Rxivist logo

Integrated analysis of single cell transcriptomic data across conditions, technologies, and species

By Andrew Butler, Rahul Satija

Posted 18 Jul 2017
bioRxiv DOI: 10.1101/164889 (published DOI: 10.1038/nbt.4096)

Single cell RNA-seq (scRNA-seq) has emerged as a transformative tool to discover and define cellular phenotypes. While computational scRNA-seq methods are currently well suited for experiments representing a single condition, technology, or species, analyzing multiple datasets simultaneously raises new challenges. In particular, traditional analytical workflows struggle to align subpopulations that are present across datasets, limiting the possibility for integrated or comparative analysis. Here, we introduce a new computational strategy for scRNA-seq alignment, utilizing common sources of variation to identify shared subpopulations between datasets as part of our R toolkit Seurat. We demonstrate our approach by aligning scRNA-seq datasets of PBMCs under resting and stimulated conditions, hematopoietic progenitors sequenced across two profiling technologies, and pancreatic cell 'atlases' generated from human and mouse islets. In each case, we learn distinct or transitional cell states jointly across datasets, and can identify subpopulations that could not be detected by analyzing datasets independently. We anticipate that these methods will serve not only to correct for batch or technology-dependent effects, but also to facilitate general comparisons of scRNA-seq datasets, potentially deepening our understanding of how distinct cell states respond to perturbation, disease, and evolution. Availability: Installation instructions, documentation, and tutorials are available at http://www.satijalab.org/seurat

Download data

  • Downloaded 12,942 times
  • Download rankings, all-time:
    • Site-wide: 136 out of 83,521
    • In genomics: 24 out of 5,407
  • Year to date:
    • Site-wide: 2,683 out of 83,521
  • Since beginning of last month:
    • Site-wide: 6,042 out of 83,521

Altmetric data


Downloads over time

Distribution of downloads per paper, site-wide


PanLingua

Sign up for the Rxivist weekly newsletter! (Click here for more details.)


News