RNA-Seq data analysis for Planarian with tensor decomposition-based unsupervised feature extraction

Abstract

ABSTRACTRNA-Seq data analysis of non-model organisms is often difficult because of the lack of a well-annotated genome. In model organisms, after short reads are mapped to the genome, it is possible to focus on the analysis of regions well-annotated regions. However, in non-model organisms, contigs can be generated by de novo assembling. This can result in a large number of transcripts, making it difficult to easily remove redundancy. A large number of transcripts can also lead to difficulty in the recognition of differentially expressed transcripts (DETs) between more than two experimental conditions, because P-values must be corrected by considering multiple comparison corrections whose effect is enhanced as the number of transcripts increases. Heavily corrected P-values often fail to take sufficiently small P-values as significant. In this study, we applied a recently proposed tensor decomposition (TD)-based unsupervised feature extraction (FE) to the RNA-seq data obtained for a non-model organism, Planarian; we successfully obtained a limited number of transcripts whose expression was altered between normal and defective samples as well as during time development. TD-based unsupervised FE is expected to be an effective tool that can identify a limited number of DETs, even when a poorly annotated genome is available.