Thank You for the reply Gordon,

I have been trying to get this to work for the whole day yesterday and
realized that my data files would need a considerable amount of parsing
before they could be imported into R. Therefore I decided that the easiest
and errorless approach will be to use my mysql database into which I had
already imported the data. Out of it I get a file with 3 columns
corresponding to "gene id", "sample id" and "expression value". Then I do
the following:

# initialize a matrix with just zeros; of the correct size (I have 76455
genes and 3001 samples)
  data_matrix <- matrix(0, 76455, 3001)
# read in the expression data exported from the mysql database
  expression <- read.table(file.choose(), header=TRUE)
# populate the empty matrix with the data having the columns as sample
indices and rows as gene indices
  mtx <- as.matrix(expression)
  data_matrix[mtx[,1:2] ]<- mtx[,3]
# use the normalization method from edgeR
  d <- calcNormFactors(data_matrix, method="RLE")

I thought this should work (and it does when I try it with a small dummy
file), but when I apply it to my own data (1.4GB) I get all the
normalization factors which are "NA". I did print out some of my
'data_matrix' and it does seem to be populated correctly, so I really don't
know what is causing this now. Could it be something to do with the amount
of data - would I have to use doubles instead of floats or something like
that?

Best wishes,

Jan



On 12 May 2013 04:43, Gordon K Smyth <smyth@wehi.edu.au> wrote:

> Dear Jan,
>
> The first step is to read the documentation!  Page 9 of the edgeR User's
> Guide says:
>
> "If the counts for different samples are stored in separate files, then
> the files have to be read separately and collated together.  The edgeR
> function readDGE is provided to do this.  Files need to contain two
> columns, one for the counts and one for a gene identifier.  See the SAGE
> and deepSAGE case studies for examples of this."
>
> The readDGE() function does exactly what you want to do. Type ?readDGE at
> the R prompt.
>
> Best wishes
> Gordon
>
>  Date: Fri, 10 May 2013 16:32:38 +0100
>> From: Jan Zaucha <Jan.Zaucha@bristol.ac.uk>
>> To: bioconductor@r-project.org
>> Subject: [BioC] calcNormFactors in edgeR - quick question from a very
>>         inexperienced user
>>
>> Hi,
>>
>> I'm totally new to the field, I've never used R before, but I need to
>> normalize some expression data.
>>
>> In every file I have many columns corresponding to different samples
>> (different source cells) and rows corresponding to different genes. However
>> I have many different files corresponding to different experiments and they
>> have different total numbers of rows (genes).
>>
>> I want to use RLE normalization to normalize all of the data, which is
>> implemented in the function calcNormFactors from the package edgeR, but I
>> don't understand how can I put the read counts into a matrix since my files
>> contain different numbers of genes (rows).
>>
>> I thought I should have a giant matrix containing data from all of my
>> files where the columns are the samples and rows are the genes.
>>
>> Should I perhaps take the file that has the highest number of genes and
>> input "0" for these genes if they are not present in the other files?
>>
>> Thanks for your time.
>> Jan
>>
>>
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