dmrseq: cannot adjust for a covariate
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Entering edit mode
l.kremer • 0
@lkremer-14928
Last seen 6.8 years ago

Hello everyone,

I'm trying to use the tool dmrseq to find differentially methylated regions (DMRs) from bisulfite-seq data. My experimental design looks like this:

##             celltype treatment
##             <factor>  <factor>
## neuron_c1     neuron   control
## neuron_c2     neuron   control
## neuron_t1     neuron   treated
## neuron_t2     neuron   treated
## stemcell_c1 stemcell   control
## stemcell_c2 stemcell   control
## stemcell_t1 stemcell   treated
## stemcell_t2 stemcell   treated

Now I want to find e.g. DMRs between stem cells and neurons while adjusting for the effect of treatment. According to the help site of "dmrseq::dmrseq", this should be possible by specifying the "adjustCovariate" argument.

However, specifying "adjustCovariate" always results in an error (that occurs very quickly before any calculations are done). See below for a minimal working (crashing) example that's reproducible on multiple machines.

Of course I also tried the code below using a proper dataset, but to no avail. I also tried specifying pData as character instead of factor, but it doesn't seem to make a difference.

Thank you in advance, my experience with dmrseq has been great so far!

Detecting differentially methylated regions with dmrseq

require(bsseq)
require(BiocGenerics)
library(dmrseq)

Constructing a toy dataset from the bsseq example file.

infile <- system.file("extdata/test_data.fastq_bismark.bismark.cov.gz",
                       package = 'bsseq')

cpg <- BiocGenerics::combine(
  bsseq::read.bismark(files = infile, sampleNames = "neuron_c1", strandCollapse = F),
  bsseq::read.bismark(files = infile, sampleNames = "neuron_c2", strandCollapse = F),
  bsseq::read.bismark(files = infile, sampleNames = "neuron_t1", strandCollapse = F),
  bsseq::read.bismark(files = infile, sampleNames = "neuron_t2", strandCollapse = F),
  bsseq::read.bismark(files = infile, sampleNames = "stemcell_c1", strandCollapse = F),
  bsseq::read.bismark(files = infile, sampleNames = "stemcell_c2", strandCollapse = F),
  bsseq::read.bismark(files = infile, sampleNames = "stemcell_t1", strandCollapse = F),
  bsseq::read.bismark(files = infile, sampleNames = "stemcell_t2", strandCollapse = F)
)
## [read.bismark] Reading file '/home/lukas/R/x86_64-pc-linux-gnu-library/3.4/bsseq/extdata/test_data.fastq_bismark.bismark.cov.gz' ... done in 0.8 secs
## [read.bismark] Joining samples ... done in 0.2 secs
## [read.bismark] Reading file '/home/lukas/R/x86_64-pc-linux-gnu-library/3.4/bsseq/extdata/test_data.fastq_bismark.bismark.cov.gz' ... done in 0.5 secs
## [read.bismark] Joining samples ... done in 0.1 secs
## [read.bismark] Reading file '/home/lukas/R/x86_64-pc-linux-gnu-library/3.4/bsseq/extdata/test_data.fastq_bismark.bismark.cov.gz' ... done in 0.1 secs
## [read.bismark] Joining samples ... done in 0.1 secs
## [read.bismark] Reading file '/home/lukas/R/x86_64-pc-linux-gnu-library/3.4/bsseq/extdata/test_data.fastq_bismark.bismark.cov.gz' ... done in 0.1 secs
## [read.bismark] Joining samples ... done in 0.1 secs
## [read.bismark] Reading file '/home/lukas/R/x86_64-pc-linux-gnu-library/3.4/bsseq/extdata/test_data.fastq_bismark.bismark.cov.gz' ... done in 0.1 secs
## [read.bismark] Joining samples ... done in 0.1 secs
## [read.bismark] Reading file '/home/lukas/R/x86_64-pc-linux-gnu-library/3.4/bsseq/extdata/test_data.fastq_bismark.bismark.cov.gz' ... done in 0.1 secs
## [read.bismark] Joining samples ... done in 0.1 secs
## [read.bismark] Reading file '/home/lukas/R/x86_64-pc-linux-gnu-library/3.4/bsseq/extdata/test_data.fastq_bismark.bismark.cov.gz' ... done in 0.1 secs
## [read.bismark] Joining samples ... done in 0.1 secs
## [read.bismark] Reading file '/home/lukas/R/x86_64-pc-linux-gnu-library/3.4/bsseq/extdata/test_data.fastq_bismark.bismark.cov.gz' ... done in 0.1 secs
## [read.bismark] Joining samples ... done in 0.1 secs
cpg
## An object of type 'BSseq' with
##   2013 methylation loci
##   8 samples
## has not been smoothed
## All assays are in-memory

