  Cellular mechanism-of-action is of fundamental concern in many biological
studies. It is of particular interest for identifying the cause of disease and
learning the way in which treatments act against disease. However, pinpointing
such mechanisms is difficult, due to the fact that small perturbations to the
cell can have wide-ranging downstream effects. Given a snapshot of cellular
activity, it can be challenging to tell where a disturbance originated. The
presence of an ever-greater variety of high-throughput biological data offers
an opportunity to examine cellular behavior from multiple angles, but also
presents the statistical challenge of how to effectively analyze data from
multiple sources. In this setting, we propose a method for mechanism-of-action
inference by extending network filtering to multi-attribute data. We first
estimate a joint Gaussian graphical model across multiple data types using
penalized regression and filter for network effects. We then apply a set of
likelihood ratio tests to identify the most likely site of the original
perturbation. In addition, we propose a conditional testing procedure to allow
for detection of multiple perturbations. We demonstrate this methodology on
paired gene expression and methylation data from The Cancer Genome Atlas
(TCGA).
