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Betty Gilbert
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@betty-gilbert-1120
Last seen 10.2 years ago
Hello,
If this has been discussed in the archives, my apologies but I
couldn't find it. I am comparing two array CGH datasets, one
generated by Nimblegen which is very complete and one generated by
myself on a 70mer array with over 10,000 elements which has 3-4
replicates for three species I have Nimblegen data for. I have
calculated corrected pvalues for the nimblegen set using multtest and
would like to do so for the 70mer set but have issues with missing
data. I used t-tests, testing for variance, that filter out or
disregard the missing data for the 70mer set already using the
program ACUITY to calculate p-values.
I wanted to compare the corrected p-values after using a method to
impute the missing data to see how different the results are from
filtered dataset.
My question: For a 70mer array with one oligo per open reading frame
what method of data imputation is best statistically. I looked over
the knn method in the package impute (mostly recommended for
expression data) and impute.lowess in the package aCGH which may be
optimized for high density arrays from what i can tell and my
apologies if that is not the case.
Does anyone have any recommendations about which method for imputing
data I should try for a 70mer platform? Thank you for your time.
Sincerely,
Betty Gilbert
--
Betty Gilbert
lgilbert at berkeley.edu
Taylor Lab
Plant and Microbial Biology
321 Koshland Hall
U.C. Berkeley
Berkeley, Ca 94720