Incorrect number of dimensions: Infinium Human Methylation BeadChip

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I am trying to run the Infinium Human Methylation BeadChip tool, and I have the following error at the end of the run:

Error in dmrs$table[, c(1, 2, 3)] : incorrect number of dimensions
Execution halted

I have checked that the number and names of the samples match the phenotype table. Can you please advise on what object may have an incorrect number of dimensions?

Thank you!

Here is the longer version of the code from the bug output:

Attaching package: ‘BiocGenerics’

The following objects are masked from ‘package:parallel’:

clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
clusterExport, clusterMap, parApply, parCapply, parLapply,
parLapplyLB, parRapply, parSapply, parSapplyLB

The following objects are masked from ‘package:stats’:

IQR, mad, sd, var, xtabs

The following objects are masked from ‘package:base’:

anyDuplicated, append, as.data.frame, basename, cbind, colnames,
dirname, do.call, duplicated, eval, evalq, Filter, Find, get, grep,
grepl, intersect, is.unsorted, lapply, Map, mapply, match, mget,
order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank,
rbind, Reduce, rownames, sapply, setdiff, sort, table, tapply,
union, unique, unsplit, which, which.max, which.min

Attaching package: ‘S4Vectors’

The following object is masked from ‘package:base’:

expand.grid

Welcome to Bioconductor

Vignettes contain introductory material; view with
'browseVignettes()'. To cite Bioconductor, see
'citation("Biobase")', and for packages 'citation("pkgname")'.

Attaching package: ‘matrixStats’

The following objects are masked from ‘package:Biobase’:

anyMissing, rowMedians

Attaching package: ‘DelayedArray’

The following objects are masked from ‘package:matrixStats’:

colMaxs, colMins, colRanges, rowMaxs, rowMins, rowRanges

The following objects are masked from ‘package:base’:

aperm, apply, rowsum

Attaching package: ‘Biostrings’

The following object is masked from ‘package:base’:

strsplit

locfit 1.5-9.1 2013-03-22
Setting options(‘download.file.method.GEOquery’=‘auto’)
Setting options(‘GEOquery.inmemory.gpl’=FALSE)

Attaching package: ‘IlluminaHumanMethylation450kanno.ilmn12.hg19’

The following objects are masked from ‘package:IlluminaHumanMethylation27kanno.ilmn12.hg19’:

Locations, Manifest, Other, SNPs.132CommonSingle,
SNPs.135CommonSingle, SNPs.137CommonSingle, SNPs.138CommonSingle,
SNPs.141CommonSingle, SNPs.142CommonSingle, SNPs.144CommonSingle,
SNPs.146CommonSingle, SNPs.147CommonSingle

Attaching package: ‘IlluminaHumanMethylationEPICanno.ilm10b4.hg19’

The following objects are masked from ‘package:IlluminaHumanMethylation450kanno.ilmn12.hg19’:

Islands.UCSC, Locations, Manifest, Other, SNPs.132CommonSingle,
SNPs.135CommonSingle, SNPs.137CommonSingle, SNPs.138CommonSingle,
SNPs.141CommonSingle, SNPs.142CommonSingle, SNPs.144CommonSingle,
SNPs.146CommonSingle, SNPs.147CommonSingle, SNPs.Illumina

The following objects are masked from ‘package:IlluminaHumanMethylation27kanno.ilmn12.hg19’:

Locations, Manifest, Other, SNPs.132CommonSingle,
SNPs.135CommonSingle, SNPs.137CommonSingle, SNPs.138CommonSingle,
SNPs.141CommonSingle, SNPs.142CommonSingle, SNPs.144CommonSingle,
SNPs.146CommonSingle, SNPs.147CommonSingle

Loading required package: IlluminaHumanMethylationEPICmanifest
[bumphunterEngine] Using a single core (backend: doSEQ, version: 1.4.8).
[bumphunterEngine] Computing coefficients.
[bumphunterEngine] Finding regions.
[bumphunterEngine] No bumps found!
Error in dmrs$table[, c(1, 2, 3)] : incorrect number of dimensions
Execution halted

1 Like
2

Hi @kiaraeldred

This is the informative part of the error message:

The first two lines are referring to a function – and it didn’t find any significant results.

The last line is a query against those results – and since there aren’t any, the query fails.

Quote from the tool form. The last sentence is describing the function that is failing.

What it does

The workflow combines 5 main steps, starting with raw intensity data loading (.idat) and then optional preprocessing and normalisation of the data. The next quality control step performs an additional sample check to remove low-quality data, which normalisation cannot detect. The workflow gives the user the opportunity to perform any of these preparation and data cleaning steps, including the highly recommended genetic variation annotation step resulting in single nucleotide polymorphism identification and removal. Finally, the dataset generated through all of these steps can be used to hunt (find) differentially-methylated positions (DMP)and regions (DMR) with respect to a phenotype covariate.

This GTN tutorial covers the usage, along with example data and methods, and should help you to find out what is going wrong. It may be a content problem with the inputs or possibly some parameter needs to be tuned to better fit your data.