This analysis was performed using R (ver. 3.1.0).
Download raw data
The raw data files for this lab are in the
rawdata repository, available here:
Click Download ZIP in order to download all the files, unzip this file which should result in a
rawdata-master folder. Rename this folder to rawdata.
Read Agilent data
Agilent data is a two color arrays. For two-color arrays it’s slightly more complicated, because you have a pairing of files (red and green channels). Different scanners spit out different formats for this. So the target information will be a little bit different. limma package is used to read the files. The readTargets function tells you what’s in the red and the green channels.
The function read.maimages(microarray images) is used to read the data. You have to tell it what software produced the images. There’s different software for that, although these days, there’s a default that works pretty broadly. So all we’re doing now is telling it where the files are. We tell it what imaging software was used to produce these files (“genepix”). The function read.maimages, stores red and green separately.
library(limma) library(rafalib)#installed from github #Define a base directory basedir <- "~/hubiC/Documents/R/doc/english/genomics/rawdata/agilent" setwd(basedir) #Read sample information table : it tells you what's in the red channel and the green channel targets <- readTargets("TargetBeta7.txt") #Read microarray files : Red and green are stored separately RG <- read.maimages(targets$FileName, source="genepix")
## Read 6Hs.195.1.gpr ## Read 6Hs.168.gpr ## Read 6Hs.166.gpr ## Read 6Hs.187.1.gpr ## Read 6Hs.194.gpr ## Read 6Hs.243.1.gpr
Data are normalized using MA.RG function. It stores the information as the log ratio– that’s the M– and the average of the logs, which is the A.
#Normalization MA <- MA.RG(RG,bc.method="none")
Just to give you a quick idea of what you can do with this. You can do an MA plot. You just plot the A versus the M.
#Normalization MA <- MA.RG(RG,bc.method="none") #MA plot plot(MA$A[,1],MA$M[,1])#MA plot for the first sample
Another nice feature is to make images pretty quickly.
#Array image imageplot(MA$M[,2], RG$printer, zlim=c(-3,3))
This is a nice example where we see an image where there seems to be a little bit of a problem. If you look at the edge, you see that it's quite green, compared to the middle, which is redder. Then you have too much red on the left. So we're detecting that there's somewhat of a problem in this file.
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