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Product Introduction

Transcriptomic analysis is powerful for studying gene function. Despite that transcriptome sequencing can provide numerous differentially expressed genes and regulatory networks, identifying key pathways remains challenging. Metabolomics show directly phenotypic changes, but does not explain the genetic mechanisms underlying these changes.  By integrating transcriptome and metabolome data, organisms can be studied at multiple aspects, allowing for mutual verification. so as to comprehensively and systematically analyze complex biological processes.

The transcriptome analysis reveals gene expression differences, while metabolomics identifies metabolite differences and enriches metabolic pathway information. By combining gene and metabolite differences, calculating their correlation, and constructing a correlation network, the key genes causing metabolite changes can be found and the key regulatory pathways can be determined. Generally, this joint analysis predominantly includes correlation analysis, KEGG pathway analysis, KEGG enrichment analysis.

Advantages of joint analysis

  • To compensate for the lack of mono-omics data and mitigate noise interference.
  • Investigating biological problems from both causal and consequential perspectives, employing cross-validation to minimize false positives..
  • Elucidating the correlation mechanism between molecular regulation and phenotype, systematically analyzing the functionality and regulatory mechanisms of biomolecules.
  • Identifing key metabolic pathways, genes, and metabolites from extensive datasets, facilitating subsequent studies.

Analysis process

Analysis results display

Figure 6 KEGG enrichment histogram

Figure 5 Correlation nine-quadrant diagram

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