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(Hocke, 1998, Hocke and K?mpfer, 2009, Scargle, 1982?and?Scargle, 1989)). This allowed us to reconstruct the series so that standard time-series analysis methods could selleck inhibitor be applied, and to fill in gaps in a robust fashion given that the spectral approach considers the entire time-series data as a whole, in contrast to other local linear or spline interpolation methods. The data was then classified into three groups: (I) After reconstructing, each time-series curve, Y(tj)Y(tj), we considered autocorrelation, ��k=��j=1N?k(Y(tj)?��Y)(Y(t(j+k))?��Y)/��j=1N(Y(tj)?��Y), at lag k?= 1, as a check of non-randomness. A class of autocorrelated signals was selected (p? 0.25, Data S11C, III, in good agreement with theoretical values ( Anderson, 1942) for the length of data, N?= 20). After removal of the autocorrelated signals from the set, the remaining signals were checked for aberrant spikes, significantly high or low signal instances compared to what would be expected in a random distribution. Signals that displayed aberrant high signals (p?Sirolimus in vivo of randomly simulated distribution of normalized time signals of corresponding length N for each time-series) were classified as (II) spike maxima, while signals that displayed aberrant low signals (p?Biperiden HCl assess the biological significance for each of the obtained clusters, gene-based pathway and ontology enrichment and network analysis was performed using Cytoscape ( Cline et?al., 2007, Shannon et?al., 2003?and?Smoot et?al., 2011). Namely, the Reactome ( Croft et?al., 2011, Joshi-Tope et?al., 2005, Matthews et?al., 2007, Matthews et?al., 2009?and?Vastrik et?al., 2007) Functional Interaction (FI) plugin was used to assess membership of genes to Reactome and KEGG ( Kanehisa and Goto, 2000) pathways and to calculate enrichment (p?