  Personalized treatment of patients based on tissue-specific cancer subtypes
has strongly increased the efficacy of the chosen therapies. Even though the
amount of data measured for cancer patients has increased over the last years,
most cancer subtypes are still diagnosed based on individual data sources (e.g.
gene expression data). We propose an unsupervised data integration method based
on kernel principal component analysis. Principal component analysis is one of
the most widely used techniques in data analysis. Unfortunately, the
straight-forward multiple-kernel extension of this method leads to the use of
only one of the input matrices, which does not fit the goal of gaining
information from all data sources. Therefore, we present a scoring function to
determine the impact of each input matrix. The approach enables visualizing the
integrated data and subsequent clustering for cancer subtype identification.
Due to the nature of the method, no free parameters have to be set. We apply
the methodology to five different cancer data sets and demonstrate its
advantages in terms of results and usability.
