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. 2018 Mar 1;12(2):026015. doi: 10.1088/1752-7163/aa9eef

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© 2018 IOP Publishing Ltd

This article is made available via the PMC Open Access Subset for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source.

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Figure 2. — Differential production of volatile metabolites by IAV-infected and uninfected MLE-Kd cells. (A) ROC curve for the discrimination between infected and uninfected cells using random forest (RF, green line), support vector machines with a linear kernel (linear SVM, blue line), and partial least-squares discriminant analysis (PLS-DA, red line). *Optimal sensitivity and specificity of each statistical model are calculated at the given class prediction cutoff (ranging from 0 to 1). (B) Venn Diagram of the top 20% features selected as the most discriminatory between IVA-infected cell line and uninfected control using RF. SVM, and PLS-DA algorithm. (C) Most significant clusters obtained by k-Means Clustering analysis of the difference between infected and uninfected cell cultures. Remaining cluster is reported in supplementary figure 4. # features codification as in table 1.

Figure 2. — Differential production of volatile metabolites by IAV-infected and uninfected MLE-Kd cells. (A) ROC curve for the discrimination between infected and uninfected cells using random forest (RF, green line), support vector machines with a linear kernel (linear SVM, blue line), and partial least-squares discriminant analysis (PLS-DA, red line). *Optimal sensitivity and specificity of each statistical model are calculated at the given class prediction cutoff (ranging from 0 to 1). (B) Venn Diagram of the top 20% features selected as the most discriminatory between IVA-infected cell line and uninfected control using RF. SVM, and PLS-DA algorithm. (C) Most significant clusters obtained by k-Means Clustering analysis of the difference between infected and uninfected cell cultures. Remaining cluster is reported in supplementary figure 4. # features codification as in table 1.