A web server for comparative analysis of single-cell RNA-seq data

Amir Alavi, Matthew Ruffalo, Aiyappa Parvangada, Zhilin Huang and Ziv Bar-Joseph ()
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Amir Alavi: Carnegie Mellon University
Matthew Ruffalo: Carnegie Mellon University
Aiyappa Parvangada: Carnegie Mellon University
Zhilin Huang: Carnegie Mellon University
Ziv Bar-Joseph: Carnegie Mellon University

Nature Communications, 2018, vol. 9, issue 1, 1-11

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.

Date: 2018
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DOI: 10.1038/s41467-018-07165-2

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