DIA assay libraries enable holistic functional analyses of
co-regulated clusters
Significance testing is a commonly used method for evaluating proteomic
data, but often fails to illuminate the context in which these proteins
are functioning. The DIA assay library generated in this study contained
over 2000 proteins, which permitted non-biased clustering of all
corresponding protein abundance patterns associated with salinity
acclimation. In our example, cluster analysis identified groups of
proteins which were regulated in similar ways to the significant
proteins, capturing 88% of the significant proteins in one of two
clusters and expanding the number of proteins to be evaluated by six
times while still focusing on proteins responding similarly to a
salinity challenge. The value of this expansion was particularly evident
in the STRING network analysis, as an analysis of only 42 significant
proteins did not return any protein-protein networks with more than one
edge. The expanded list returned a complex network indicating the
connections between significant proteins with non-significant
intermediaries, and the effects of significant regulation on connected
proteins despite these effects not reaching the level of statistical
significance. Additionally, STRING and KEGG enrichment only returned one
protein domain and one KEGG pathway which were highly enriched for
significantly up-regulated proteins. Thus, our study is in agreement
with previous reports pointing out that systems scale “omics” studies
are rendered more powerful when a variety of threads of evidence,
including cluster analysis, are used to expand on significance testing
results (Gehlenborg et al., 2010).