## 3 Results
### Conjoint Analysis of Transcriptomes and Metabolomes  
We opted for two models that satisfied the criteria of intergroup association analysis, i.e., maximizing differences in components: the mycelium vs. primordium (GM vs. GPM) and primordium vs. fruiting body (GPM vs. GSM) phases. Our analysis delved into the correlation and expression trend of each component. The outcomes of this screening, coupled with the differential folds, were visually represented in a nine-quadrant plot (note: the plot could not be generated when the metabolite differential folds threshold was 1) (Figure 7A,B) [40; 41]. Given that the up- and down-regulated expressions of genes and metabolites did not meet the FC screening conditions individually, we focused on six quadrants to depict the fundamental expression trends of genes and metabolites. Quadrants 3 and 4 indicated that gene and metabolite expression trends were consistent, implying potentially positive regulation of the metabolite by the gene. Conversely, quadrants 1 and 6 indicated opposite expression trends, signifying a negative correlation between gene and metabolite. Quadrants 2 and 5 suggested that the gene remained unchanged while the metabolite underwent up- or down-regulation. The horizontal coordinates indicate the transcriptome's differential multiplicity (represented as log2), while the vertical coordinates indicate the differential multiplicity of the metabolome (also represented as log2). The analysis focused on trends in differential genes and metabolites. The dashed lines along the horizontal and vertical coordinates indicate the differential fold changes of the transcriptome and metabolome, respectively. Noteworthy differences in genes and metabolites are denoted outside the threshold lines, whereas non-significant differences are marked within these lines. Each fitted line corresponds to a gene or metabolite, with red lines indicating genes and metabolites that are significantly different (exhibiting either consistent or opposite expression trends), and green lines indicating metabolites that are significantly different in one of the omics but not the others. The correlation analysis and quadrant labeling provided useful insights. In the GM vs. GPM stage, quadrants 1 and 2 suggest a higher expression abundance of metabolites than genes, indicating the up-regulation of metabolites. Quadrants 3 and 4 suggest that the  
Figure 7: Correlation analysis of genes and metabolites in the development stages of _Thelephora ganbajun_ Zang: (**A**) GM vs. GPM, (**B**) GPM vs. GSM. gene expression trend aligns with metabolites, potentially signifying a positive regulatory effect of genes on metabolites. Quadrants 5 and 6 indicate that metabolites are expressed less abundantly than genes, corresponding to the down-regulation of metabolites. Notably, quadrants 1 and 3 have the highest number of differentially abundant metabolites (DAMs) and differentially expressed genes (DEGs), exhibiting a negative correlation in quadrant 1 and a positive correlation in quadrant 3. Moving to the GPM vs. GSM stage, the basic expression trends of genes and metabolites remain consistent with the previous stage, although the numbers of DAMs and DEGs in these quadrants are reduced. The correlation analysis shows significant differences in the genes and metabolic pathways regulating biosynthesis between different growth stages. When conducting KEGG annotation (Figure 8A), multiple pathways are often annotated for both differential metabolites and differential genes. In such instances, prioritizing genes and metabolic pathways with a \(p\)-value \(<\) 0.05 for analysis can allow for the swift identification of pathways related to the study's objectives. In the two-layer omics clustering, the common pathway with a \(p\)-value \(<\) 0.05 in the GM vs. GPM stage for both transcriptome and metabolome was found for the biosynthesis of amino acids.