RESUMO
Recently, with the rapid progress of high-throughput sequencing technology, diverse genomic data are easy to be obtained. To effectively exploit the value of those data, integrative methods are urgently needed. In this paper, based on SNF (Similarity Network Diffusion) [1], we proposed a new integrative method named ndmaSNF (network diffusion model assisted SNF), which can be used for cancer subtype discovery with the advantage of making use of somatic mutation data and other discrete data. Firstly, we incorporate network diffusion model on mutation data to make it smoothed and adaptive. Then, the mutation data along with other data types are utilized in the SNF framework by constructing patient-by-patient similarity networks for each data type. Finally, a fused patient network containing all the information from different input data types is obtained by using a nonlinear iterative method. The fused network can be used for cancer subtype discovery through the clustering algorithm. Experimental results on four cancer datasets showed that our ndmaSNF method can find subtypes with significant differences in the survival profile and other clinical features.
RESUMO
One major goal of large-scale cancer omics study is to understand molecular mechanisms of cancer and find new biomedical targets. To deal with the high-dimensional multidimensional cancer omics data (DNA methylation, mRNA expression, etc.), which can be used to discover new insight on identifying cancer subtypes, clustering methods are usually used to find an effective low-dimensional subspace of the original data and then cluster cancer samples in the reduced subspace. However, due to data-type diversity and big data volume, few methods can integrate these data and map them into an effective low-dimensional subspace. In this paper, we develop a dimension-reduction and data-integration method for indentifying cancer subtypes, named Scluster. First, Scluster, respectively, projects the different original data into the principal subspaces by an adaptive sparse reduced-rank regression method. Then, a fused patient-by-patient network is obtained for these subgroups through a scaled exponential similarity kernel method. Finally, candidate cancer subtypes are identified using spectral clustering method. We demonstrate the efficiency of our Scluster method using three cancers by jointly analyzing mRNA expression, miRNA expression, and DNA methylation data. The evaluation results and analyses show that Scluster is effective for predicting survival and identifies novel cancer subtypes of large-scale multi-omics data.
