  Motivation: How do we integratively analyze large-scale multi-platform
genomic data that are high dimensional and sparse? Furthermore, how can we
incorporate prior knowledge, such as the association between genes, in the
analysis systematically? Method: To solve this problem, we propose a Scalable
Network Constrained Tucker decomposition method we call SNeCT. SNeCT adopts
parallel stochastic gradient descent approach on the proposed parallelizable
network constrained optimization function. SNeCT decomposition is applied to
tensor constructed from large scale multi-platform multi-cohort cancer data,
PanCan12, constrained on a network built from PathwayCommons database. Results:
The decomposed factor matrices are applied to stratify cancers, to search for
top-k similar patients, and to illustrate how the matrices can be used for
personalized interpretation. In the stratification test, combined twelve-cohort
data is clustered to form thirteen subclasses. The thirteen subclasses have a
high correlation to tissue of origin in addition to other interesting
observations, such as clear separation of OV cancers to two groups, and high
clinical correlation within subclusters formed in cohorts BRCA and UCEC. In the
top-k search, a new patient's genomic profile is generated and searched against
existing patients based on the factor matrices. The similarity of the top-k
patient to the query is high for 23 clinical features, including
estrogen/progesterone receptor statuses of BRCA patients with average precision
value ranges from 0.72 to 0.86 and from 0.68 to 0.86, respectively. We also
provide an illustration of how the factor matrices can be used for
interpretable personalized analysis of each patient.
