Hello,

I've previously used fRMA and the MBNI BrainArray CDFs with some success, but the last time I tried was a year ago. I just updated to R 3.0.2 and the newest version of BioC. I'm trying to use fRMA with some U133+ 2.0 arrays, and getting errors with custom CDFs. It works fine with the stock CDFs. I've tried both robust_weighted_average and random_effect for summarization. I've also tried BrainArray CDF versions from 15 to 18 (all EntrezGene). The error is different between using random_effect and robust_weighted_average.
With robust_weighted_average I get "Error in rcModelWPLM(y = x1, w = w.tmp, row.effects = pe.tmp, input.scale = x4) :
  row.effects should sum to zero".

Aside from the convenience of summarize straight to gene-level, would you actually recommend using the BrainArray CDFs with fRMA? It seems like the combination of probe weighting based on data, and probe selection based on re-annotation would produce some nice results- if using the custom CDFs doesn't break any assumptions made in fRMA.

Thanks for the help again.

R version 3.0.2 (2013-09-25)
Platform: x86_64-w64-mingw32/x64 (64-bit)

locale:
[1] LC_COLLATE=English_United States.1252  LC_CTYPE=English_United States.1252    LC_MONETARY=English_United States.1252 LC_NUMERIC=C
[5] LC_TIME=English_United States.1252

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

other attached packages:
[1] hgu133plus2cdf_2.13.0          hgu133plus2frmavecs_1.3.0      hgu133plus2hsentrezgcdf_16.0.0 heatmap.plus_1.3               frma_1.14.0
 [6] GGally_0.4.5                   reshape_0.8.4                  plyr_1.8                       RColorBrewer_1.0-5             ggplot2_0.9.3.1
[11] xlsx_0.5.5                     xlsxjars_0.5.0                 rJava_0.9-6                    annotate_1.40.0                org.Hs.eg.db_2.10.1
[16] puma_3.4.0                     mclust_4.2                     VennDiagram_1.6.5              scatterplot3d_0.3-35           annaffy_1.34.0
[21] KEGG.db_2.10.1                 GO.db_2.10.1                   RSQLite_0.11.4                 DBI_0.2-7                      AnnotationDbi_1.24.0
[26] gplots_2.12.1                  MASS_7.3-29                    affyPLM_1.38.0                 preprocessCore_1.24.0          simpleaffy_2.38.0
[31] gcrma_2.34.0                   genefilter_1.44.0              marray_1.40.0                  limma_3.18.12                  BiocInstaller_1.12.0
[36] affy_1.40.0                    Biobase_2.22.0                 BiocGenerics_0.8.0

loaded via a namespace (and not attached):
[1] affxparser_1.34.0    affyio_1.30.0        Biostrings_2.30.1    bit_1.1-11           bitops_1.0-6         caTools_1.16         codetools_0.2-8
 [8] colorspace_1.2-4     dichromat_2.0-0      digest_0.6.4         ff_2.2-12            foreach_1.4.1        gdata_2.13.2         GenomicRanges_1.14.4
[15] gtable_0.1.2         gtools_3.3.0         IRanges_1.20.6       iterators_1.0.6      KernSmooth_2.23-10   labeling_0.2         munsell_0.4.2
[22] oligo_1.26.2         oligoClasses_1.24.0  proto_0.3-10         reshape2_1.2.2       scales_0.2.3         splines_3.0.2        stats4_3.0.2
[29] stringr_0.6.2        survival_2.37-7      tools_3.0.2          XML_3.98-1.1         xtable_1.7-1         XVector_0.2.0        zlibbioc_1.8.0


Adam Cornwell
Programmer/Analyst


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