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readme.md

Best practice for mRNA microarray

Note : Please don't use it if you are not the fan of our biotrainee, Thanks.

Install required packages by the codes below:

R包是基础!

options()$BioC_mirror
options()$repos
install.packages('shiny')
install.packages(c("devtools","ggplot2","pheatmap"))
install.packages(c("ggpubr","ggstatsplot"))

source("http://bioconductor.org/biocLite.R") 
library('BiocInstaller') 
biocLite(c('airway','DESeq2','edgeR','limma')) 
biocLite('clusterProfiler')

But if you are in China, you should use the codes below:

options("repos" = c(CRAN="https://mirrors.tuna.tsinghua.edu.cn/CRAN/"))
install.packages("devtools",
			   repos="https://mirrors.tuna.tsinghua.edu.cn/CRAN/")
library(devtools) 
source("https://bioconductor.org/biocLite.R") 
options(BioC_mirror="https://mirrors.ustc.edu.cn/bioc/")  
BiocInstaller::biocLite(c('airway','DESeq2','edgeR','limma')) 
BiocInstaller::biocLite(c('ALL','CLL','pasilla','clusterProfiler')) 


library(devtools) 
source("https://bioconductor.org/biocLite.R") 
options("repos" = c(CRAN="https://mirrors.tuna.tsinghua.edu.cn/CRAN/"))
options(BioC_mirror="https://mirrors.ustc.edu.cn/bioc/")  
BiocInstaller::biocLite('org.Hs.eg.db')
install.packages("remotes",repos="https://mirror.lzu.edu.cn/CRAN/") 
install.packages("pheatmap",repos="https://mirror.lzu.edu.cn/CRAN/")

It will install many other packages for you automately, such as : ALL, CLL, pasilla, airway ,limma,DESeq2,clusterProfiler , that's why it will take a long time to finish if all of these packages are not installed before in your computer.

Then run step1 :

数据是灵魂!

It always not very easy to download data if you are in China, so I also upload the file GSE42872_raw_exprSet.Rdata , you can load it directly.

if(F){
  library(GEOquery)
  gset <- getGEO('GSE42872', destdir=".",
                 AnnotGPL = F,
                 getGPL = F)
  save(gset,'GSE42872.gset.Rdata')
}
load('GSE42872_eSet.Rdata')
b = eSet[[1]] # 看清楚,某些GSE数据集有多个平台,注意挑选
raw_exprSet=exprs(b) 
group_list=c(rep('control',3),rep('case',3))
save(raw_exprSet,group_list,
     file='GSE42872_raw_exprSet.Rdata')

Then step2:

高质量的数据是保障!

Try to understand my codes, how did I filter the probes by the annotation of each microarry, and how I check the group information for the different samples in each experiment.

Including PCA and Cluster figures, as below:

Cluster

PCA

Please ensure that you do run those codes by yourself !!!

Then step3:

差异分析是核心流程

Normally we will do differential expression analysis for the microarray, and LIMMA is one of the best method, so I just use it. If the expression matrix(raw counts ) comes from mRNA-seq, you can also choose DESeq based on negative binomial (NB) distributions or baySeq and EBSeq.

Once DEG finished, we can choose top N genes for heatmap as below:

heatmap

and volcano plot as below:

Then step4 :

数据库的注释是升华!

Annotation for the significantly changed genes, over-representation test or GSEA for GO/KEGG/biocarta/rectome/MsigDB and so on.

KEGG_GSEA

KEGG-enrichment

Step5: survival analysis

生存分析是补充!

KM and cox

Step6: GSEA for Molecular Signatures Database (MSigDB)

算法是亮点

Step7: GSVA for Molecular Signatures Database (MSigDB)

算法是门槛

更多其它例子

The videos tutorials :

All the videos are uploaded in YouTube: https://www.youtube.com/channel/UC67sImqK7V8tSWHMG8azIVA/videos

如果你在中国,你可能会喜欢B站: https://www.bilibili.com/read/cv719181 ,视频链接: https://www.bilibili.com/video/av26731585/

番外

其实不止是针对转录组表达芯片的数据分析教材,还有转录组数据处理流程,希望你可以仔细看,还有批量生存分析等各种其它统计分析方法我也会慢慢添加。

主要是根据大家的需求啦,希望大家多多反馈和提问哈!

最重要的是:

如果你觉得我的教程对你有帮助,请赞赏一杯咖啡哦!

如果你的赞赏超过了50元,请在扫描赞赏的同时留下你的邮箱地址,我会发送给你一个惊喜哦!

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