Hi, everyone. I have a question about the function "ComBat" in the package "sva". I am using the ComBat to remove the batch effect in methylation data. For the test data, the sample information is: sample covariate batch 101 1 A 102 1 A 103 1 B 104 1 B 201 2 A 202 2 A 203 2 C 204 2 C There are 3 batches and 1 covariate with two elements. It will works smoothly. However, if I change the data like this: sample covariate batch 101 1 A 102 1 A 103 1 B 104 1 B 201 2 D 202 2 D 203 2 C 204 2 C Then it comes out a error message: ComBat failed??? the batch effect is confounded with the covariate. I searched the google group about this question, the answer given is: the difference between the batch B and batch D may come from the covariate 1 and covariate 2. So that is why the effect is confounded. I think it may because of the algorithm, but all my real data is like that. Each batch only belongs to one element of the covariate. like this: sample covariate batch 101 1 A 102 1 A 103 1 B 104 1 B 105 1 C 106 1 C 201 2 D 202 2 D 203 2 E 204 2 E 205 2 F 206 2 F So, is there anybody come up with some ideas? (Especially Dr. Evan Johnson). Thank you very much Jie Yang Graduate student UTHealth at Houston School of Public Health -- output of sessionInfo(): R version 3.0.3 (2014-03-06) Platform: x86_64-apple-darwin10.8.0 (64-bit) locale: [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8 attached base packages: [1] parallel stats graphics grDevices utils datasets methods base other attached packages: [1] IlluminaHumanMethylation450kmanifest_0.4.0 ChAMP_1.2.7 [3] Illumina450ProbeVariants.db_0.99.3 ChAMPdata_0.99.6 [5] minfi_1.8.9 bumphunter_1.2.0 [7] locfit_1.5-9.1 iterators_1.0.7 [9] foreach_1.4.2 Biostrings_2.30.1 [11] GenomicRanges_1.14.4 XVector_0.2.0 [13] IRanges_1.20.7 reshape_0.8.5 [15] lattice_0.20-29 Biobase_2.22.0 [17] BiocGenerics_0.8.0 loaded via a namespace (and not attached): [1] annotate_1.40.1 AnnotationDbi_1.24.0 base64_1.1 beanplot_1.1 cluster_1.15.2 [6] codetools_0.2-8 corpcor_1.6.6 DBI_0.2-7 digest_0.6.4 DNAcopy_1.36.0 [11] doRNG_1.6 genefilter_1.44.0 grid_3.0.3 illuminaio_0.4.0 impute_1.36.0 [16] itertools_0.1-3 limma_3.18.13 marray_1.40.0 MASS_7.3-33 matrixStats_0.10.0 [21] mclust_4.3 multtest_2.18.0 nlme_3.1-117 nor1mix_1.1-4 pkgmaker_0.22 [26] plyr_1.8.1 preprocessCore_1.24.0 R.methodsS3_1.6.1 RColorBrewer_1.0-5 Rcpp_0.11.2 [31] registry_0.2 rngtools_1.2.4 RPMM_1.10 RSQLite_0.11.4 siggenes_1.36.0 [36] splines_3.0.3 stats4_3.0.3 stringr_0.6.2 survival_2.37-7 sva_3.8.0 [41] tools_3.0.3 wateRmelon_1.2.2 XML_3.95-0.2 xtable_1.7-3 -- Sent via the guest posting facility at bioconductor.org.