Semantic Scholar Open Access 2019 1394 sitasi

PAGA: graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells

F. Wolf F. Hamey M. Plass Jordi Solana Joakim S. Dahlin +4 lainnya

Lihat Sumber DOI

Abstrak

Single-cell RNA-seq quantifies biological heterogeneity across both discrete cell types and continuous cell transitions. Partition-based graph abstraction (PAGA) provides an interpretable graph-like map of the arising data manifold, based on estimating connectivity of manifold partitions (https://github.com/theislab/paga). PAGA maps preserve the global topology of data, allow analyzing data at different resolutions, and result in much higher computational efficiency of the typical exploratory data analysis workflow. We demonstrate the method by inferring structure-rich cell maps with consistent topology across four hematopoietic datasets, adult planaria and the zebrafish embryo and benchmark computational performance on one million neurons.

Topik & Kata Kunci

Biology Medicine

Penulis (9)

F. Wolf

F. Hamey

M. Plass

Jordi Solana

Joakim S. Dahlin

B. Göttgens

N. Rajewsky

L. Simon

Fabian J Theis

Format Sitasi

APA MLA BibTeX

Wolf, F., Hamey, F., Plass, M., Solana, J., Dahlin, J.S., Göttgens, B. et al. (2019). PAGA: graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells. https://doi.org/10.1186/s13059-019-1663-x

Akses Cepat

Lihat di Sumber doi.org/10.1186/s13059-019-1663-x

Informasi Jurnal

Tahun Terbit: 2019
Bahasa: en
Total Sitasi: 1394×
Sumber Database: Semantic Scholar
DOI: 10.1186/s13059-019-1663-x
Akses: Open Access ✓