## 2 Results
### Identification and Functional Enrichment Analyses of DEGs  
For the biological replication samples, DESeq2 was used for the differential expression gene (DEG) analysis between sample groups. After the differential expression analysis, it was necessary to perform multiple hypothesis test corrections on the hypothesis test probability (_p_-value) to obtain the false discovery rate (FDR) using the Benjamini-Hochberg method. The fragments per kilobase of the exon model per million mapped fragment (FPKM) values were calculated for each unigene by setting \(\mid\) log\({}_{2}\) fold change \(\mid\)\(\geq\) 1 and FDR \(<\) 0.05 as the DEGs' cut-off values. The results showed that a total of 3257 significant DEGs were screened out among the 11 comparison groups (Table 2). Among them, compared with the CK group, the largest number of significant DEGs (624 downregulated and 722 upregulated) was found in the TF14 library (Figure 4A). The hierarchical clustering of the DEGs of the CK and TF14 comparison groups is shown in Figure 4B. From the clustering heat map, it can be seen that the gene expression patterns in the TF14 and CK groups had significant differences. Similar results were observed for several comparisons, including TC7 vs. TC14, TF7 vs. TF14, and CK vs. TC14, where there were 1362, 889, and 853 DEGs, respectively. This may be due to the sampling time, hormonal induction, and individual differences in the samples. In order to study the expression patterns of genes after different lengths of MeJA treatment, a cluster analysis was used to cluster genes with the same or similar expression patterns into classes to identify the unknown functions of known genes or the functions of unknown genes. Firstly, the FPKM values of the genes were  
Figure 3: Classification of unigenes from _Dendrobium nobile_ Lindl. The horizontal axis represents the functional classification ID (code) of KOG, and the vertical axis represents the number of differential genes included. Different classifications are represented by different colors. The legend is a code with a functional description. centralized and standardized, then a K-means cluster analysis was performed to determine the expression patterns of differential genes for different treatment times, and they were plotted (Figure S5). In order to better explore the biological functions of DEGs, significant DEGs in the 11 comparative groups were functionally classified and annotated through GO and KEGG enrichment analyses. For the GO analysis (Figure 5A), the significant DEGs in the \"cellular component\" category between the CK and TF14 libraries were mainly enriched for the \"cell and cell part\", \"membrane and membrane part\", and \"organelle and organelle part\" subcategories; the significant DEGs in the \"molecular function\" category between the CK and TF14 libraries were mainly enriched for the \"transcription regulator activity\", \"catalytic activity\", \"transporter activity\", and \"binding\" subcategories; and the significant DEGs in the \"biological process\" category between the CK and TF14 libraries were mainly enriched for the \"developmental process\", \"metabolic process\", \"cellular process\", \"multicellular organismal process\", \"regulation of biological process\", and \"response to stimulus\" subcategories. The \"cellular component\" category was mainly composed of upregulated DEGs, whereas the \"cell wall biogenesis and hemicellulose metabolic process\" subcategory was mainly composed of downregulated DEGs. Directed acyclic graphs were also used to describe similar classification results for the GO structures (Figure 5B). Three GO directed acyclic graphs were constructed, representing cellular components, biological processes, and molecular functions. In addition, 200 of the GO categories were significantly affected in the CK and TF14 libraries (Supplementary Table S1).