Analyse Illumina Microarray data with lumi - read in data

Question

[Tobias.Wohland]

Hi everybody, I'm new in working with microarray data. I want to analyse data which was produced by the Illumina HumanHT12.v4 chip. I started to work with lumi and I'm doing well so far but there is one question which occurred after reading more and more about the topic. More precisely I have a problem with understanding the following which is written in the lumi description:

As a result, the controlData slot in LumiBatch class was added to keep the control probe (gene) information, and a QC slot to keep the quality control information, including the "Sample Table" output by BeadStudio version 3.

They write there is the controlData slot for the control probe information and additionally the QC slot for QC information and the "Sample Table" output but what I don't get is that I can't specify to read in the "Samples Table". I mean I can add the Control Probe data but I can't for the Sample Table. Isn't it necessary? I'm feeling really stupid because I didn't find anything about this topic except this sentence in the lumi description. Maybe I missed something. It would be very nice if someone could clarify for what exactly the "Sample Table"-file is needed and whether I can read it into a lumi-batch or what to do with it.

Thanks in advance.

Best,
Tobi

[Kritika]

hi Tobias Wohland

Actually I am working on certain illumina microarray data produced by Illumina HumanHT12.v4 chip , My question is for running lumi and limma we neet to input text file, so from where will i get text file is it from genome studio ? if yes from studio is it automatically made or we have to run certain parameters?

Answer 1

[Manvendra Singh]

It's a file containing sample summary information in the same directory where your samples are.

This is lumiR.batch object

It's better described here.

what I do always is that I do not go for it

I go for lumiR function which fetch the signal compared to background from all the files and stores it in the lumiR object, in your directory there would be one ".AVG" file for each sample, which would be already containing average signal data for the respective samples.

Then just go for

dati <- lumiR("data.txt",
columnNameGrepPattern = list(exprs = "AVG_signal",
se.exprs = "BEAD_STERR", beadNum = NA, detection = "Detection Pval"))

Then you can normalize and do downstream analysis.

[Tobias.Wohland]

Thanks Manvendra, this helps, although I already knew the link :)! I was wondering whether I have to consider the file or not but it seems that one don't have to consider it, at least in terms of an lumiR object.

[Manvendra Singh]

Genau :)

lumiR object would fetch the signals which are significant against background, for me this was always the convinient way to go.

with lumiR.batch object you have to create a sample file and describe the names phenotypes and etc etc