Statistical analyses
RNAseq analysis was performed using the genomic analysis tools available through Galaxy (Afgan et al., 2018). Quality of RNAseq runs was validated by FastQC and adapter sequences were clipped using FASTQ (Gordon, 2010). Reads were mapped to the A. thaliana reference genome (TAIR10), and preliminary differential expression analysis was conducted using HISAT and StringTie (Kim, Landmead, & Salzberg, 2015; Pertea et al., 2015). Differential expression analysis was conducted using DESeq2 as well as the calculation of adjusted p- values, which limit high false positive discovery rates due to multiple testing (Love, Huber, & Anders, 2014). Data can be accessed on the Gene Expression Omnibus at GSE154349. Log2 fold-changes were transformed with the rlog (regularized log) function to minimize variance caused by low expression genes, then clustered and plotted using pheatmap (Kolde, 2018). In pheatmap, each sample was clustered on the horizontal axis based on the similarity of its transcriptome to the 23 other transcriptomes. On the vertical axis, individual genes were clustered based on the similarity of their expression profile across the 24 samples to the expression profile of other genes.
Comparisons of two means were evaluated via Student’s t tests, and comparisons of multiple means evaluated via one-way analysis of variance (ANOVA) and post hoc Tukey–Kramer Honestly Significant Differences (HSD) tests. Nonlinear curves were generated using 3-parameter exponential and 4-parameter logistic models. All statistical analyses, excluding those of RNAseq data, were conducted using JMP software (Pro 15.0.0; SAS Institute Inc., Cary, NC, USA).
iv. b. Results: