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. 2020 Sep 17;21:411. doi: 10.1186/s12859-020-03722-z

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© The Author(s) 2020

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

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Fig. 1 — PAWER pipeline. Raw GPR files are uploaded to PAWER (1), then the system proceeds to identifying foreground and background intensities and a panel of control proteins that can be used for normalisation (2). Robust linear model is then used to estimate and remove the technical artifacts associated with each array and array block (3). Normalised data is then combined with sample metadata (4) to produce a list of differentially expressed proteins (5). PAWER is linked with two other tools (g:Profiler and ClustVis) to enable additional analysis, namely: protein enrichment analysis. The figure was generated in Keynote, version 10.1 (6913)