Deep generative model embedding of single-cell RNA-Seq profiles on hyperspheres and hyperbolic spaces
Name
s41467-021-22851-4.pdf
Description
Published version
Size
8.37 MB
Format
Adobe PDF
Checksum (MD5)
0813e5392e661897ca9f1a5982a01620
Author(s)
Ding, Jiarui
Regev, Aviv
Date Issued
2021
Journal
Nature Communications
Publisher
Springer Science and Business Media LLC
Version
Final published version
Abstract
AbstractSingle-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.
MIT Department
Massachusetts Institute of Technology. Department of Biology
Howard Hughes Medical Institute
Koch Institute for Integrative Cancer Research at MIT
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1038/s41467-021-22851-4
