[BioC] normalization data with ..txt or ..xls file by marray or limma

[rwin qian]

Thanks a lot for Matt's great help!
 
Sorry, I would like to ask one more question and please excuse me for such a basic issue.
 
Once I get my cDNA microarray data, when should I delete the poor quality spots, before the normalization or after the normalization. I think it needs to be done before the normalization. In such case, what rule should I use? 
 
Thanks in advance!

Darwin

Matthew Ritchie <mritchie at wehi.edu.au> wrote:
Hi Darwin,

>I have a question as following and would like to ask for help.
> 
>Is it possible to read in and do normalization of cDNA microarray data with format as .txt or .xls by marray package or limma.
> 
>
Yes, you should be able to read in the data if it is in .txt format. 
You can save the .xls files in tab delimited text format in excel by 
going to File > Save As and selecting Save as type: Text (tab delimited).

Once you have the files in tab delimited text format, the function 
read.maimages() in limma can be used to read in the data, provided it 
is in a consistent format. Try ?read.maimages for more information or 
check out the '3.ReadingData' section in the limma help.

You can specify the columns to be stored for the red foreground (Rf) and 
background (Rb) and green foreground (Gf) and background (Gb) using the 
'columns' argument in read.maimages(). If your data comes from a 
standard image analysis program (such as Quantarray or GenePix) you 
specify this using the 'source' argument, and the relevant columns will 
be selected automatically. For example if I have the files array1.txt 
and array2.txt in the current directory, then

files <- dir(pattern="*.txt")
files
# [1] "array1.txt" "array2.txt"

RG <- read.maimages(files, columns=list(Rf="Red", Gf="Green", Rb="Red 
bg", Gb="Green bg"))

will store the columns Red, Green, Red bg and Green bg from these files 
in an RGList object. Once you have read in the data, you can use the 
normalizeWithinArrays() function to normalize the data.

Alternatively, you should be able to use the read.marrayRaw() function 
from the marrayInput library to read in the data, followed by maNorm() 
from the marrayNorm library to do the normalization. There are more 
normalization methods available in the marrayNorm library.

I hope this helps to get you started. Best wishes,

Matt Ritchie

>Thanks in advance!
> 
>Darwin
>



---------------------------------

Yahoo! Search - Find what you’re looking for faster.
	[[alternative HTML version deleted]]