Meta analysis of public drought gene expression data in plants

Physiologically relevant drought stress is difficult to apply
consistently, and the heterogeneity in experimental design, growth
conditions, and sampling schemes make it challenging to compare water
deficit studies in plants. Here, we re-analyzed hundreds of drought gene
expression experiments across diverse model and crop species and
quantified the variability across studies. We found that drought studies
are surprisingly uncomparable, even when accounting for differences in
genotype, environment, drought severity, and method of drying. Many
studies, including most Arabidopsis work, lack high-quality phenotypic and
physiological datasets to accompany gene expression, making it impossible
to assess the severity or in some cases the occurrence of water deficit
stress events. From these datasets, we developed supervised learning
classifiers that can accurately predict if RNA-seq samples have
experienced a physiologically relevant drought stress, and suggest this
can be used as a quality control for future studies. Together, our
analyses highlight the need for more community standardization, and the
importance of paired physiology data to quantify stress severity for
reproducibility and future data analyses.