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. 2016 Jul 1;13:176. doi: 10.1186/s12974-016-0642-3

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© The Author(s). 2016

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. 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.

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Fig. 3 — Heatmap of serum AQP4-antibody levels against AQP4-M23, AQP4-M1 and AQP4-M23 mutants (columns) at baseline. Rows are individual samples with patient IDs (Nr) and flow cytometry AQP4-M23 binding ratios (BR) shown at the right side. Data are shown as percent binding of AQP4-M23. Values range from blue (0 %) to white (50 %) to red (100 %). Columns were clustered according to their Pearson’s correlation coefficients, and rows were clustered according to their Eucledian distance (both average linkage). Two major antibody binding patterns were identified, a loop A-dependent pattern A (upper panel) and an independent pattern B. The heatmap was generated using GENE-E matrix visualization and analysis software (http://www.broadinstitute.org/cancer/software/GENE-E/index.html)