THREE-WAY CLUSTERING OF MULTI-TISSUE MULTI-INDIVIDUAL GENE EXPRESSION DATA USING SEMI-NONNEGATIVE TENSOR DECOMPOSITION.

ABSTRACT: The advent of high-throughput sequencing technologies has led to an increasing availability of large multi-tissue data sets which contain gene expression measurements across different tissues and individuals. In this setting, variation in expression levels arises due to contributions specific to genes, tissues, individuals, and interactions thereof. Classical clustering methods are ill-suited to explore these three-way interactions and struggle to fully extract the insights into transcriptome complexity contained in the data. We propose a new statistical method, called MultiCluster, based on semi-nonnegative tensor decomposition which permits the investigation of transcriptome variation across individuals and tissues simultaneously. We further develop a tensor projection procedure w

SUBMITTER: Wang M 

PROVIDER: S-EPMC7771883 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature