Asset Details
Do you wish to reserve the book?
Deep generative model embedding of single-cell RNA-Seq profiles on hyperspheres and hyperbolic spaces
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
Deep generative model embedding of single-cell RNA-Seq profiles on hyperspheres and hyperbolic spaces
Do you wish to request the book?
Deep generative model embedding of single-cell RNA-Seq profiles on hyperspheres and hyperbolic spaces
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Journal Article
Deep generative model embedding of single-cell RNA-Seq profiles on hyperspheres and hyperbolic spaces
2021
Overview
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.
Single-cell RNA-seq allows the study of tissues at cellular resolution. Here, the authors demonstrate how deep learning can be used to gain biological insight from such data by accounting for biological and technical variability. Data exploration is improved by accurately visualizing cells on an interactive 3D surface.
Publisher
Nature Publishing Group UK,Nature Publishing Group,Nature Portfolio
