SCENERY: a web application for (causal) network reconstruction from cytometry data

Georgios Papoutsoglou, Giorgos Athineou, Vincenzo Lagani, Iordanis Xanthopoulos, Angelika Schmidt, Szabolcs Éliás, Jesper Tegner, Ioannis Tsamardinos

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

Flow and mass cytometry technologies can probe proteins as biological markers in thousands of individual cells simultaneously, providing unprecedented opportunities for reconstructing networks of protein interactions through machine learning algorithms. The network reconstruction (NR) problem has been well-studied by the machine learning community. However, the potentials of available methods remain largely unknown to the cytometry community, mainly due to their intrinsic complexity and the lack of comprehensive, powerful and easy-to-use NR software implementations specific for cytometry data. To bridge this gap, we present Single CEll NEtwork Reconstruction sYstem (SCENERY), a web server featuring several standard and advanced cytometry data analysis methods coupled with NR algorithms in a user-friendly, on-line environment. In SCENERY, users may upload their data and set their own study design. The server offers several data analysis options categorized into three classes of methods: data (pre)processing, statistical analysis and NR. The server also provides interactive visualization and download of results as ready-to-publish images or multimedia reports. Its core is modular and based on the widely-used and robust R platform allowing power users to extend its functionalities by submitting their own NR methods. SCENERY is available at scenery.csd.uoc.gr or http://mensxmachina.org/en/software/.
Original languageEnglish (US)
Pages (from-to)W270-W275
Number of pages1
JournalNucleic Acids Research
Volume45
Issue numberW1
DOIs
StatePublished - May 19 2017

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