< Back to Article

A Computational Framework for Influenza Antigenic Cartography

Figure 3

Computational simulation demonstrates that temporal model can reduce the biases generated by the Type II data (low reactors) in hemagglutination inhibition (HI) dataset.

(a) HI matrix ( data absense) with neither Type II nor Type III data, using multidimensional scaling (MDS); (b) HI matrix ( data absense, data structure: randomly distributed) with Type III data but without Type II data, using Alternating Gradient Descent (AGD) and MDS; (c) HI matrix ( data absense, data structure: similar to H3N2 data as shown in Figure 1) with both Type II data and Type III data, using AGD and MDS; (d)HI matrix ( data absense, data structure: similar to H3N2 data as shown in Figure 1) with both Type II and Type III data, using MC-MDS. (e)HI matrix ( data absense, data structure: similar to H3N2 data as shown in Figure 1) with both Type II and Type III data, using Metric MDS. (f) Another independent run by the same setting and method as (e).

doi: https://doi.org/10.1371/journal.pcbi.1000949.g003