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

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

doi:10.1093/nar/gkx448

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/.

Highlights

Every process in the cell, ranging from proliferation and differentiation to cell survival and death, results from a sequence of molecular interactions
We present SCENERY, a web server devised to allow researchers to apply standard pre-processing, statistical analysis, advanced visualization methods and network reconstruction (NR) methods on single-cell cytometry data
SCENERY has been developed into a complete flow-/masscytometry data analysis online toolkit

Summary

Introduction

Every process in the cell, ranging from proliferation and differentiation to cell survival and death, results from a sequence of molecular interactions. SCENERY offers a wide range of data analysis methods including, (i) basic pre-processing methods, to allow users to transform, compensate and manually gate samples; (ii) univariate analysis methods such as regression and factor analysis and (iii) advanced machine learning methods for association and causal NR that identify interactions between the measured quantities.

Results

Conclusion