1. Monitoring inflammatory heterogeneity with multiple biomarkers for multidimensional endotyping of asthma 
Ioana Agache, MD, PhD, Daniel S. Strasser, PhD, Gabin M. Pierlot, Hervé Farine, PhD, Kenji Izuhara, MD, PhD, Cezmi A. Akdis, MD, PhD 
Journal of Allergy and Clinical Immunology 
Volume 141, Issue 1, Pages (January 2018)
DOI: /j.jaci
Copyright © 2017 American Academy of Allergy, Asthma & Immunology Terms and Conditions

2. Fig 1
TDA was used to analyze and visualize clustering of subjects based on their similarity across 21 clinical, physiologic, and inflammatory parameters simultaneously. TDA constructs a network of nodes with edges between them. The nodes represent sets of data points, and 2 nodes are connected with an edge if their corresponding collections of data points have a point in common. A patient can fall into several nodes, which is how 2 nodes are connected. More precisely, we connected 2 nodes with an edge if they shared at least 1 patient. The filter function was set to principal and secondary singular value decomposition. The number of intervals was 10 and 10, respectively, and the overlapping percentage was fixed to 50. The original TDA network is simplified by focusing on nodes with more than 4 patients. Clusters are visualized by 2 using dimensions: asthma severity (GINA) and blood eosinophilia. Nodes are colored based on severity, and their size is proportional to the number of patients, which is also indicated numerically inside the node. Connecting nodes were assigned to a particular cluster based on either patient overrepresentation in the neighboring nodes or the number of connections within a cluster. Six clusters were identified. Eight patients did not cluster and appeared to represent a less common individual endotype.
Journal of Allergy and Clinical Immunology  , DOI: ( /j.jaci )
Copyright © 2017 American Academy of Allergy, Asthma & Immunology Terms and Conditions

3. Fig 2
The 12 most relevant clinical characteristics and serum biomarkers are visualized as box plots for the 6 TDA clusters. Dots represent outliers, and the box represents the second, median, and third quartiles (50% of the data points). The upper whisker goes to the highest value that is within 1.5*interquartile range (IQR), and the lower whisker goes to the lowest value that is within 1.5*IQR (opposite direction). The rest of data points are called outliers and represented as dots. ACT, Asthma Control Test; MCP-1, monocyte chemoattractant protein 1.
Journal of Allergy and Clinical Immunology  , DOI: ( /j.jaci )
Copyright © 2017 American Academy of Allergy, Asthma & Immunology Terms and Conditions

4. Fig E1
Journal of Allergy and Clinical Immunology  , DOI: ( /j.jaci )
Copyright © 2017 American Academy of Allergy, Asthma & Immunology Terms and Conditions

5. Fig E2
Journal of Allergy and Clinical Immunology  , DOI: ( /j.jaci )
Copyright © 2017 American Academy of Allergy, Asthma & Immunology Terms and Conditions

6. Fig E3
Journal of Allergy and Clinical Immunology  , DOI: ( /j.jaci )
Copyright © 2017 American Academy of Allergy, Asthma & Immunology Terms and Conditions

7. Fig E4
Journal of Allergy and Clinical Immunology  , DOI: ( /j.jaci )
Copyright © 2017 American Academy of Allergy, Asthma & Immunology Terms and Conditions