Meta‐analysis and meta‐regression of transcriptomic responses to water stress in Arabidopsis

Summary

The large amounts of transcriptome data available for Arabidopsis thaliana make a compelling case for the need to generalize results across studies and extract the most robust and meaningful information possible from them. The results of various studies seeking to identify water stress‐responsive genes only partially overlap. The aim of this work was to combine transcriptomic studies in a systematic way that identifies commonalities in response, taking into account variation among studies due to batch effects as well as sampling variation, while also identifying the effect of study‐specific variables, such as the method of applying water stress, and the part of the plant the mRNA was extracted from. We used meta‐analysis, the quantitative synthesis of independent research results, to summarize expression responses to water stress across studies, and meta‐regression to model the contribution of covariates that may affect gene expression. We found that some genes with small but consistent differential responses become evident only when results are synthesized across experiments, and are missed in individual studies. We also identified genes with expression responses that are attributable to use of different plant parts and alternative methods for inducing water stress. Our results indicate that meta‐analysis and meta‐regression provide a powerful approach for identifying a robust gene set that is less sensitive to idiosyncratic results and for quantifying study characteristics that result in contrasting gene expression responses across studies. Combining meta‐analysis with individual analyses may contribute to a richer understanding of the biology of water stress responses, and may prove valuable in other gene expression studies.

Significance Statement

Transcriptome studies are sometimes difficult to compare due to different experimental conditions. Here we show that that meta‐analysis and meta‐regression are useful for identifying robust gene sets and can quantify study characteristics that result in contrasting gene expression responses across studies. Combining meta‐analysis with individual analyses thus contribute to a richer understanding of the biology of water stress responses and may prove valuable in other gene expression studies.