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在RNA-seq数据的差异表达基因被建议在规范化技术的交叉点上

Mohammad Elahimanesh¹, Mohammad Najafi^1,2

¹Clinical Biochemistry Department, Faculty of Medical Sciences, Iran University of Medical Sciences, Tehran, Iran.

Biochemistry and biophysics reports

|January 11, 2024

概括

RNA-seq数据正常化显著影响喉癌中差异表达基因 (DEG) 识别. 不同的规范化方法,如TPM,FPKM和DESeq2产生不同的DEG结果,影响下游路径分析.

科学领域:

生物信息学是一种生物信息学.
基因组学就是基因组学.
癌症研究癌症研究

背景情况:

准确的RNA测序 (RNA-seq) 数据分析严重依赖于适当的数据规范化技术.
转录读取正常化对于比较不同样本的基因表达水平至关重要.
存在各种各样的规范化方法,每一种都可能对下游分析产生影响.

研究的目的:

研究不同规范化技术对喉癌中差异表达基因 (DEG) 识别的影响.
为了比较使用每百万转录 (TPM),每百万千基基数碎片 (FPKM) 和DESeq2.2获得的DEG结果.
从标准化数据来评估DEG交叉点的适用性,用于途径丰富分析.

主要方法:

使用了癌症基因组图谱 (TCGA) 喉癌数据集.
使用TPM,FPKM和DESeq2方法规范化的RNA-seq数据.
确定了每个规范化技术的差异表达基因 (DEG).
进行了DEG集的比较分析,并探索了用于路径丰富的DEG交叉点.

主要成果:

规范化技术对差异表达基因 (DEGs) 的识别产生了重大影响.
在使用TPM,FPKM和DESeq2时,报告了不同的DEG集.
选择规范化方法会影响为后续分析选择顶部成绩单.

结论:

选择RNA-seq规范化方法至关重要,并影响喉癌中DEG识别.
研究人员在解释来自各种规范化方法的DEG数据时应该意识到这些差异.
从规范化数据中利用DEG交叉点可以提高路径丰富发现的可靠性.

关键词:

DEGs 是一个DEG.数据规范化的数据规范化.通过RNA-Seq分析进行分析.

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