conumee error: query intensities not given for all probes.
2
1
Entering edit mode
@yuragrabovska-9835
Last seen 3.1 years ago
Conumee error:

Error in .local(query, ref, anno, ...) : 
  query intensities not given for all probes.

 

This error results when I use 

x <- CNV.fit(data[1], controls.data, anno)

The data object was generated by:

rgSet <- combineArrays(rgSet.450k, rgSet.850k)
mSet <- preprocessNoob(rgSet, dyeMethod = "single")
data <- CNV.load(mSet)

 

The anno object was made by: 

anno <- CNV.create_anno(chrXY = TRUE, 
                        exclude_regions = exclude_regions, 
                        detail_regions = detail_regions,
                        array_type = "overlap")

Controls data is generated in a similar way to the test data.

Could you please let me what checks to carry out? I current work around this by amending the anno data to reflect the missing problems and end up dropping quite a number of probes in the process. 

Many thanks

R version 3.4.0 (2017-04-21)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 16.04.2 LTS

Matrix products: default
BLAS: /usr/lib/libblas/libblas.so.3.6.0
LAPACK: /usr/lib/lapack/liblapack.so.3.6.0

locale:
 [1] LC_CTYPE=en_GB.UTF-8       LC_NUMERIC=C               LC_TIME=en_GB.UTF-8        LC_COLLATE=en_GB.UTF-8     LC_MONETARY=en_GB.UTF-8   
 [6] LC_MESSAGES=en_GB.UTF-8    LC_PAPER=en_GB.UTF-8       LC_NAME=C                  LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_GB.UTF-8 LC_IDENTIFICATION=C       

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

other attached packages:
 [1] conumee_1.10.0                                      IlluminaHumanMethylationEPICmanifest_0.3.0          IlluminaHumanMethylationEPICanno.ilm10b2.hg19_0.6.0
 [4] IlluminaHumanMethylation450kmanifest_0.4.0          IlluminaHumanMethylation450kanno.ilmn12.hg19_0.6.0  minfi_1.22.1                                       
 [7] bumphunter_1.16.0                                   locfit_1.5-9.1                                      iterators_1.0.8                                    
[10] foreach_1.4.3                                       Biostrings_2.44.1                                   XVector_0.16.0                                     
[13] SummarizedExperiment_1.6.3                          DelayedArray_0.2.7                                  matrixStats_0.52.2                                 
[16] Biobase_2.36.2                                      GenomicRanges_1.28.3                                GenomeInfoDb_1.12.2                                
[19] IRanges_2.10.2                                      S4Vectors_0.14.3                                    BiocGenerics_0.22.0                                

loaded via a namespace (and not attached):
 [1] httr_1.2.1               nor1mix_1.2-2            bit64_0.9-7              splines_3.4.0            doRNG_1.6.6              blob_1.1.0              
 [7] GenomeInfoDbData_0.99.0  Rsamtools_1.28.0         RSQLite_2.0              lattice_0.20-35          limma_3.32.2             quadprog_1.5-5          
[13] digest_0.6.12            RColorBrewer_1.1-2       preprocessCore_1.38.1    Matrix_1.2-10            plyr_1.8.4               GEOquery_2.42.0         
[19] siggenes_1.50.0          XML_3.98-1.9             biomaRt_2.32.1           genefilter_1.58.1        zlibbioc_1.22.0          xtable_1.8-2            
[25] BiocParallel_1.10.1      tibble_1.3.3             openssl_0.9.6            annotate_1.54.0          beanplot_1.2             pkgmaker_0.22           
[31] GenomicFeatures_1.28.4   survival_2.41-3          magrittr_1.5             mclust_5.3               memoise_1.1.0            nlme_3.1-131            
[37] MASS_7.3-47              tools_3.4.0              registry_0.3             data.table_1.10.4        stringr_1.2.0            rngtools_1.2.4          
[43] AnnotationDbi_1.38.1     base64_2.0               compiler_3.4.0           rlang_0.1.1              grid_3.4.0               RCurl_1.95-4.8          
[49] bitops_1.0-6             DNAcopy_1.50.1           codetools_0.2-15         multtest_2.32.0          DBI_0.7                  reshape_0.8.6           
[55] R6_2.2.2                 illuminaio_0.18.0        GenomicAlignments_1.12.1 rtracklayer_1.36.3       bit_1.1-12               stringi_1.1.5           
[61] Rcpp_0.12.11            
 
 

 

 

conumee • 1.8k views
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4
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@volker-hovestadt-7393
Last seen 9 days ago
boston

there are some probes missing on more recent EPIC arrays which causes this error.

for now you can try installing the more recent "IlluminaHumanMethylationEPICanno.ilm10b3.hg19" package and adapt the "CNV.create_anno" function (line 96: https://github.com/Bioconductor-mirror/conumee/blob/release-3.5/R/annotation.R).

i will fix this in the next release. thank you for reporting this bug.

 

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0
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Thank you for getting back to me. We assumed there was something going on along these lines as I recently had a similar problem with minfi reading in newer EPIC array idats before the update dropped. 

Will implement change you recommended.

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5
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@splittinginfinity-11669
Last seen 3.3 years ago

I had the same problem as well. In case this may be interesting to others, the temporary measure that I'm doing is finding the overlaps between sample and anno file right after CNV.load:

data <- CNV.load(Mset)

controls.data <- CNV.load(MsetControls)

### find overlaps ###

Mset <- mapToGenome(Mset)

anno@probes <- subsetByOverlaps(anno@probes, granges(Mset))
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0
Entering edit mode

Unfortunately I experience the same problem when combining 450K and EPIC arrays. Could you please elaborate on your solution, I'm not sure I understand what you are doing in this part?

### find overlaps ###

Mset <- mapToGenome(Mset)

anno@probes <- subsetByOverlaps(anno@probes, granges(Mset))

Many thanks

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0
Entering edit mode

Hey, were you able to fix this issue? :)

Shweta

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0
Entering edit mode

Yes, thanks :) I actually don't remember exactly what I've done, but I found a workaround!

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0
Entering edit mode

glad you got it to work. this would be my solution:

when combining 450k and EPIC datasets, make sure to use CNV.create_anno() with array_type="overlap"

then use this line (similar to the above):

anno@probes <- anno@probes[names(anno@probes) %in% names(minfi::getLocations(IlluminaHumanMethylationEPICanno.ilm10b4.hg19::IlluminaHumanMethylationEPICanno.ilm10b4.hg19))]

as always, the larger and more diverse (450k/EPIC, good/bad quality ..) the reference set, the better the results.

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