[BioC] Background correction (Normexp+offset)
Juan Carlos Oliveros
oliveros at cnb.csic.es
Tue Jul 12 18:03:39 CEST 2011
I am pretty sure that the differences in the cases 1. 2. and 3. are due
to low expressed genes. The offset can affect only the results for genes
with very low intensities.
I always use normexp and offset=50. Then, I look up the MA plot to
decide which combination of Fold Change and p.value (adjusted) is better
(I highlight the results of each filter on the MA plot).
If you have difficulties for generating MA plots and comparing different
filters you can use our on-line tool FIESTA:
This tool was developed to help the users to see (graphically) the
effect of different thresholds in their results.
You just need a table (text tabulated format) with all numerical results
for all genes in the microarray, including A and M values, and p.values.
(apologies if this is not the proper place to talk about a
Hope that helps,
On 07/12/2011 05:40 PM, Kachroo, Priyanka wrote:
> Dear All,
> I needed your help with some 2-color microarray data analysis. So the problem is that after sorting by pvalue and fold change cut off of 1.5, I am left with very few differentially expressed genes. I use Normexp method for background correction with an offset value of 50 (default).
> 1. So if i use offset=50, i get downregulated genes=11, upregulated genes=31
> 2. If i use offset=25, i get downregulated=14 , upregulated=46
> 3. If i use offset=10, i get downregulated=20 ,upregulated=93
> I read on the Limma-bioconductor forum that making a boxplot of foreground and background (green and red channels) should help decide if background correction is needed or not. I made that boxplot but do not know how to interpret it. I could not attach the MA plots for the offset values 10 and 25 with this email. Can someone guide me as to how to interpret MA plots after background correction and what offset values to use.
> Also this is what the moderator writes for a way to decide the offset value " You can judge a good value for the offset by inspection of the MA-plots. If you really want a quantitative way to judge this, look at the component fit$df.prior after you use the eBayes() function in limma. The better you stabilize the variances, the larger will be df.prior and the greater will be the power to detect DE genes. Hence the offset which maximises df.prior is, in sense, optimal "
> So, when i run my code and type fit$df.prior i get a value of 1.481457. How does this number help me decide the offset.
> Priyanka Kachroo
> Graduate Assistant Research
> Texas A&M University
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