preprocessFunnorm: Error in kmeans (initial centers are not distinct) about minfi HOT 7 OPEN

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@kasperdanielhansen Thanks for fast reply. If I instead run preprocessFunnorm(rgset, sex = "F") I get this error:

> mset <- preprocessFunnorm(rgset, sex = "F")
[preprocessFunnorm] Background and dye bias correction with noob
[preprocessFunnorm] Mapping to genome
[preprocessFunnorm] Quantile extraction
[preprocessFunnorm] Normalization
Error in oobG[2, ] : subscript out of bounds

> traceback()
3: .buildControlMatrix450k(extractedData)
2: .normalizeFunnorm450k(object = gmSet, extractedData = extractedData, 
       sex = sex, nPCs = nPCs, verbose = subverbose)
1: preprocessFunnorm(rgset, sex = "F")

No, I only need this one sample, not mixed with others.

I get similar if I try instead with quantile normalisation:

> mset <- preprocessQuantile(rgset)
[preprocessQuantile] Mapping to genome.
Error in kmeans(dd, centers = c(min(dd), max(dd))) : 
  initial centers are not distinct

> mset <- preprocessQuantile(rgset, sex = "F")
[preprocessQuantile] Mapping to genome.
[preprocessQuantile] Fixing outliers.
[preprocessQuantile] Quantile normalizing.
Error in if (ncol(mat) == 1) return(mat) : argument is of length zero

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@kasperdanielhansen Do you have any ideas of what can cause this? Again, I can send you the idat-files (via a private channel), if that can help?

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In most preprocessing methods we need to do something special for the sex chromosome (for example due to X inactivation). To do this well, we need to know the sex of the samples. We have a standard way of estimating the sex of the samples using kmeans, and this step fails. It could fail for a number of reasons, the top contenders are (a) you only have 1 sex (the code assumes there are both males and females) (b) you have cancer samples with big CN changes on the sex chromosomes.

In case (a) or (b) you can override the prediction by directly supplying a vector of sex.

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In case (a) or (b) you can override the prediction by directly supplying a vector of sex.

@kasperdanielhansen I already tried with the sex = "F" argument (cf. above), and it still fails. Is there another way to supply the sex?

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Ok, I am sorry, I see I basically wrote the same thing twice.

I also see you're trying to run 1 sample through functional normalization, right? That won't work. We essentially remove between-sample variation by regressing out certain confounders and that approach won't work for 1 sample processing.

Are you working in a prediction setting? If so, I would look into using Noob (in its single-sample mode, which is the default). Noob is "true" single sample normalization which means normalizing 1 sample is the same as normalizing many samples together.

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I also see you're trying to run 1 sample through functional normalization, right? That won't work. We essentially remove between-sample variation by regressing out certain confounders and that approach won't work for 1 sample processing.

Yes, I started with that. But I also tried preprocessQuantile - that should work with one sample, right?

Are you working in a prediction setting? If so, I would look into using Noob (in its single-sample mode, which is the default). Noob is "true" single sample normalization which means normalizing 1 sample is the same as normalizing many samples together.

Thanks, I will try that, too.

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