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Less routine, more sincerity-表达芯片数据-差异分析-注释

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发表于 2016-10-24 09:13:51 | 显示全部楼层 |阅读模式
本帖最后由 ydchen 于 2016-10-24 09:33 编辑

取这个题目是为了讽刺!
看到某知名公众号发布一篇文章;不用做实验的2.5分SCI,吃顿饭的功夫就能做完说到:这篇文章发表在Med Oncol(IF=2.486),题目是:Identification of key pathways and genes in colorectal cancer using bioinformatics analysis,虽然只有两分半,但看题目就知道了,这篇文章都不需要做实验,随便搞搞就可以出来了,吃顿饭就能完成所有的分析过程。
的确,里面非常简单,就是分析一个公共芯片数据GSE21815,芯片平台是Agilent GPL6480 platform (Agilent GeneChip Human Genome Microarray 4 × 44 K G4112F)
我实在不明白,这种套路为什么直到现在(2016年末),还能持续灌水发文章,但是正好给大家做个项目实战吧!


The GSE21815 dataset contained 141 samples, including 132 CRC and 9 normal colon epitheliums. The gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) enrichment analyses were performed, and protein-protein interaction (PPI) network of the differentially expressed genes (DEGs) was constructed by Cytoscape software. In total, 3500 DEGs were identified in CRC, including 1370 up-regulated genes and 2130 down-regulated genes. GO analysis results showed that up-regulated DEGs were significantly enriched in biological processes (BP), including cell cycle, cell division, and cell proliferation; the down-regulated DEGs were significantly enriched in biological processes, including immune response, intracellular signaling cascade and defense response. KEGG pathway analysis showed the up-regulated DEGs were enriched in cell cycle and DNA replication, while the down-regulated DEGs were enriched in drug metabolism, metabolism of xenobiotics by cytochrome P450, and retinol metabolism pathways. The top 10 hub genes, GNG2, AGT, SAA1, ADCY5, LPAR1, NMU, IL8, CXCL12, GNAI1, and CCR2 were identified from the PPI network, and sub-networks revealed these genes were involved in significant pathways, including G protein-coupled receptors signaling pathway, gastrin-CREB signaling pathway via PKC and MAPK, and extracellular matrix organization. In conclusion, the present study indicated that the identified DEGs and hub genes promote our understanding of the molecular mechanisms underlying the development of CRC, and might be used as molecular targets and diagnostic biomarkers for the treatment of CRC.


套路很简单,就是处理芯片数据,拿到表达矩阵,根据GEO数据库里面对芯片数据的分组描述来做差异分析,根据criteria来选取差异基因列表!然后对差异基因列表做GO/KEGG的注释,一般是富集分析,同时对这些基因做string数据库的PPI分析,找找hub的gene





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 楼主| 发表于 2016-10-24 09:34:19 | 显示全部楼层
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发表于 2017-4-27 19:51:29 | 显示全部楼层
请问有没有Agilent芯片分析的教程啊?数据是从GEO下载的TXT,R包的话该用什么样的包?
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