/methods ; Sequence Analysis, RNA/methods ; Single-Cell Analysis/methods |
| Language | English |
| Publishing date | 2021-05-05 |
| Publishing country | England |
| Document type | Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't |
| ZDB-ID | 2553671-0 |
| ISSN | 2041-1723 ; 2041-1723 |
| ISSN (online) | 2041-1723 |
| ISSN | 2041-1723 |
| DOI | 10.1038/s41467-021-22851-4 |
| Database | MEDical Literature Analysis and Retrieval System OnLINE |
More links
Kategorien
Order via subito
This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.