MEREDITH: a multiplatform data integration approach
We devised a novel multiplatform data integration approach (MEREDITH) to identify similarities among 4,672 patients taken from the Cancer Genome Atlas (TCGA7) across 19 cancer types based on four genome-wide measurements platforms: gene expression (GE, 18,882 features), DNA-methylation (ME, 11,429 features), copy number variation (CN, 23,638 features) and microRNA expression (MIR, 467 features) data. MEREDITH is a hybrid of a concatenation and a transformation-based data integration approach able to find similarities between samples across all platforms at once, following seven principal steps (See Figure). First, it maps features to the corresponding genes and uses a principal component analysis (PCA) per platform for an initial dimensionality reduction. Here we retained the 50 PCs with the highest eigenvalues for each platform. The contributions per platform are scaled on the total variance of each of their respective set of 50 PCs to ensure that the final integrated results are not dominated by a single platform. Next, the reduced features for each of the four platforms are concatenated resulting in a 200 dimensional space in which the samples are represented. Then, samples are mapped to a two dimensional multiplatform map using t-distributed stochastic neighborhood embedding (t-SNE). Here we used the Barnes-Hut implementation, an efficient implementation of t-SNE that handles thousands of samples. The power of the non-linear t-SNE mapping is that it retains the local similarity between samples instead of the relationships between dissimilar samples. The multiplatform map allows for subsequent analysis of the samples, such as cluster analysis, platform contribution analysis, or patient survival analysis.