## 2 Results
### Modeling of the Role of GPVI in Thromus Formation on Various Collagens  
We then applied a regression analysis to provide a systematic examination of the generated data (M1-9), which consisted of 416 data points (52 mean control flow runs of 9 surfaces, 8 parameters), to reveal the GPVI dependency of each surface. First, a partial least square (PLS) regression model was generated for collagen peptides M1-3 (which had an assumed high GPVI dependency) and for M4,5 (with supposedly no role for GPVI), after which the data from M6 (collagen-H) were entered into the model. This analysis resulted in relevant components 1 and 2, explaining 68% and 15% of the variation, respectively (Figure S4). A principal component plot indicated a tight cluster of flow runs with M1-3,6. The data for M5 (negative component 1) and M4 (negative component 2) lay further out in the model. This agreed with the large observed differences in (the parameters of) thrombus formation on M4 and M5. The calculated beta matrix indicated that P2-6 contributed to the modeled results to a similar extent.  
Because of the separation of M4-5 parameters, the component 1 model was used for further analysis. Prediction testing of the model showed near complete prediction accuracy for all surfaces, except for M4 (because there was no component 2) (Table 2). The model was further used to predict the role of GPVI in the remaining collagen surfaces, M7-9. For both fibrillar collagens (M7,9), the prediction of GPVI dependency was mixed, while it was negative for the degraded collagen-I (M8). Subsequently, we integrated into the model the second set of 416 data points of Syk-inhibited blood samples (52 mean flow runs with Syk-IN for 9 surfaces, 8 parameters) to predict the absence of GPVI activity. Markedly, across all surfaces, 51 out of 52 samples predicted a negative GPVI dependency, wherein the only incorrectly predicted sample was just above the conventional 0.5 threshold value for right prediction. Taken together, the constructed PLS model indicated, in addition to complete GPVI-independency of all Syk-inhibited samples, no role of GPVI on surfaces M5 and M8.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Microspot**} & \multirow{2}{*}{**GPVI Dependency**} & \multicolumn{3}{c}{**Correctly Predicted**} \\ \cline{3-5}  & & **Range** & **Ctrl** & **Syk-IN** \\ \hline M1 & positive & 0.41–1.06 & 5/6 & 6/6 \\ M2 & positive & 0.27–0.76 & 4/5 & 5/5 \\ M3 & positive & 0.86–1.07 & 5/5 & 5/5 \\ M4 & negative & 0.57–0.97 & 0/6 & 6/6 \\ M5 & negative & 0.21–0.34 & 5/5 & 5/5 \\ M6 & positive & 0.68–1.11 & 7/7 & 6/7 \\ M7 & mixed & 0.44–0.85 & 5/7 & 7/7 \\ M8 & negative & 0.13–0.41 & 0/5 & 5/5 \\ M9 & mixed & 0.49–0.67 & 3/5 & 5/5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Modeled partial least square (PLS) analysis (based on component 1 principal component analysis (PCA)) of range-scaled data for collagen peptides (M1–5) plus collagen-H (M6), with assumed GPVI dependency. The ranges of prediction values are shown. Predicted accuracy is given by numbers of mean flow runs per donor (control and Syk-IN). By default, a correct prediction was set at \(>\)0.5. In addition, back-prediction of GPVI dependency of mean flow runs per donor for M7–9 is shown. Prediction outcomes are given here in italics. The contributions of parameters to the prediction model were in the order of P2–6 \(>>\) P1,7,8.