How to select differential genes in KEGG pathway annotation and enrichment analysis with metabolomics and transcriptomics data?

When metabolomics and transcriptomics data are available, KEGG pathway annotation and enrichment analysis for screening differential genes can follow these steps:

1. Data Preprocessing

1. Transcriptomics data:

• Quality control: Use tools like FastQC to check the quality of raw sequence data, then use Trimmomatic or fastp for quality trimming.
• Sequence alignment: Use alignment tools (such as HISAT2, STAR, or Bowtie2) to align the clean reads to the reference genome.
• Expression quantification: Use tools like featureCounts or HTSeq to count the number of reads for each gene, then use tools like DESeq2, edgeR, etc., to convert read counts to normalized expression levels.

2. Metabolomics data:

• Data preprocessing: Includes baseline correction, peak detection, peak normalization, and metabolite identification. Tools such as MZmine and XCMS can be used for processing.
• Quantitative analysis: Obtain the relative or absolute abundance of each metabolite.

2. Differential Analysis

1. Transcriptomics:

• Use R packages like DESeq2, edgeR, or limma for differential gene expression analysis, applying appropriate statistical significance thresholds (e.g., P-value < 0.05 and |log2FoldChange| > 1) to screen for differential genes.

2. Metabolomics:

• Conduct statistical analysis of metabolite abundance, such as t-tests or ANOVA, to identify significantly different metabolites. Similarly, set appropriate significance thresholds.

3. KEGG Pathway Annotation and Enrichment Analysis

• Annotation: Use the list of differential genes or metabolites and tools like KEGGREST, DAVID, or KEGG Mapper for KEGG pathway annotation.
• Enrichment analysis: Use tools like ClusterProfiler (an R package) for enrichment analysis to identify significantly enriched KEGG pathways. Enrichment analysis helps understand the role of differential genes or metabolites in biological processes.

In performing the above analyses, it is important to pay attention to data quality control, appropriate selection of statistical tests, and reasonable threshold settings, as these are key factors to ensure the accuracy and reliability of the analysis results.

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