[BioC] Combat: Removing multiple batch effects

Shaun Webb [guest] guest at bioconductor.org
Fri Aug 1 17:00:47 CEST 2014


Hi, I am attempting to remove two known batch effects from my Illumina BeadChip expression data. I have the following experimental design:

ID Condition Batch Parents
1 wt 1 1
2 ko 1 1
3 wt 1 2
4 ko 1 2
5 wt 2 3
6 ko 2 3

These are 3 pairs of sibling mice each with different parents. Within each pair of siblings I have a wt and ko. On top of this, samples were prepared in 2 separate batches (batch1 = first 4 samples).

After background correction and normalisation in limma my MDS plots shows that siblings group together and the first 4 samples are split from the final 2.

I would like to adjust my expression data for both of these effects to test for differential expression between wt and ko groups. ComBat seems to be a good choice however I am struggling to figure out the best approach for multiple batches. I'd appreciate if anyone could give me some advice on how to set up ComBat in this context.

Thanks in advance for your help
Shaun Webb
University of Edinburgh

 -- output of sessionInfo(): 

R version 3.1.1 (2014-07-10)
Platform: x86_64-pc-linux-gnu (64-bit)

locale:
 [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C         LC_TIME=C           
 [4] LC_COLLATE=C         LC_MONETARY=C        LC_MESSAGES=C       
 [7] LC_PAPER=C           LC_NAME=C            LC_ADDRESS=C        
[10] LC_TELEPHONE=C       LC_MEASUREMENT=C     LC_IDENTIFICATION=C 

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

other attached packages:
 [1] pamr_1.54.1                             
 [2] survival_2.37-7                         
 [3] cluster_1.15.2                          
 [4] bladderbatch_1.2.0                      
 [5] statmod_1.4.20                          
 [6] sva_3.10.0                              
 [7] mgcv_1.7-29                             
 [8] nlme_3.1-117                            
 [9] corpcor_1.6.6                           
[10] illuminaMousev2.db_1.22.1               
[11] org.Mm.eg.db_2.14.0                     
[12] DESeq2_1.4.5                            
[13] RcppArmadillo_0.4.320.0                 
[14] Rcpp_0.11.2                             
[15] DiffBind_1.10.1                         
[16] GenomicAlignments_1.0.1                 
[17] BSgenome_1.32.0                         
[18] Rsamtools_1.16.0                        
[19] Biostrings_2.32.0                       
[20] limma_3.20.4                            
[21] XVector_0.4.0                           
[22] TxDb.Hsapiens.UCSC.hg19.knownGene_2.14.0
[23] GenomicFeatures_1.16.2                  
[24] lumi_2.16.0                             
[25] methyAnalysis_1.6.0                     
[26] org.Hs.eg.db_2.14.0                     
[27] RSQLite_0.11.4                          
[28] DBI_0.2-7                               
[29] AnnotationDbi_1.26.0                    
[30] Biobase_2.24.0                          
[31] GenomicRanges_1.16.3                    
[32] GenomeInfoDb_1.0.2                      
[33] IRanges_1.22.6                          
[34] BiocGenerics_0.10.0                     

loaded via a namespace (and not attached):
 [1] AnnotationForge_1.6.1    BBmisc_1.6              
 [3] BatchJobs_1.2            BiocInstaller_1.14.2    
 [5] BiocParallel_0.6.1       Formula_1.1-1           
 [7] Gviz_1.8.3               Hmisc_3.14-4            
 [9] KernSmooth_2.23-12       MASS_7.3-33             
[11] Matrix_1.1-4             R.methodsS3_1.6.1       
[13] RColorBrewer_1.0-5       RCurl_1.95-4.1          
[15] VariantAnnotation_1.10.1 XML_3.98-1.1            
[17] affy_1.42.2              affyio_1.32.0           
[19] amap_0.8-12              annotate_1.42.0         
[21] base64_1.1               beanplot_1.1            
[23] biomaRt_2.20.0           biovizBase_1.12.1       
[25] bitops_1.0-6             brew_1.0-6              
[27] bumphunter_1.4.2         caTools_1.17            
[29] codetools_0.2-8          colorspace_1.2-4        
[31] dichromat_2.0-0          digest_0.6.4            
[33] doRNG_1.6                edgeR_3.6.2             
[35] fail_1.2                 foreach_1.4.2           
[37] gdata_2.13.3             genefilter_1.46.1       
[39] geneplotter_1.42.0       genoset_1.16.2          
[41] gplots_2.13.0            gtools_3.4.1            
[43] illuminaio_0.6.0         iterators_1.0.7         
[45] lattice_0.20-29          latticeExtra_0.6-26     
[47] locfit_1.5-9.1           matrixStats_0.10.0      
[49] mclust_4.3               methylumi_2.10.0        
[51] minfi_1.10.2             multtest_2.20.0         
[53] munsell_0.4.2            nleqslv_2.2             
[55] nor1mix_1.1-4            pkgmaker_0.22           
[57] plyr_1.8.1               preprocessCore_1.26.1   
[59] registry_0.2             reshape_0.8.5           
[61] rngtools_1.2.4           rtracklayer_1.24.1      
[63] scales_0.2.4             sendmailR_1.1-2         
[65] siggenes_1.38.0          stats4_3.1.1            
[67] stringr_0.6.2            tools_3.1.1             
[69] xtable_1.7-3             zlibbioc_1.10.0   

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