Dictionary Learning for Transcriptomics Data Reveals Type-Specific Gene Modules in a Multi-Class Setting

Extracting information from large biological datasets is a challenging task, due to the large data size, high-dimensionality, noise, and errors in the data. Gene expression data contains information about which gene products have been formed by a cell, thus representing which genes have been read to activate a particular biological process. Understanding which of these gene products can be related to which processes can for example give insights about how diseases evolve and might give hints about how to fight them. The Next Generation RNA-sequencing method emerged over a decade ago and is nowadays state-of-the-art in the field of gene expression analyses. However, analyzing these large, complex datasets is still a challenging task. Many of the existing methods do not take into account the underlying structure of the data. In this paper, we present a new approach for RNA-sequencing data analysis based on dictionary learning. Dictionary learning is a sparsity enforcing method that has widely been used in many fields, such as image processing, pattern classification, signal denoising and more. We show how for RNA-sequencing data, the atoms in the dictionary matrix can be interpreted as modules of genes that either capture patterns specific to different types, or else represent modules that are reused across different scenarios. We evaluate our approach on four large datasets with samples from multiple types. A Gene Ontology term analysis, which is a standard tool indicated to help understanding the functions of genes, shows that the found gene-sets are in agreement with the biological context of the sample types. Further, we find that the sparse representations of samples using the dictionary can be used to identify type-specific differences.