normalization data with ..txt or ..xls file by marray or limma
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Matthew Ritchie ▴ 1000
@matthew-ritchie-650
Last seen 7 months ago
Australia
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 >
Normalization limma marray Normalization limma marray • 1.0k views
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rwin qian ▴ 50
@rwin-qian-648
Last seen 8.6 years ago
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@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 youÂ’re looking for faster. [[alternative HTML version deleted]]
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Matthew Ritchie ▴ 1000
@matthew-ritchie-650
Last seen 7 months ago
Australia
Hi Darwin, >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? > A good question, and not an easy one to answer. Firstly defining poor quality spots can be done in many ways (there are a number of papers on the subject, which I can send you the references for if you're interested). Most involve coming up with a spot specific measure, and filtering (removing) genes with an unfavourable value of this measure from subsequent analysis. In limma, spot quality weights can be used in the normalization and linear models to do this. Log-ratios from spots which are assigned low weights (close to 0) have less influence in the normalization and linear model fit compared to spots with high weights (around 1). Spots with 0 weights are ignored. These relative weights can be automatically determined from data coming out of the image analysis programs Spot and GenePix. The weights for Spot are based on the ideal spot size (spots smaller and larger than ideal are down-weighted), and for GenePix, they are derived from the quality flags (good spot - 0 flag, full weight, bad spot - negative flag, low weight). Specifying the 'weights' argument in normalizeWithinArrays() and lmFit() makes use of the weights in the normalization and linear model analysis. At the end of this message is an example which might be helpful. Does the image analysis package you're using provide any quality flags that you might be able to use? Sorry I don't have a more definite answer to your question. Best wishes, Matt Ritchie # Set up a random dataset of 6 replicate arrays with 100 genes on each array RG <- new("RGList", list(R=matrix(rnorm(100*6, 1000, 300), 100, 6), G=matrix(rnorm(100*6, 1000, 300), 100, 6), Rb=NULL, Gb=NULL)) RG$printer <- list(nspot.r=5, nspot.c=4, ngrid.r=1, ngrid.c=5) # specify the array grid layout RG$weights <- matrix(1, 100, 6) # define the weights. All spots are given full weight (1), except RG$weights[1,] <- 0 # for the observations for gene 1 (deemed to be poor quality) RG$weights[,1] <- 0 # and the observations from array 1 (bad array) # spots with 0 weights (from array 1, and gene 1 in this example are ignored in the normalization and linear model fit MA <- normalizeWithinArrays(RG, weights=RG$weights) fit <- lmFit(MA, weights=RG$weights) fit <- eBayes(fit) >Thanks in advance! > >Darwin >
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