不完美的iDEP分析程序折腾

折腾完fastqc，trimmomatic和salmon后，开始折腾转录组差异化表达矩阵了。最主流最码农向的当然是直接用R跑DEseq2了，但是其实这两年又推出不少的在线分析工具。例如:
iDEP
BioJupies
networkanalyst
IRIS
对于模式植物或者人来说可能哪个都差不多，但是对于小众的植物可能支持最好的还是iDEP吧。毕竟在一开始你就能选择你需要的物种。

使用也很简单，首先设置好你的experiment design：

Study_design	p53_mock_1	p53_mock_2	p53_mock_3	p53_IR_1	p53_IR_2	p53_IR_3
p53	wt	wt	wt	wt	wt	wt
Treatment	mock	mock	mock	IR	IR	IR

然后用另一个txt填入你需要的数据：

Data Matrix	p53_mock_1	p53_mock_2	p53_mock_3	p53_IR_1	p53_IR_2	p53_IR_3
ENSMUSG00000028995	3210	2553	2096	1054	663	1012
...	...	...	...	...	...	...
ENSMUSG00000093392	7	25	0	10	0	22

因为我使用的salmon默认有read counts data所以直接用第一个就好了。这个数据有助于后续筛选高表达基因基因ID。筛选过程你可以用excel读取txt或者csv做列操作，当然也能使用TBtools。如果你想学可以考虑这个66.66你买不了吃亏买不了上当。如果你对data format有兴趣可以读一下这个。
剩下的操作在线平台都会帮你完成了……
最后附上iDEP的流程图……

iDEP工作流程图

故事到这里就该结束了，但其实还有更多值得折腾的东西。比如我们可以在自己的笔记本上跑iDEP！而这也是我接下来做的事情……经过两天的折腾，走了不少弯路（因为官方提供的教程已经过时了！）。现在把安装的过程分享在网络上。结果并不完美，原因最后会说。

1.把你的R和Rstudio版本升级到最新

2. 运行这个脚本

# dplyr complains this required libraries: libudunits2-dev, libmariadb-client-lgpl-dev
# install.packages("plotly", repos="http://cran.rstudio.com/", dependencies=TRUE)
# sometimes need to remove all installed packages: https://www.r-bloggers.com/how-to-remove-all-user-installed-packages-in-r/ 
list.of.packages <- c(
  "shiny", "shinyAce", "shinyBS", "plotly",
  "RSQLite", "gplots", 
  "ggplot2", "dplyr", #"tidyverse",
  "plotly",
  "e1071", "reshape2", "DT",
  "data.table", "Rcpp","WGCNA","flashClust","statmod","biclust","igraph","Rtsne",
  "visNetwork", "BiocManager"
)

list.of.bio.packages  <- c(
  "limma", "DESeq2", "edgeR", "gage", "PGSEA", "fgsea", "ReactomePA", "pathview", "PREDA",
  "impute", "runibic","QUBIC","rhdf5", "STRINGdb",
  "PREDAsampledata", "sfsmisc", "lokern", "multtest", "hgu133plus2.db", 
   "org.Ag.eg.db","org.At.tair.db","org.Bt.eg.db","org.Ce.eg.db","org.Cf.eg.db",
   "org.Dm.eg.db","org.EcK12.eg.db","org.EcSakai.eg.db","org.Gg.eg.db",
   "org.Hs.eg.db","org.Mm.eg.db","org.Mmu.eg.db","org.Pf.plasmo.db",
   "org.Pt.eg.db","org.Rn.eg.db","org.Sc.sgd.db","org.Ss.eg.db","org.Xl.eg.db"
)

