Systems Analysis of Arrestin Pathway Functions - Progress in Molecular Biology and Translational Science - page 439

large enough, distribution of an average of sampled observations is normal
regardless of the nature of parent distribution.” Statistical PAGE analysis
intentionally directs the analysis of predefined signaling pathways in datasets
rather than of individual factors. To generate easily appreciable data, with
respect to differential signaling states, PAGE uses the fold change (gene tran-
script or protein) between the control and experimental groups to calculate
Z -scores of the predefined datasets and a normal distribution to assign sta-
tistical significance to the experimental data. 142 Traditional mass dataset
analysis requires that individual genes/proteins have significantly different
expression levels in order for them to be considered differentially regulated.
PAGE specifically takes into account that factors can be both coregulated
and coexistent, to help populate discrete signaling pathways. Therefore, it
is possible that factors individually may not be significantly regulated above
or below the baseline, but significant regulation of pathways can be gener-
ated by such factors by grouping them significantly into the predefined sig-
naling sets. 143 PAGE and GSEA are especially powerful for the
interpretation of large datasets that will possess an increased likelihood of
retrieved factor identity variation between experiments (especially the case
for MS-based proteomics), or when there are subtle differences between
control and experimental paradigms. Therefore, the predictive functional
output of the analytical system is not wholly reliant upon a small number
of individual factors but on the coexpression and coherent regulation of
these factors, reflecting the coordinated, interconnected nature of receptor
signaling pathways themselves.
The combined employment of mass data collection and signaling pathway
analytical tools is likely to revolutionize signal transduction research in the next
decade. The ability to accurately appreciate and perhaps predict a global cel-
lular impact of pharmacological signaling actions may create a greater under-
standing of disease etiology and eventual drug control of disease at the level of
the factor network rather than the linear signaling pathway level. The appreci-
ation of a network hypothesis for biological activity presents many important
new avenues for signal transduction and pharmacological research. For exam-
ple, the ability to identify molecular keystone factors that exert the most pro-
found actions upon the state of a given pathological network may facilitate
the creation of collateral pharmacological strategies. 102 Computational plat-
forms are currently being developed using advanced bioinformatic retrieval
processes such as latent semantic indexing (LSI), to facilitate the discovery
of these network-regulating factors. LSI is a data extraction process recently
applied to biomedical science that employs singular value decomposition to