Labeling the toy samples according to treatment & cell type:

pData(cpg)$celltype  <- as.factor(c("neuron", "neuron", "neuron", "neuron",
                                    "stemcell", "stemcell", "stemcell", "stemcell"))
pData(cpg)$treatment <- as.factor(c("control", "control", "treated", "treated",
                                    "control", "control", "treated", "treated"))

Double-checking that this table makes sense:

pData(cpg)
## DataFrame with 8 rows and 2 columns
##             celltype treatment
##             <factor>  <factor>
## neuron_c1     neuron   control
## neuron_c2     neuron   control
## neuron_t1     neuron   treated
## neuron_t2     neuron   treated
## stemcell_c1 stemcell   control
## stemcell_c2 stemcell   control
## stemcell_t1 stemcell   treated
## stemcell_t2 stemcell   treated

Only keep loci that have at least 1 coverage in all samples

cpg <- filterLoci(cpg)
## Filtering out loci with coverage less than 1 read in at least one sample
## Removed 0 out of 2013 loci

Trying to detect differentially methylated regions (DMRs) with dmrseq while adjusting for "treatment":

regions <- dmrseq(bs = cpg,
                  testCovariate = "celltype",
                  adjustCovariate = "treatment")
## Error in colnames(design)[, (max(coeff) + 1):ncol(design)] <- colnames(pData(bs))[adjustCovariate]: incorrect number of subscripts on matrix

Traceback is not very helpful.

traceback()
## 1: dmrseq(bs = cpg, testCovariate = "celltype", adjustCovariate = "treatment")

That didn't work, let's try again, this time I'm specifying the covariate by column number:

regions <- dmrseq(bs = cpg,
                  testCovariate = "celltype",
                  adjustCovariate = 2)
## Error in colnames(design)[, (max(coeff) + 1):ncol(design)] <- colnames(pData(bs))[adjustCovariate]: incorrect number of subscripts on matrix
traceback()
## 1: dmrseq(bs = cpg, testCovariate = "celltype", adjustCovariate = 2)

Still didn't work. But it works if I don't specify a covariate to adjust for:

regions <- dmrseq(bs = cpg,
                  testCovariate = "celltype")
## Condition stemcell vs neuron
## Using a single core
## Detecting candidate regions with coefficient larger than 0.1 in magnitude.
## ... (rest of the results omitted) ...

(Okay, it doesn't actually work here because I'm using a toy dataset, but it works on my own data)

