To investigate whether these overlapping periods could reflect transmission of P. carinii between the members of the cluster, we calculated, for each type, the multinomial probability that the observed clusters occurred by chance alone using Monte-Carlo simulation experiments (we assumed uniform distribution over time).
To compare putative gene functions between these clusters, we examined the GO categories for the four clusters above.
Also, since the goal is to identify a relatively small set of nodes responsible for most of the connections between the two clusters, we insist that the size of S is always less than half the size of either V X) or V Y) in order to identify any node in S as a gateway (or, S ≤ min X 2, Y 2 ).
By examining the concordance between the clustering techniques, we determined a set of core six breast cancer classes (Soria et al, 2010).
To characterize the functional differences between the studies activating the 2 clusters, we performed quantitative "forward inference" and "reverse inference" (cf. Poldrack 2006; Poldrack 2011) on the 2 networks (right anterior DLPFC-[anterior cingulate cortex] ACC and right posterior DLPFC-posterior parietal cortex).
At each step, it is necessary to have a definition of the distance between two clusters: we take this distance to be the mean distance of members of different clusters.
As similarity measure between individual objects (diseases/TFs) we used the Euclidean distance, and for distance between clusters we used the average (Euclidean) distance between all pairs of objects.
Given that some choice disability factors like partner violence are clustered, and there was a high degree of heterogeneity between clusters, we repeated the analysis using generalised estimating equation (GEE) in the R package Zelig [ 45] in an exchangeable correlation structure (logit.gee model, 1000 simulations, robust 95%CI).
First, for conveying the association between the clusters, in this work we only visualize the cardinality of the association and filter them by variance.
To limit the risk of contamination between clusters we used census data from Statistics South Africa to demarcate clusters with similar estimated population sizes and suitable borders or natural boundaries (such as roads, rivers, and hills).
