Hello,
I've begun processing Methylation450 data using them infi package. The operation has been flawless through data input: (read.450k.exp), phenotype table (pData), Illumina background normalization (preprocessIllumina), and SWAN normalization (preprocessSWAN). However, I am some difficulty with dmpFinder that I cannot figure out or find on any chat/help forums other than 'Iimma' but they have not provided a solution.
After successfully creating a SWAN normalized dataset I'm ready to find differentially methylated regions between individuals at the start and end of a trial (e.g.,"0" and "1") using dmpHunter. However, I am getting an error that I cannot solve: Error in lm.fit(design, t(M)) : incompatible dimensions. Here is my process that produces the error.
>pd1 <- pData(RGset)
>Mset.swan <- preprocessSWAN(RGset, MSet.norm)
>M <- getM(Mset.swan, type = "beta", betaThreshold = 0.001)
>dmpTimePoint <- dmpFinder(M, pheno=pd1$TimePoint, type = "categorical")
Error in lm.fit(design, t(M)) : incompatible dimensions
At first I thought the issue was with the dimensions of 'M' or 'pd', but they're both 168:
> dim(M)
[1] 485512 168
> dim(pd)
[1] 168 17
I then tried removing any 'NA' values in 'pd' but that resulted in the same error message:
Error in lm.fit(design, t(M)) : incompatible dimensions
As requested, here is the output for the sessionInfo() command:
R version 3.0.1 (2013-05-16)
Platform: x86_64-apple-darwin10.8.0 (64-bit)
locale:
[1] en_CA.UTF-8/en_CA.UTF-8/en_CA.UTF-8/C/en_CA.UTF-8/en_CA.UTF-8
attached base packages:
[1] parallel stats graphics grDevices utils datasets methods base
other attached packages:
[1] GPArotation_2012.3-1
[2] psych_1.4.8.11
[3] minfiData_0.3.4
[4] IlluminaHumanMethylation450kannotation.ilmn.v1.2_0.1.3
[5] IlluminaHumanMethylation450kmanifest_0.4.0
[6] minfi_1.6.0
[7] Biostrings_2.28.0
[8] GenomicRanges_1.12.5
[9] IRanges_1.18.4
[10] reshape_0.8.5
[11] lattice_0.20-29
[12] Biobase_2.20.1
[13] BiocGenerics_0.6.0
loaded via a namespace (and not attached):
[1] beanplot_1.1 grid_3.0.1 illuminaio_0.2.0
[4] limma_3.16.8 MASS_7.3-34 matrixStats_0.10.0
[7] mclust_4.3 multtest_2.16.0 nor1mix_1.2-0
[10] plyr_1.8.1 preprocessCore_1.22.0 R.methodsS3_1.6.1
[13] RColorBrewer_1.0-5 Rcpp_0.11.2 siggenes_1.34.0
[16] splines_3.0.1 stats4_3.0.1 survival_2.37-7
[19] tools_3.0.1
Thank you in advance, any and all help would be greatly appreciated.
Sincerely,
Mike Zulyniak
McMaster University
Hi Jim,
Thanks for the reply. I'll give it shot and let you know how it turns out. The debug() function is a new one to me but certainly makes sense as a good place to start.
I'll let you know how it turns out.
Mike