 if(1) { # remove all old packages, to solve problem caused by Bioconductor upgrade
    # create a list of all installed packages
     ip <- as.data.frame(installed.packages())
    # head(ip)
    # if you use MRO, make sure that no packages in this library will be removed
     ip <- subset(ip, !grepl("MRO", ip$LibPath))
    # we don't want to remove base or recommended packages either\
     ip <- ip[!(ip[,"Priority"] %in% c("base", "recommended")),]
    # determine the library where the packages are installed
     path.lib <- unique(ip$LibPath)
    # create a vector with all the names of the packages you want to remove
     pkgs.to.remove <- ip[,1]
    # head(pkgs.to.remove)
    # remove the packages
     sapply(pkgs.to.remove, remove.packages, lib = path.lib)
}

new.packages <- list.of.packages[!(list.of.packages %in% installed.packages()[,"Package"])]
new.bio.packages <- list.of.bio.packages[!(list.of.bio.packages %in% installed.packages()[,"Package"])]
notInstalledPackageCount = length(new.packages) + length(new.bio.packages)

#Install Require packages
while(notInstalledPackageCount != 0){

    if(length(new.packages)) install.packages(new.packages, repos="http://cran.rstudio.com/", dependencies=TRUE, quiet=TRUE)
    if(length(new.bio.packages)){
        BiocManager::install(new.bio.packages, ask = FALSE, dependencies=TRUE, quiet=TRUE)
    }

    new.packages <- list.of.packages[!(list.of.packages %in% installed.packages()[,"Package"])]
    new.bio.packages <- list.of.bio.packages[!(list.of.bio.packages %in% installed.packages()[,"Package"])]
    if( notInstalledPackageCount == length(new.packages) + length(new.bio.packages) )
    {
        #no new package installed.
        break
    }
    else {
       notInstalledPackageCount = length(new.packages) + length(new.bio.packages)
    }
}

#Load Packages
suc = unlist ( lapply(list.of.packages, require, character.only = TRUE) )
if(sum(suc) < length(list.of.packages) )
    cat ("\n\nWarnning!!!!!! These R packages cannot be loaded:", list.of.packages[!suc] )

suc = unlist ( lapply(list.of.bio.packages, require, character.only = TRUE) )
if(sum(suc) < length(list.of.bio.packages) )
    cat ("\n\nWarnning!!!!!! These Bioconductor packages cannot be loaded:", list.of.bio.packages[!suc] )

如果遇到问题，你可以根据提示手动重新安装一下你需要的bio包，具体方法为：

BiocManager::install("bio包的名字")

3. 你需要找到iDEP的github，下载所有文件。
4. 继续下载这几个文件pathwayDB, Motif, geneInfo, data_go, convertIDs
5. 对于自己电脑上使用iDEP需要看shinyapps的位置。例如如果你的文件位置为：
   \idep\shinyapps
   你需要把之前下载的pathwayDB，Motif，geneInfo，data_go，convertIDs文件解压缩，并放在如下地址：
   \idep\data\data92\data_go
   \idep\data\data92\geneInfo
   \idep\data\data92\motif
   \idep\data\data92\pathwayDB
   \idep\data\data92\convertIDs.db
6. 找到\idep\shinyapps\idep\STRING10_species.csv
   把该文件复制到\idep\data\data92\data_go下
7. 此时如果你运行\idep\shinyapps\idep82下的
   server.R
   ui.R
   然后在rstudio里执行run app就可以了

---

整个过程并不完美因为最新的版本已经更新到了v0.92，支持了更多的物种也因此STRING10_species.csv升级为STRING11_species.csv，其他数据库也有了相应的更新。
但是作者并没有上传新的数据库文件，所以离线版本肯定没办法用最新的了。
如果你想折腾可以把
\idep\data\ data92
\idep\data\data92\data_go\STRING10_species.csv
改为
\idep\data\ data100
\idep\data\data100\data_go\STRING11_species.csv
就可以运行最新的v0.92版本了，但是结果并不完美。例如小麦转录组在online能正常运行，但是在离线版上提示找不到gene ID……查看数据库发现小麦使用的还是最早的TGAC数据……这个工程量就大了，只能等作者释出最新的数据库了。