BiocInstaller::biocValid()
## [1] TRUE
sessionInfo()
## R version 3.4.3 (2017-11-30)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 16.04.3 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_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_GB.UTF-8        LC_COLLATE=en_US.UTF-8    
##  [5] LC_MONETARY=en_GB.UTF-8    LC_MESSAGES=en_US.UTF-8   
##  [7] LC_PAPER=en_GB.UTF-8       LC_NAME=C                 
##  [9] 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 
## [8] methods   base     
## 
## other attached packages:
##  [1] dmrseq_0.99.1              bsseq_1.14.0              
##  [3] SummarizedExperiment_1.8.1 DelayedArray_0.4.1        
##  [5] matrixStats_0.53.0         Biobase_2.38.0            
##  [7] GenomicRanges_1.30.1       GenomeInfoDb_1.14.0       
##  [9] IRanges_2.12.0             S4Vectors_0.16.0          
## [11] BiocGenerics_0.24.0        knitr_1.19                
## 
## loaded via a namespace (and not attached):
##   [1] nlme_3.1-131                             
##   [2] bitops_1.0-6                             
##   [3] TxDb.Rnorvegicus.UCSC.rn6.refGene_3.4.1  
##   [4] bit64_0.9-7                              
##   [5] RColorBrewer_1.1-2                       
##   [6] progress_1.1.2                           
##   [7] httr_1.3.1                               
##   [8] TxDb.Hsapiens.UCSC.hg18.knownGene_3.2.2  
##   [9] doRNG_1.6.6                              
##  [10] tools_3.4.3                              
##  [11] R6_2.2.2                                 
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##  [13] lazyeval_0.2.1                           
##  [14] colorspace_1.3-2                         
##  [15] permute_0.9-4                            
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##  [17] RMySQL_0.10.13                           
##  [18] bit_1.1-12                               
##  [19] compiler_3.4.3                           
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##  [21] TxDb.Mmusculus.UCSC.mm10.knownGene_3.4.0 
##  [22] rtracklayer_1.38.3                       
##  [23] scales_0.5.0                             
##  [24] readr_1.1.1                              
##  [25] stringr_1.2.0                            
##  [26] digest_0.6.15                            
##  [27] Rsamtools_1.30.0                         
##  [28] R.utils_2.6.0                            
##  [29] XVector_0.18.0                           
##  [30] pkgconfig_2.0.1                          
##  [31] htmltools_0.3.6                          
##  [32] BSgenome_1.46.0                          
##  [33] regioneR_1.10.0                          
##  [34] limma_3.34.6                             
##  [35] TxDb.Dmelanogaster.UCSC.dm6.ensGene_3.4.1
##  [36] rlang_0.1.6                              
##  [37] RSQLite_2.0                              
##  [38] BiocInstaller_1.28.0                     
##  [39] shiny_1.0.5                              
##  [40] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2  
##  [41] bindr_0.1                                
##  [42] BiocParallel_1.12.0                      
##  [43] gtools_3.5.0                             
##  [44] dplyr_0.7.4                              
##  [45] R.oo_1.21.0                              
##  [46] RCurl_1.95-4.10                          
##  [47] magrittr_1.5                             
##  [48] GenomeInfoDbData_1.0.0                   
##  [49] Matrix_1.2-11                            
##  [50] Rcpp_0.12.15                             
##  [51] munsell_0.4.3                            
##  [52] R.methodsS3_1.7.1                        
##  [53] stringi_1.1.6                            
##  [54] yaml_2.1.16                              
##  [55] zlibbioc_1.24.0                          
##  [56] bumphunter_1.20.0                        
##  [57] org.Hs.eg.db_3.5.0                       
##  [58] plyr_1.8.4                               
##  [59] AnnotationHub_2.10.1                     
##  [60] grid_3.4.3                               
##  [61] blob_1.1.0                               
##  [62] lattice_0.20-35                          
##  [63] Biostrings_2.46.0                        
##  [64] TxDb.Rnorvegicus.UCSC.rn5.refGene_3.4.2  
##  [65] GenomicFeatures_1.30.2                   
##  [66] hms_0.4.1                                
##  [67] locfit_1.5-9.1                           
##  [68] pillar_1.1.0                             
##  [69] org.Dm.eg.db_3.5.0                       
##  [70] rngtools_1.2.4                           
##  [71] codetools_0.2-15                         
##  [72] reshape2_1.4.3                           
##  [73] biomaRt_2.34.2                           
##  [74] XML_3.98-1.9                             
##  [75] glue_1.2.0                               
##  [76] evaluate_0.10.1                          
##  [77] outliers_0.14                            
##  [78] annotatr_1.4.1                           
##  [79] data.table_1.10.4-3                      
##  [80] foreach_1.4.4                            
##  [81] httpuv_1.3.5                             
##  [82] org.Mm.eg.db_3.5.0                       
##  [83] gtable_0.2.0                             
##  [84] assertthat_0.2.0                         
##  [85] org.Rn.eg.db_3.5.0                       
##  [86] ggplot2_2.2.1                            
##  [87] TxDb.Mmusculus.UCSC.mm9.knownGene_3.2.2  
##  [88] mime_0.5                                 
##  [89] TxDb.Rnorvegicus.UCSC.rn4.ensGene_3.2.2  
##  [90] xtable_1.8-2                             
##  [91] tibble_1.4.2                             
##  [92] TxDb.Hsapiens.UCSC.hg38.knownGene_3.4.0  
##  [93] iterators_1.0.9                          
##  [94] registry_0.5                             
##  [95] GenomicAlignments_1.14.1                 
##  [96] AnnotationDbi_1.40.0                     
##  [97] memoise_1.1.0                            
##  [98] bindrcpp_0.2                             
##  [99] interactiveDisplayBase_1.16.0            
## [100] TxDb.Dmelanogaster.UCSC.dm3.ensGene_3.2.2
dmrseq dmr dmrs bisulfite methylation • 1.7k views
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2
Entering edit mode
keegan ▴ 60
@keegan-11987
Last seen 3 months ago
Vancouver, BC, Canada

Hi l.kremer,

Sorry for the delay in responding to this inquiry. I hadn't yet been checking the support site for questions since dmrseq was just recently accepted into Bioconductor.

Thanks for your thorough and reproducible bug report. I was able to track down the root cause and provide a patch. Now providing an adjustCovariate should work smoothly. You can get the latest version on github (https://github.com/kdkorthauer/dmrseq), or it will be in Bioc-devel shortly (within a matter of days).

Best,
Keegan

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0
Entering edit mode

Hi Keegan,

I updated to the latest version and now it works flawlessly. Thank you very much for the quick fix!

Lukas

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