Microorganisms constantly modify their gene expression and metabolic profiles in response to alterations in their surrounding environment. Monitoring these changes is crucial for regulating microbial production of substances. However, it remains challenging to identify differential culture conditions through the extraction of differentially expressed genes and clustering of gene expression profiles. In this study, we employed a dimensionality reduction technique for yeast gene expression data obtained under multiple culture conditions to visualize discrepancies among culture conditions. Our findings indicate that this approach is effective in identifying multiple culture conditions.

中文翻译：

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用降维方法识别差异培养条件

微生物不断修改其基因表达和代谢特征以响应周围环境的变化。监测这些变化对于调节微生物物质的生产至关重要。然而，通过提取差异表达基因和基因表达谱聚类来识别差异培养条件仍然具有挑战性。在这项研究中，我们对多种培养条件下获得的酵母基因表达数据采用降维技术，以可视化培养条件之间的差异。我们的研究结果表明，这种方法可以有效识别多种培养条件。