A web server for comparative analysis of single-cell RNA-seq data. Issue 1 (December 2018)

• Record Type: Journal Article
• Authors: Alavi, Amir; Ruffalo, Matthew; Parvangada, Aiyappa; Huang, Zhilin; Bar-Joseph, Ziv
• Is Part Of: Nature communications. Volume 9:Issue 1(2018)
• Page Start: 1
• Page End: 11
• Publication Date: 2018-12
• Subjects: Biology -- Periodicals; Physical sciences -- Periodicals; 505
• DOI: 10.1038/s41467-018-07165-2
• Languages: English
• ISSNs: 2041-1723

Abstract

Abstract Single cell RNA-Seq (scRNA-seq) studies profile thousands of cells in heterogeneous environments. Current methods for characterizing cells perform unsupervised analysis followed by assignment using a small set of known marker genes. Such approaches are limited to a few, well characterized cell types. We developed an automated pipeline to download, process, and annotate publicly available scRNA-seq datasets to enable large scale supervised characterization. We extend supervised neural networks to obtain efficient and accurate representations for scRNA-seq data. We apply our pipeline to analyze data from over 500 different studies with over 300 unique cell types and show that supervised methods outperform unsupervised methods for cell type identification. A case study highlights the usefulness of these methods for comparing cell type distributions in healthy and diseased mice. Finally, we present scQuery, a web server which uses our neural networks and fast matching methods to determine cell types, key genes, and more. Publicly available single cell RNA-seq datasets represent valuable resources for comparative and meta-analysis. Here, the authors develop scQuery, a web server integrating over 500 different studies with over 300 unique cell types for comparative analysis of existing and new scRNA-seq data.