Summary: KODAMA, a novel learning algorithm for unsupervised feature extraction, is specifically designed for analysing noisy and high-dimensional datasets. Here we present an R package of the algorithm with additional functions that allow improved interpretation of high-dimensional data. The package requires no additional software and runs on all major platforms. Availability and Implementation: KODAMA is freely available from the R archive CRAN ( http://cran.r-project.org ). The software is distributed under the GNU General Public License (version 3 or later). Contact: [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online.

There are three main hypotheses for the activation of the human uterus at labour: functional progesterone withdrawal, inflammatory stimulation, and oxytocin receptor activation. To test these alternatives we have taken information and data from the literature to develop causal pathway models for the activation of human myometrium. The data provided quantitative RT-PCR results on key genes from samples taken before and during labour. Principal component analysis showed that pre-labour samples form a homogenous group compared to those during labour. We therefore modelled the alternative causal pathways in non-laboring samples using directed graphs and statistically compared the likelihood of the different models using structural equations and D-separation approaches. Using the computer program LISREL, inflammatory activation as a primary event was highly consistent with the data (p = 0.925), progesterone withdrawal, as a primary event, is plausible (p = 0.499), yet comparatively unlikely, oxytocin receptor mediated initiation is less compatible with the data (p = 0.091). DGraph, a software program that creates directed graphs, produced similar results (p= 0.684, p= 0.280, and p = 0.04, respectively). This outcome supports an inflammatory aetiology for human labour. Our results demonstrate the value of directed graphs in determining the likelihood of causal relationships in biology in situations where experiments are not possible.