SCENERY: a web application for (causal) network reconstruction from cytometry data
KAUST DepartmentBiological and Environmental Sciences and Engineering (BESE) Division
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Online Publication Date2017-05-19
Print Publication Date2017-07-03
Permanent link to this recordhttp://hdl.handle.net/10754/623682
MetadataShow full item record
AbstractFlow 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/.
CitationPapoutsoglou G, Athineou G, Lagani V, Xanthopoulos I, Schmidt A, et al. (2017) SCENERY: a web application for (causal) network reconstruction from cytometry data. Nucleic Acids Research. Available: http://dx.doi.org/10.1093/nar/gkx448.
SponsorsThe research leading to these results has received funding from the European Research Council under the European Union's Seventh Framework Programme (FP/2007-2013) / ERC Grant Agreement n. 617393; CAUSALPATH - Next Generation Causal Analysis project. Funding for open access charge: ERC.
PublisherOxford University Press (OUP)
JournalNucleic Acids Research
Except where otherwise noted, this item's license is described as This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
- PTHGRN: unraveling post-translational hierarchical gene regulatory networks using PPI, ChIP-seq and gene expression data.
- Authors: Guan D, Shao J, Zhao Z, Wang P, Qin J, Deng Y, Boheler KR, Wang J, Yan B
- Issue date: 2014 Jul
- QuIN: A Web Server for Querying and Visualizing Chromatin Interaction Networks.
- Authors: Thibodeau A, Márquez EJ, Luo O, Ruan Y, Menghi F, Shin DG, Stitzel ML, Vera-Licona P, Ucar D
- Issue date: 2016 Jun
- The Multi-Q web server for multiplexed protein quantitation.
- Authors: Yu CY, Tsui YH, Yian YH, Sung TY, Hsu WL
- Issue date: 2007 Jul
- An open-source solution for advanced imaging flow cytometry data analysis using machine learning.
- Authors: Hennig H, Rees P, Blasi T, Kamentsky L, Hung J, Dao D, Carpenter AE, Filby A
- Issue date: 2017 Jan 1
- ZoomOut: Analyzing Multiple Networks as Single Nodes.
- Authors: Athanasiadis EI, Bourdakou MM, Spyrou GM
- Issue date: 2015 Sep-Oct