fRMA results different for Windows and Linux
Entering edit mode
osieman52 • 0
Last seen 24 days ago


When I apply fRMA on a dataset using a windows machine and a linux machine I find diffrences when I compare the expressions, Is this expected ,because the diffrences are not small. I am using more or less the same R versions and librarys. 


R version 3.4.4 (2018-03-15)

Platform: x86_64-w64-mingw32/x64 (64-bit)

Running under: Windows >= 8 x64 (build 9200)

Matrix products: default


[1] LC_COLLATE=Dutch_Netherlands.1252  LC_CTYPE=Dutch_Netherlands.1252    LC_MONETARY=Dutch_Netherlands.1252 LC_NUMERIC=C                      

[5] LC_TIME=Dutch_Netherlands.1252    

attached base packages:

[1] parallel  stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:

[1] hgu133plus2cdf_2.18.0     hgu133plus2frmavecs_1.5.0 affy_1.56.0               frma_1.30.1               Biobase_2.38.0            BiocGenerics_0.24.0      

loaded via a namespace (and not attached):

 [1] Rcpp_0.12.15               BiocInstaller_1.28.0       compiler_3.4.4             pillar_1.2.1               GenomeInfoDb_1.14.0       

 [6] XVector_0.18.0             bitops_1.0-6               iterators_1.0.9            tools_3.4.4                zlibbioc_1.24.0           

[11] digest_0.6.15              bit_1.1-12                 memoise_1.1.0              RSQLite_2.0                preprocessCore_1.40.0     

[16] tibble_1.4.2               lattice_0.20-35            ff_2.2-13                  rlang_0.2.0                Matrix_1.2-12             

[21] foreach_1.4.4              DelayedArray_0.4.1         DBI_0.7                    yaml_2.1.17                GenomeInfoDbData_1.0.0    

[26] affxparser_1.50.0          Biostrings_2.46.0          S4Vectors_0.16.0           IRanges_2.12.0             stats4_3.4.4              

[31] bit64_0.9-7                grid_3.4.4                 AnnotationDbi_1.40.0       oligo_1.42.0               blob_1.1.0                

[36] codetools_0.2-15           matrixStats_0.53.1         oligoClasses_1.40.0        MASS_7.3-49                GenomicRanges_1.30.0      

[41] splines_3.4.4              SummarizedExperiment_1.8.1 RCurl_1.95-4.10            affyio_1.48.0             



R version 3.3.2 (2016-10-31)

Platform: x86_64-pc-linux-gnu (64-bit)

Running under: Scientific Linux 7.4 (Nitrogen)

Matrix products: 


[1] C

attached base packages:

[1] parallel  methods   tools     stats     graphics  grDevices utils     datasets  base     

other attached packages:

[1] hgu133plus2cdf_2.18.0     hgu133plus2frmavecs_1.5.0 frma_1.30.1               affy_1.52.0               Biobase_2.34.0            BiocGenerics_0.20.0      

[7] yaml_2.1.14              

loaded via a namespace (and not attached):

 [1] Rcpp_0.12.13               AnnotationDbi_1.36.0       affxparser_1.46.0          XVector_0.14.0             splines_3.3.2             

 [6] GenomicRanges_1.26.1       zlibbioc_1.20.0            MASS_7.3-45                IRanges_2.8.1              bit_1.1-12                

[11] lattice_0.20-34            foreach_1.4.3              GenomeInfoDb_1.10.1        SummarizedExperiment_1.4.0 grid_3.3.2                

[16] ff_2.2-13                  DBI_0.7-12                 iterators_1.0.8            oligoClasses_1.36.0        digest_0.6.11             

[21] preprocessCore_1.36.0      oligo_1.38.0               affyio_1.44.0              Matrix_1.2-7.1             codetools_0.2-15          

[26] S4Vectors_0.12.1           memoise_1.0.0              RSQLite_1.1-1              BiocInstaller_1.24.0       Biostrings_2.42.1         

[31] stats4_3.3.2


frma expr gene expression data affy • 874 views
Entering edit mode
Last seen 1 hour ago
United States

You should not have the same version of any Bioconductor package for those two R versions, so you must have done something other than using biocLite to install. We don't support outdated R/Bioconductor software (both of your installs are outdated), nor do we support installs that aren't valid (e.g., running biocValid gives the all clear sign).

Please update to the current version of R (R-3.5.0) and Bioconductor on both machines and let us know if the issue persists.

Entering edit mode

Unfortunately I don't have access to change the linux machine, but I did update the windows machine to the current version but I receive an error when I'm trying to use the frma library (that was the reason I went back to the older version on my windows machine) :

`> library(frma)`
Error: package or namespace load failed for ‘frma’ in loadNamespace(i, c(lib.loc, .libPaths()), versionCheck = vI[[i]]):
 there is no package called ‘BiocParallel’

So using the newest version doesn't really solve the problem. BUt maybe you can help me with this problem


Entering edit mode

There isn't a Windows binary for BiocParallel, so you need to get set up to compile. This used to be a daunting task, but now it's a simple download and install of the Rtool set. You obviously want the one for R-3.5.x, and you need to install as administrator and I usually click the box to add rtools to your path.

Once you have done that you should be able to restart R and then do


at which time it will say it's a source package and do you want to compile, to which you say heck yeah.

Entering edit mode

I should also note that unless your sysadmin is cheaping you out on personal space on the Linux box, you can always just compile R in your home dir and add that to your path. Or if you have write access to a larger dir, you could do it there.

Entering edit mode

Thanks alot, this seems to work!

The reason why we can't just update to the newest R version is that alot of experiments are based on the older version and we find different results for different platforms(Linux vs Windows) as well as differences with old and new librarys(old fRMA vs new fRMA same samples) running the same machine. We can't redo our experiments when we find biases like these.


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