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: