Hi

After reading all the responses to my e-mail I have come up with the following idea:

Read the data with lumiR
Normalize it with lumiN using the quantile parameter.
Get the expression values: norm_data <- exprs(data_norm)
Use log2 on the expression data: data_n_exp <- log2(norm_data) or use vsn or ....
Use data_n_exp for the statistical analysis (for limma)

Just one thing: Should I log2 transform the data before or after the normalization?

Regards,
Ingrid



 
Hello,

limma's lmFit expects log-transformed expression values as input, and
the returned "log fold-change" is then about the difference between the
mean expression levels in each group, which given your input data may
very well be 5000 but this is not the proper log fold change at all. I
don't know what kind of data preprocessing you performed but you may
want to make sure that you feed log-transformed data into lmFit.

Regards,
Joern

Ingrid H. G. Østensen wrote:
> Hi
>
> I am using limma to analyze Illumina expression data (two groups), and this time I got some really high logFC values for some genes and "low" for others. Example:
>
> Probe       log2 Ratio(logFC)     Moderated t-statistic (t)  Raw p-value    Adjusted p-value     B
> ILMN_27575	5443.972	         27.305	               1.81E-06	     0.009621899	-2.29002
>
> ILMN_14823        42.5	                 19.077	               1.00E-05	     0.022251754	-2.32116
>
> The first gene has intensities in one group (3 samples) around 10 000 and in the other group (3 samples) around 5000, and the second gene has intensities around 110 in the first group and around 80 in the second group. 
>
> I have never seen so high logFC values before, are they realistic? Does this values mean that there are big differences combined with hight intensities?
>
> Regards,
> Ingrid
>
>   



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