How to Select Differential Genes for KEGG Analysis with Metabolomics and Transcriptomics Data?
When conducting KEGG pathway annotation and enrichment analysis with metabolomics and transcriptomics data to identify differential genes, follow these steps:
Data Preprocessing
1. Transcriptomics Data
(1) Quality Control: Use tools like FastQC to check raw sequence quality, then trim low-quality reads with Trimmomatic or fastp.
(2) Sequence Alignment: Align clean reads to the reference genome using HISAT2, STAR, or Bowtie2.
(3) Expression Quantification: Use featureCounts or HTSeq to count reads per gene, then normalize with DESeq2 or edgeR.
2. Metabolomics Data
(1) Preprocessing: Perform baseline correction, peak detection, normalization, and metabolite identification using tools like MZmine or XCMS.
(2) Quantification: Determine relative or absolute metabolite abundance.
Differential Analysis
1. Transcriptomics
Use DESeq2, edgeR, or limma to identify differentially expressed genes, setting significance thresholds (e.g., P-value < 0.05, |log₂FoldChange| > 1).
2. Metabolomics
Perform statistical tests (e.g., t-test, ANOVA) on metabolite abundance to identify significantly different metabolites with appropriate thresholds.
KEGG Pathway Annotation and Enrichment Analysis
1. Annotation
Use tools like KEGGREST, DAVID, or KEGG Mapper to annotate pathways with the differential gene or metabolite list.
2. Enrichment Analysis
Apply tools like ClusterProfiler (an R package) to identify significantly enriched KEGG pathways and understand their biological roles.
Ensuring proper data quality control, selecting appropriate statistical tests, and setting reasonable thresholds are key factors for accurate and reliable results.
MtoZ Biolabs, an integrated chromatography and mass spectrometry (MS) services provider.
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