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Question: unable to install GenomeWideSNP_6 cdf
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gravatar for vinaysbharadhwaj
4 months ago by
vinaysbharadhwaj0 wrote:

Hi,

I am trying to install GenomeWideSNP_6 using makecdfenv() but I am unable to do so. I keep getting the error 

make.cdf.package("GenomeWideSNP_6.cdf", cdf.path="/USER/Downloads/E-GError in make.cdf.env(filename, cdf.path = cdf.path, compress = compress,  : gpath  makecdfenv does not currently know how to handle cdf files of this type (genotyping).

I am new to BiocLite and SNP analysis in Microarray data. Please help me.

And I did try installing the pd.genomewidesnp.6 using biocLite(pd.genomewidesnp.6) and loading it using library(pd.genomewidesnp.6) but each time I ran eset.mas5 = mas5 (affy.data), I got the following error.

Loading required package: DBI
> affy.data = ReadAffy()
> eset.mas5 = mas5(affy.data)
background correction: mas
PM/MM correction : mas
expression values: mas
background correcting...Error in getCdfInfo(object) :
  Could not obtain CDF environment, problems encountered:
Specified environment does not contain GenomeWideSNP_6
Library - package genomewidesnp6cdf not installed
Bioconductor - genomewidesnp6cdf not available

Session Info

sessionInfo()
R version 3.3.3 (2017-03-06)
Platform: x86_64-redhat-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C
 [9] LC_ADDRESS=C               LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C

attached base packages:
[1] stats4    parallel  stats     graphics  grDevices utils     datasets
[8] methods   base

other attached packages:
 [1] pdInfoBuilder_1.38.0 affxparser_1.46.0    RSQLite_2.0
 [4] oligo_1.38.0         Biostrings_2.42.1    XVector_0.14.1
 [7] IRanges_2.8.2        S4Vectors_0.12.2     oligoClasses_1.36.0
[10] affy_1.52.0          Biobase_2.34.0       BiocGenerics_0.20.0
[13] makecdfenv_1.50.0    affyio_1.44.0        BiocInstaller_1.24.0

loaded via a namespace (and not attached):
 [1] Rcpp_0.12.11               GenomeInfoDb_1.10.3
 [3] bitops_1.0-6               iterators_1.0.8
 [5] tools_3.3.3                zlibbioc_1.20.0
 [7] digest_0.6.12              bit_1.1-12
 [9] memoise_1.1.0              preprocessCore_1.36.0
[11] tibble_1.3.3               lattice_0.20-35
[13] ff_2.2-13                  rlang_0.1.1
[15] Matrix_1.2-10              foreach_1.4.3
[17] DBI_0.7                    bit64_0.9-7
[19] grid_3.3.3                 blob_1.1.0
[21] codetools_0.2-15           GenomicRanges_1.26.4
[23] splines_3.3.3              SummarizedExperiment_1.4.0
[25] RCurl_1.95-4.8

Traceback

traceback()
3: .Call("ReadCDFFile", file.path(path.expand(cdf.path), filename),
       PACKAGE = "affyio")
2: make.cdf.env(filename, cdf.path = cdf.path, compress = compress,
       return.env.only = FALSE)
1: make.cdf.package("GenomeWideSNP_6.cdf", cdf.path = "/USER/Downloads/E-GEOD-71522.raw.1",
       compress = FALSE, species = "Homo_sapiens", package.path = pkgpath)

 

Please help me with this issue.

Thanks.

ADD COMMENTlink modified 4 months ago by James W. MacDonald45k • written 4 months ago by vinaysbharadhwaj0
0
gravatar for James W. MacDonald
4 months ago by
United States
James W. MacDonald45k wrote:

The MAS5.0 algorithm is intended for expression arrays, but you have a SNP array. If you want to generate SNP calls, you should be using crlmm (in the crlmm package).
 

ADD COMMENTlink written 4 months ago by James W. MacDonald45k

Oh, Thank you so much for the help. Could you also let me know how to use the crlmm package for annotation of SNP arrays? Is there any tutorial I can follow to annotate my CEL files. I am trying to find the expression of a certain SNP in the genomes of cancer patients.

ADD REPLYlink modified 4 months ago • written 4 months ago by vinaysbharadhwaj0

SNPs are not expressed. They are 'signposts' on the genome that you can use to help find genomic regions that appear to be associated with a given trait. In some instances they also represent coding changes to a gene that may have functional repercussions. You would do well to read about GWAS analyses so you at least know what you are attempting to do. Figuring out how to use a package is the easy part - understanding what you are doing and why is harder, but necessary to ensure you don't botch the job.

Every package (including crlmm) has at least one vignette, intended to show how to use the package. You can find the vignette(s) for any package on the landing page for that package, or by installing, loading the package, and then using the openVignette function.

ADD REPLYlink written 4 months ago by James W. MacDonald45k
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