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. 2018 Apr 26;8:6558. doi: 10.1038/s41598-018-24830-0

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© The Author(s) 2018

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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/.

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Principal component analysis plot generated by pcaExplorer. Each dot represents a sample colored to separate by line (Fayoumi: Fay; Leghorn: Leg), challenge status (Challenged; Nonchallenged: NON), and time (2, 6, and 10 dpi). Ellipses were drawn around each treatment group with 95% confidence. The top 5000 genes were used to calculate the principal components. Principal component 1 (PC1) accounts for 51.06% of the variance in the Harderian gland transcriptome. PC2 accounts for 8.62% of the variance and clusters clearly separate by line.