Large-scale atlas of microarray data reveals the distinct expression landscape of different tissues in Arabidopsis

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Abstract

SUMMARY

Transcriptome data sets from thousands of samples of the model plantArabidopsis thalianahave been collectively generated by multiple individual labs. Although integration and meta-analysis of these samples has become routine in the plant research community, it is often hampered by a lack of metadata or differences in annotation styles of different labs. In this study, we carefully selected and integrated 6057 Arabidopsis microarray expression samples from 304 experiments deposited to the Gene Expression Omnibus (GEO) at the National Center for Biotechnology Information (NCBI). Metadata such as tissue type, growth conditions and developmental stage were manually curated for each sample. We then studied the global expression landscape of the integrated data set and found that samples of the same tissue tend to be more similar to each other than to samples of other tissues, even in different growth conditions or developmental stages. Root has the most distinct transcriptome, compared with aerial tissues, but the transcriptome of cultured root is more similar to the transcriptome of aerial tissues, as the cultured root samples lost their cellular identity. Using a simple computational classification method, we showed that the tissue type of a sample can be successfully predicted based on its expression profile, opening the door for automatic metadata extraction and facilitating the re-use of plant transcriptome data. As a proof of principle, we applied our automated annotation pipeline to 708 RNA-seq samples from public repositories and verified the accuracy of our predictions with sample metadata provided by the authors.

Significance Statement

A total of 6057 Arabidopsis microarray expression samples from 304 experiments deposited to NCBI GEO were computationally integrated and consistently re-annotated. We demonstrate that the tissue type of microarray samples can be accurately predicted from the expression profile. Thus missing or incomplete metadata of transcriptome data sets can be automatically reconstructed.

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