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表达芯片数据分析实战二:GPL15207-nash疾病

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发表于 2016-9-5 21:35:51 | 显示全部楼层 |阅读模式
首先需要自己读懂这篇文章:
  • du Plessis J, van Pelt J, Korf H, Mathieu C et al. Association of Adipose Tissue Inflammation With Histologic Severity of Nonalcoholic Fatty Liver Disease. Gastroenterology 2015 Sep;149(3):635-48.e14. PMID: 26028579
这个芯片平台(GPL15207        [PrimeView] Affymetrix Human Gene Expression Array)还是比较新的,跟大家熟悉的affy系列芯片不一样,芯片设计比较复杂,所以很多人不知道如何把芯片的探针ID转换为所对应的基因,我后面给出了参考解决方案。而且实验设计也很复杂,Patients were grouped according histology: group I (<5% steatosis), group II (NAFLD, 30-50% steatosis), group III (NASH) and group IV (NASH + fibrosis F2-F3).所以不仅仅可以做不同分组直接的差异分析,而且还有时间序列的分析,还是蛮有趣的!
其余的项目介绍,大家可以自己看:http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE58979
Nonalcoholic fatty liver disease (NAFLD) has become the most common cause of liver disease affecting 20-30% of the population in developed countries. NAFLD is strongly associated with abdominal obesity and is recognized as the hepatic manifestation of the metabolic syndrome. In a subgroup of patients with NAFLD inflammation and fibrosis develops, this so-called Non-Alcoholic Steatohepatitis (NASH) may progress to cirrhosis and hepatocellular carcinoma. A multi-hit hypothesis has been proposed in which during the first “hit” fat accumulation occurs in hepatocytes from excessive delivery of fatty acids from adipose tissue, in addition there is an imbalance in lipid synthesis and export. However, the reason why fat accumulation is subsequently followed by inflammation and fibrosis in some patients is poorly understood. We studied the role of inflammatory processes in visceral and subcutaneous fat at the transcriptional level using microarray in bariatric patients from whom the liver histology was available.实验设计流程是:
Patients scheduled for bariatric surgery were recruited in two centers (Pretoria/South-Africa and Antwerpen/Belgium). At the time of the procedure, tissue samples of the visceral and subcutaneous fat were taken for molecular analysis as well as liver tissue for histology, also full biochemical data was collected. Patients were grouped according histology: group I (<5% steatosis), group II (NAFLD, 30-50% steatosis), group III (NASH) and group IV (NASH + fibrosis F2-F3).
The following samples were used for microarray (number of 'patients' respectively for stages I-II-III-IV): visceral fat (9-7-7-5), subcutaneous fat (6-6-6-5). Microarrays were run in two batches (15xxx versus 17xxx CEL file samples; indicated in the description field). Samples from two patients (FN61; FN76) were put twice on array (15078+17881; 15064+17877) to verify and exclude batch effects.
Please note that, due to technical repeats with two patient samples, the number of 'visceral fat samples' for stages I-II-III-IV is 10-7-8-5.


芯片数据分析的套路很简单,就是下载芯片数据文件,用R包处理成表达矩阵,然后根据分组信息来做差异分析,最后选取合适的显著性的差异基因做一些功能性分析,如果作者有做其它数据,就结合其它数据一起分析。
也可以对表达矩阵做其它的统计学分析来找有意义的功能改变信息,比如GSEA






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 楼主| 发表于 2016-9-5 21:37:20 | 显示全部楼层
参考分析方案:https://github.com/jmzeng1314/my ... les/GSE58979-NASH.R
这样的分析很明显是不够的,大家需要在这个基础什么加深
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