## 4 Discussion  
The use of graphical methods for categorical data has permeated the peer-reviewed literature in many Science, Technology, Engineering, and Mathematics (STEM) related fields [51; 52; 53; 54]. Some fields, such as those in the 'omics arena, require advanced computational methods in order to organize complex quantitative data sets [51]. Other analytical fields, such as statistical sciences, have contributed to the development of a data visualization industry; major players can be described as solution providers of either visual analytics or reporting tools [55; 56]. Designing effective graphics is generally regarded as art informed by large sets of empirical data [57; 58; 59]. When relating (or correlating) particle properties to adverse health effects, it is important to define the parametric space in which the experimental data set belongs. In toxicological data, the entire space can be defined as the biological (or biochemical) effects of an exogenous toxicant (or endogenous toxin) on an in vivo or in vitro test system. However, an investigator can also define a specific space, such as the composition of the NPPC construct. In the former, the mass spectrometry data used in this paper reveals only a small number of critical attributes needed to define the possible health effects of gold nanoparticle exposure on human health. In the latter, the mass spectrometry data visualized herein defines the composition of the NPPC construct completely and lends itself to interpretation that promotes the next set of pathway-specific toxicity studies. Specific to 'omics data, modern nanotoxicology experimental designs incorporate genomics, proteomics, and metabolomics into the already tedious process of nanomaterial physicochemical characterization, dose-response relationships, exposure dosimetry, and mechanistic analyses [60; 61; 62; 63]. For example, genomic data provide detailed information about specific cellular pathways triggered \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Abbreviation** & **NH3-ST2** & **NH3-ST1** & **PEG-ST2** & **PEG-ST1** & **COOH-ST2** & **COOH-ST1** \\ \hline UBB\_HUMAN & 4124 & 3575 & 12,800 & 4543 & 3729 & 3349 \\ MARCS\_RAT & 32,900 & 2622 & 18,700 & 11,700 & 896 & 5976 \\ CALL\_DROME & 28,900 & 48,500 & 18,500 & 18,600 & 61,300 & 13,900 \\ VKI\_BPP22 & 391 & 311 & 629 & 135 & 2596 & 1315 \\ COX2\_STRPU & 737 & 167 & 930 & 469 & 22 & 98 \\ CNBP\_CHICK & 22,800 & 8270 & 9565 & 14,700 & 26,500 & 28,800 \\ MARCS\_CHICK & 11,000 & 1266 & 5820 & 3351 & 2889 & 914 \\ MED22\_CAEEL & 28,600 & 43,600 & 23,200 & 29,200 & 15,900 & 58,100 \\ VSPA\_CERCE & 10,400 & 1273 & 5528 & 5847 & 5618 & 11,900 \\ SIA\_BUICC & 2979 & 810 & 1697 & 2853 & 4300 & 1602 \\ HEN1\_HUMAN & 10,200 & 278 & 11,700 & 2197 & 268 & 3025 \\ MED22\_HUMAN & 23,000 & 7404 & 13,200 & 15,800 & 8656 & 32,700 \\ ACDA1\_METTE & 376 & 228 & 168 & 79 & 0 & 5 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Heat map of the average normalized abundance of each serum/coating combination, illustrated through color scale with red as the least abundant, and green as the most. The map conveys the abundancy of protein-type or functional group in a sample for multiple coating or serum. However, this method does not consider the variations in incubation time. ST1 refers to serum-type 1 and ST2 refers to serum-type 2.  
after exposure. Such data include peptide sequence, length, and density. Metabolomics data aid in assembling biotransformation reaction schemes of degraded xenobiotics and include not only metabolites, but also information about the parent molecules [64; 65]. Similarly, proteomics data contribute to the characterization of normal cell and tissue physiological processes [66; 67]. This paper focused on the different ways in which data collected from proteomic mass spectrometry analyses can be presented, visualized, and communicated. The proteins analyzed are those that form a protein corona shell around a gold nanoparticle core.