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GO and KEEG enrichment were performed to analyze the biological functions and pathways of the DEGs. A protein-protein interaction (PPI) network of the identified DEGs was constructed using the STRING database. Differences in prognosis between low and high expression of the hub DEGs were also evaluated using the Kaplan-Meier Plotter database.
Learn how to find genes that show a difference in expression between sample groups using R packages edgeR and DESeq2. Follow the steps to pre-process, normalize, calculate fold-change and perform statistical tests on count data.
Heatmaps based on the number of DEGs, by comparison, can also be used to display the same information summarily. Using a spectrum of colors based on the magnitude of the DEG counts, DEG heatmaps can provide a straightforward method that is easily readable and interpretable. For DEG heatmaps, each cell represents the number of DEGs for the ...
本文介绍了如何使用DESeq2 R包对RNA-seq数据进行差异表达基因 (DEG)分析，包括数据准备、导入、导出、可视化等步骤。DEG分析是一种统计学方法，用于比较实验组和对照组的基因表达水平，找出有差别的基因。
DEGs are genes that show differential expression in response to various stimuli or conditions. This review summarizes the tools and strategies for analyzing and interpreting DEGs, and provides a website DEGMiner for accessing over 300 databases and tools.
This article presents an extended review on the methods and softwares for identifying differentially expressed genes (DEGs) from RNA-Seq data, and a software tool that performs the analysis. The review evaluates six methods of mapping reads and nine methods of differential expression analysis, based on real RNA-Seq data and qRT-PCR data.
This analysis showed that DEGs often reflect disease-induced changes in the transcriptome rather than disease-causing ones. Importantly, we observed that most of the correlation between gene ...
Step 4: Extract/filter the DEGs. Now, since the differential expression analysis is completed and we can extract the differentially expressed genes given the contrast condition. The output containing the top features includes the feature specific details on log fold change, average expression, t test value, p value and adjusted p value.
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