Question: preprocessFunnorm in minfi
0
3.4 years ago by
b.tawk0
b.tawk0 wrote:

Hi everyone,

I am relatively new to R. I am working with 450K and trying to normalize my data using the minfi package.

Besides the QC, checking for bad probes, these are the following steps I use:

require(minfi)
require(minfiData)
base="XYZ"
MSET.fun<-preprocessFunnorm(RGset)

However, every time I try to run the "MSET.fun<-preprocessFunnorm(RGset)", I get a warning message in the end

"[preprocessFunnorm] Background and dye bias correction with noob
[preprocessNoob] Using sample number 66 as reference level...
[preprocessFunnorm] Mapping to genome
[preprocessFunnorm] Quantile extraction
[preprocessFunnorm] Normalization
Warning message:
In .getSex(CN = CN, xIndex = xIndex, yIndex = yIndex, cutoff = cutoff) :
An inconsistency was encountered while determining sex. One possibility is that only one sex is present. We recommend further checks, for example with the plotSex function."

However, using the plotSex function, predictedSex and using my clinical annotation, I know that I have males and females in this dataset.

I am not able to pinpoint where the error in the process is. It could be really obvious for everyone else. I would really appreciate any advice I get at this point.

Thanks a lot,

Bouchra

modified 3.4 years ago by James W. MacDonald50k • written 3.4 years ago by b.tawk0

The issue isn't whether or not you have both genders, but whether or not the sex determination is consistent when predicted in two different ways. When you use plotSex() with the known sexes, does the plot make sense (e.g., are all the males blue and the females pink)?

Thank you for the reply! There is a small discrepancy in 2/100 samples (something that can be expected to happen in clinical databases) but otherwise, in all the other samples, the predictedSex matches the clinical annotation.

0
3.4 years ago by
United States
James W. MacDonald50k wrote:

That clears it up then. The function is checking to see if everything looks OK, sees two samples that are questionable and gives you a warning to check them out. If it falls within your expectation, then you can just go ahead with the rest of the analysis.

Super! Thank you very much!

Best,

B