deletions | additions       diff --git a/Figure_reffigHIVHt_s.tex b/Figure_reffigHIVHt_s.tex  index 69c4fe1..4cd2ba3 100644  --- a/Figure_reffigHIVHt_s.tex  +++ b/Figure_reffigHIVHt_s.tex 
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\subsection{H1N1 A Influenza}\label{Sec:H1N1}  In this section, we apply our method to the whole-genome sequencing time series data of influenza A H1N1 (IVA) strain. The data were collected from multiple passages with total two biological replicates (E1 \& E2) in the presence and absence of an inhibitor of neuraminidase, oseltamivir (see Figure \ref{fig:flow}). \ref{fig:flow_flu1}).  At the end of each passage, whole-genome high throughput sequencing data were collected. The read counts are unbalanced between the two experiments, as the  first replicate, E1, consistently had more reads than the second one. Theparticular  IVA strain consists of 8 segments: PB2 (2313 nucleotides (nts)), PB1 (2301 nts), PA (2303 nts), HA (1775 nts), NP (1396 nts), NA (1426 nts), M1/2 (1005 nts), and NS1/2 (869 nts). To reduce computational intensity, we examine each segment per replicate separately. Within each duplicate, we analyze the control and treatment group groups  over selected time points simultaneously. In particular, we choose five time points: 1, 3, 9, 12, and the end (13 and 18 for the Ruplicates I and II, respectively). Since the first three passages were shared across groups,in total for each biological duplicate  there were total  8 time-samples, three of which were treated. treated, for each biological replicate.  Denote the 8 collection times as $t_1, t_2, t_3, t_4, t_5, t_{3D}, t_{4D}, t_{5D}$. The summary statistics are then formulated as \begin{equation}