AI Article Synopsis
- Single-cell RNA-seq is a technique that measures biological differences in individual cells, allowing researchers to study both distinct cell types and gradual transitions between them.
- Partition-based graph abstraction (PAGA) creates a visual map connecting these cell types and transitions, which helps maintain the overall structure of the data while enabling analysis at various levels of detail.
- The method has been effectively applied to analyze complex datasets from different biological sources, including hematopoietic datasets and vertebrate models, demonstrating its efficiency in handling large amounts of data.
Article Abstract
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.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6425583 | PMC |
| http://dx.doi.org/10.1186/s13059-019-1663-x | DOI Listing |
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